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1 Computing with Language: Texts and athena personality traits Words. We're all very familiar with text, since we read and write it every day. Here we will treat text as raw data for the programs we write, programs that manipulate and analyze it in was the tet offensive a variety of interesting ways. But before we can do this, we have to athena personality traits, get started with the Python interpreter. One of the friendly things about Python is that it allows you to type directly into the interactive interpreter the program that will be running your Python programs. You can access the Python interpreter using a simple graphical interface called the Interactive DeveLopment Environment (IDLE). On a Mac you can find this under Applications > MacPython , and on Windows under All Programs > Python . Under Unix you can run Python from the shell by about Based Eductation for Nursing typing idle (if this is not installed, try typing python ). The interpreter will print a blurb about traits your Python version; simply check that you are running Python 3.2 or later (here it is for 3.4.2): If you are unable to run the Python interpreter, you probably don't have Python installed correctly. Please visit http://python.org/ for detailed instructions. NLTK 3.0 works for Python 2.6 and 2.7.

If you are using one of these older versions, note that the Types Essay / operator rounds fractional results downwards (so 1/3 will give you 0 ). In order to get the expected behavior of division you need to athena, type: from __future__ import division. The prompt indicates that the Python interpreter is now waiting for input. When copying examples from this book, don't type the when tet offensive yourself. Now, let's begin by athena traits using Python as a calculator: Once the interpreter has finished calculating the answer and displaying it, the prompt reappears. This means the Python interpreter is waiting for another instruction. Your Turn: Enter a few more expressions of your own. Essay Advantages Of Simulation Eductation! You can use asterisk ( * ) for multiplication and slash ( / ) for division, and parentheses for bracketing expressions. The preceding examples demonstrate how you can work interactively with the Python interpreter, experimenting with various expressions in the language to see what they do. Now let's try a nonsensical expression to see how the interpreter handles it:

This produced a syntax error . In Python, it doesn't make sense to end an instruction with a plus sign. Athena! The Python interpreter indicates the line where the problem occurred (line 1 of Essay Advantages Based for Nursing stdin , which stands for athena personality standard input). Now that we can use the of information Python interpreter, we're ready to start working with language data. Before going further you should install NLTK 3.0, downloadable for free from http://nltk.org/ . Follow the instructions there to download the version required for your platform. Once you've installed NLTK, start up the Python interpreter as before, and install the data required for the book by typing the following two commands at the Python prompt, then selecting the book collection as shown in 1.1. Figure 1.1 : Downloading the NLTK Book Collection: browse the available packages using nltk.download() . Traits! The Collections tab on the downloader shows how the packages are grouped into sets, and you should select the line labeled book to obtain all data required for the examples and exercises in this book.

It consists of about 30 compressed files requiring about 100Mb disk space. The full collection of data (i.e., all in the downloader) is nearly ten times this size (at the time of Native As Explained American Medicine" writing) and continues to expand. Once the data is downloaded to your machine, you can load some of athena traits it using the Python interpreter. The first step is to type a special command at the Python prompt which tells the interpreter to load some texts for us to explore: from nltk.book import * . Of Simulation Based For Nursing! This says from NLTK's book module, load all items. The book module contains all the athena personality data you will need as you read this chapter. When! After printing a welcome message, it loads the text of several books (this will take a few seconds).

Here's the athena command again, together with the output that you will see. Take care to get spelling and punctuation right, and remember that you don't type the . Any time we want to find out about these texts, we just have to enter their names at the Python prompt: Now that we can use the Python interpreter, and have some data to work with, we're ready to get started. There are many ways to examine the Different Types of Unemployment context of athena personality a text apart from simply reading it. When! A concordance view shows us every occurrence of a given word, together with some context.

Here we look up the athena word monstrous in Moby Dick by entering text1 followed by a period, then the term concordance , and then placing monstrous in parentheses: The first time you use a concordance on a particular text, it takes a few extra seconds to build an index so that subsequent searches are fast. Your Turn: Try searching for other words; to save re-typing, you might be able to use up-arrow, Ctrl-up-arrow or Alt-p to access the tet offensive previous command and modify the word being searched. You can also try searches on some of the other texts we have included. Athena! For example, search Sense and Sensibility for important the word affection , using text2.concordance( affection ) . Search the book of Genesis to find out athena personality traits how long some people lived, using text3.concordance( lived ) . You could look at text4 , the Inaugural Address Corpus , to see examples of English going back to 1789, and why was compromise important search for athena traits words like nation , terror , god to Advantages Based for Nursing, see how these words have been used differently over time. We've also included text5 , the NPS Chat Corpus : search this for unconventional words like im , ur , lol . (Note that this corpus is uncensored!) Once you've spent a little while examining these texts, we hope you have a new sense of the richness and diversity of language. In the next chapter you will learn how to athena, access a broader range of text, including text in languages other than English. A concordance permits us to see words in industry context. For example, we saw that monstrous occurred in contexts such as the ___ pictures and personality traits a ___ size . What other words appear in a similar range of contexts?

We can find out by appending the term similar to the name of the text in question, then inserting the relevant word in parentheses: Observe that we get different results for different texts. Austen uses this word quite differently from Melville; for her, monstrous has positive connotations, and of Simulation sometimes functions as an intensifier like the word very . The term common_contexts allows us to examine just the athena contexts that are shared by two or more words, such as monstrous and very . We have to enclose these words by square brackets as well as parentheses, and separate them with a comma: Your Turn: Pick another pair of words and compare their usage in Between Native American and Modern Medicine, As Explained "Native two different texts, using the similar() and common_contexts() functions. It is one thing to automatically detect that a particular word occurs in a text, and to display some words that appear in the same context. Personality Traits! However, we can also determine the location of a word in the text: how many words from the why was compromise beginning it appears. Athena Traits! This positional information can be displayed using a dispersion plot . Industry! Each stripe represents an instance of a word, and each row represents the entire text. In 1.2 we see some striking patterns of word usage over the last 220 years (in an artificial text constructed by joining the texts of the Inaugural Address Corpus end-to-end).

You can produce this plot as shown below. You might like to try more words (e.g., liberty , constitution ), and different texts. Can you predict the dispersion of a word before you view it? As before, take care to get the quotes, commas, brackets and parentheses exactly right. Figure 1.2 : Lexical Dispersion Plot for Words in U.S.

Presidential Inaugural Addresses: This can be used to investigate changes in language use over time. Important: You need to have Python's NumPy and Matplotlib packages installed in order to produce the graphical plots used in this book. Please see http://nltk.org/ for installation instructions. You can also plot the frequency of athena personality word usage through time using https://books.google.com/ngrams. Now, just for about of Simulation Eductation for Nursing fun, let's try generating some random text in the various styles we have just seen. To do this, we type the personality name of the text followed by the term generate . Why Was The Great Compromise Important! (We need to include the parentheses, but there's nothing that goes between them.) The generate() method is not available in NLTK 3.0 but will be reinstated in a subsequent version. The most obvious fact about texts that emerges from the preceding examples is personality traits, that they differ in restaurant industry the vocabulary they use. In this section we will see how to use the computer to athena personality, count the words in a text in a variety of useful ways. Technology! As before, you will jump right in and experiment with the Python interpreter, even though you may not have studied Python systematically yet.

Test your understanding by athena personality traits modifying the examples, and trying the exercises at the end of the the great compromise chapter. Let's begin by finding out the length of personality traits a text from start to finish, in Different Types of Unemployment Essay terms of the words and punctuation symbols that appear. We use the traits term len to Essay about Advantages, get the length of something, which we'll apply here to the book of Genesis: So Genesis has 44,764 words and punctuation symbols, or tokens. A token is the technical name for a sequence of athena traits characters such as hairy , his , or :) that we want to treat as a group. When we count the number of The Relationship Between Native American and Modern Medicine, As Explained "Native American Medicine" tokens in a text, say, the personality phrase to be or not to Native American Medicine, As Explained Medicine", be , we are counting occurrences of these sequences. Personality Traits! Thus, in our example phrase there are two occurrences of to , two of be , and when tet offensive one each of or and not . But there are only four distinct vocabulary items in this phrase. How many distinct words does the book of Genesis contain? To work this out in athena personality traits Python, we have to industry, pose the question slightly differently. The vocabulary of a text is just the set of tokens that it uses, since in a set, all duplicates are collapsed together.

In Python we can obtain the vocabulary items of text3 with the traits command: set(text3) . When you do this, many screens of words will fly past. Now try the following: By wrapping sorted() around the Python expression set(text3) , we obtain a sorted list of vocabulary items, beginning with various punctuation symbols and why was important continuing with words starting with A . All capitalized words precede lowercase words. We discover the size of the vocabulary indirectly, by asking for the number of athena items in Different Types of Unemployment Essay the set, and again we can use len to obtain this number . Although it has 44,764 tokens, this book has only athena personality, 2,789 distinct words, or word types. A word type is the form or spelling of the word independently of compromise its specific occurrences in a text that is, the word considered as a unique item of vocabulary. Our count of 2,789 items will include punctuation symbols, so we will generally call these unique items types instead of word types.

Now, let's calculate a measure of the lexical richness of the athena personality traits text. The next example shows us that the number of distinct words is of information, just 6% of the athena traits total number of important words, or equivalently that each word is used 16 times on average (remember if you're using Python 2, to start with from __future__ import division ). Next, let's focus on particular words. Athena Personality! We can count how often a word occurs in a text, and compute what percentage of the text is taken up by a specific word: Your Turn: How many times does the and Modern American Medicine" word lol appear in text5 ? How much is this as a percentage of the athena personality total number of words in this text?

You may want to repeat such calculations on industry several texts, but it is tedious to athena traits, keep retyping the formula. Instead, you can come up with your own name for a task, like lexical_diversity or percentage, and associate it with a block of code. Now you only have to type a short name instead of one or more complete lines of Python code, and you can re-use it as often as you like. The block of code that does a task for us is American and Modern Medicine, "Native, called a function , and we define a short name for our function with the keyword def . Athena Personality Traits! The next example shows how to define two new functions, lexical_diversity() and percentage() : The Python interpreter changes the Different of Unemployment Essay prompt from to . after encountering the colon at the end of the athena personality traits first line. The . prompt indicates that Python expects an Essay, indented code block to appear next. It is up to you to athena personality, do the indentation, by typing four spaces or hitting the tab key. To finish the indented block just enter a blank line.

In the definition of lexical_diversity() , we specify a parameter named text . This parameter is a placeholder for the actual text whose lexical diversity we want to compute, and reoccurs in the block of code that will run when the function is used . Benefits Of Information Technology! Similarly, percentage() is defined to take two parameters, named count and total . Once Python knows that lexical_diversity() and percentage() are the names for specific blocks of code, we can go ahead and use these functions: To recap, we use or call a function such as lexical_diversity() by typing its name, followed by an open parenthesis, the name of the text, and then a close parenthesis. Personality! These parentheses will show up often; their role is to separate the name of a task such as lexical_diversity() from the data that the task is to be performed on such as text3 . The data value that we place in the parentheses when we call a function is an argument to the function. You have already encountered several functions in this chapter, such as len() , set() , and sorted() . By convention, we will always add an empty pair of parentheses after a function name, as in len() , just to make clear that what we are talking about is a function rather than some other kind of Python expression. Functions are an important concept in programming, and we only mention them at of information technology, the outset to traits, give newcomers a sense of the power and creativity of programming. Of Unemployment! Don't worry if you find it a bit confusing right now. Later we'll see how to use functions when tabulating data, as in personality traits 1.1. Each row of the table will involve the same computation but with different data, and we'll do this repetitive work using a function. Lexical Diversity of when Various Genres in the Brown Corpus.

2 A Closer Look at Python: Texts as Lists of Words. You've seen some important elements of the Python programming language. Let's take a few moments to athena personality, review them systematically. What is a text? At one level, it is a sequence of benefits of information symbols on a page such as this one. At another level, it is a sequence of athena chapters, made up of a sequence of sections, where each section is a sequence of paragraphs, and so on. However, for our purposes, we will think of a text as nothing more than a sequence of words and punctuation. Here's how we represent text in Python, in this case the opening sentence of Moby Dick : After the prompt we've given a name we made up, sent1 , followed by the equals sign, and when tet offensive then some quoted words, separated with commas, and surrounded with brackets. This bracketed material is known as a list in Python: it is how we store a text. We can inspect it by typing the athena traits name . We can ask for its length . We can even apply our own lexical_diversity() function to it . Some more lists have been defined for you, one for the opening sentence of each of of information our texts, sent2 sent9 . We inspect two of traits them here; you can see the rest for yourself using the Python interpreter (if you get an error which says that sent2 is The Relationship Native American, not defined, you need to first type from nltk.book import * ). Your Turn: Make up a few sentences of your own, by personality typing a name, equals sign, and industry a list of words, like this: ex1 = [ 'Monty' , 'Python' , 'and' , 'the' , 'Holy' , 'Grail' ] . Repeat some of the other Python operations we saw earlier in 1, e.g., sorted(ex1) , len(set(ex1)) , ex1.count( 'the' ) . A pleasant surprise is that we can use Python's addition operator on lists.

Adding two lists creates a new list with everything from the first list, followed by everything from the second list: This special use of the addition operation is called concatenation ; it combines the lists together into a single list. Athena Personality Traits! We can concatenate sentences to build up a text. We don't have to literally type the lists either; we can use short names that refer to of Unemployment, pre-defined lists. What if we want to add a single item to a list? This is known as appending . When we append() to a list, the list itself is updated as a result of the operation. As we have seen, a text in Python is a list of words, represented using a combination of traits brackets and quotes. Just as with an ordinary page of text, we can count up the total number of words in text1 with len(text1) , and count the occurrences in The Relationship Between Native "Native American Medicine" a text of a particular word say, 'heaven' using text1.count( 'heaven' ) . With some patience, we can pick out the traits 1st, 173rd, or even 14,278th word in a printed text. Different Types Essay! Analogously, we can identify the personality elements of a Python list by their order of occurrence in restaurant industry the list.

The number that represents this position is the athena personality item's index . We instruct Python to Essay of Simulation Eductation, show us the athena item that occurs at why was, an index such as 173 in a text by athena personality writing the name of the text followed by the index inside square brackets: We can do the converse; given a word, find the index of when it first occurs: Indexes are a common way to access the words of a text, or, more generally, the elements of restaurant any list. Python permits us to athena, access sublists as well, extracting manageable pieces of language from large texts, a technique known as slicing . Indexes have some subtleties, and we'll explore these with the help of an artificial sentence: Notice that our indexes start from zero: sent element zero, written sent[0] , is the first word, 'word1' , whereas sent element 9 is 'word10' . The reason is industry, simple: the moment Python accesses the content of a list from the computer's memory, it is already at the first element; we have to tell it how many elements forward to go. Athena Personality! Thus, zero steps forward leaves it at the first element. This practice of restaurant counting from zero is initially confusing, but typical of athena modern programming languages. You'll quickly get the Different Essay hang of athena personality it if you've mastered the system of counting centuries where 19XY is Different of Unemployment Essay, a year in the 20th century, or if you live in a country where the floors of a building are numbered from traits, 1, and so walking up n-1 flights of stairs takes you to level n . Now, if we accidentally use an index that is too large, we get an Essay, error: This time it is not a syntax error, because the program fragment is athena personality traits, syntactically correct.

Instead, it is a runtime error , and it produces a Traceback message that shows the context of the error, followed by the name of the Essay Advantages Based Eductation error, IndexError , and a brief explanation. Let's take a closer look at slicing, using our artificial sentence again. Here we verify that the slice 5:8 includes sent elements at indexes 5, 6, and 7: By convention, m:n means elements m n-1 . As the next example shows, we can omit the first number if the slice begins at the start of the personality traits list , and we can omit the why was important second number if the slice goes to the end : We can modify an element of a list by assigning to one of athena traits its index values. Of Simulation Based Eductation For Nursing! In the athena personality traits next example, we put sent[0] on the left of the equals sign . We can also replace an when tet offensive, entire slice with new material . A consequence of this last change is that the list only has four elements, and accessing a later value generates an error . Your Turn: Take a few minutes to define a sentence of your own and modify individual words and groups of words (slices) using the same methods used earlier.

Check your understanding by trying the exercises on athena personality lists at the end of this chapter. From the start of The Relationship American and Modern American 1, you have had access to texts called text1 , text2 , and so on. Athena Traits! It saved a lot of why was the great typing to be able to refer to athena, a 250,000-word book with a short name like this! In general, we can make up names for anything we care to calculate. We did this ourselves in of Unemployment Essay the previous sections, e.g., defining a variable sent1 , as follows: Such lines have the form: variable = expression . Python will evaluate the expression, and save its result to the variable. This process is called assignment . It does not generate any output; you have to type the variable on a line of its own to athena personality, inspect its contents. Different Types Essay! The equals sign is slightly misleading, since information is moving from the right side to the left. It might help to think of it as a left-arrow. The name of the athena personality traits variable can be anything you like, e.g., my_sent , sentence , xyzzy . It must start with a letter, and restaurant can include numbers and underscores.

Here are some examples of athena personality variables and assignments: Remember that capitalized words appear before lowercase words in sorted lists. Notice in the previous example that we split the definition of my_sent over two lines. Python expressions can be split across multiple lines, so long as this happens within any kind of brackets. Python uses the . prompt to indicate that more input is expected. It doesn't matter how much indentation is used in these continuation lines, but some indentation usually makes them easier to read. It is was the tet offensive, good to choose meaningful variable names to remind you and to help anyone else who reads your Python code what your code is meant to traits, do. Python does not try to make sense of the names; it blindly follows your instructions, and does not object if you do something confusing, such as one = 'two' or two = 3 . The only restriction is that a variable name cannot be any of Python's reserved words, such as def , if , not , and import . Based Eductation! If you use a reserved word, Python will produce a syntax error: We will often use variables to hold intermediate steps of a computation, especially when this makes the code easier to follow. Thus len(set(text1)) could also be written:

Take care with your choice of names (or identifiers ) for Python variables. First, you should start the traits name with a letter, optionally followed by digits ( 0 to 9 ) or letters. Thus, abc23 is Advantages of Simulation, fine, but 23abc will cause a syntax error. Athena Personality Traits! Names are case-sensitive, which means that myVar and myvar are distinct variables. Variable names cannot contain whitespace, but you can separate words using an underscore, e.g., my_var . Be careful not to insert a hyphen instead of an restaurant industry, underscore: my-var is wrong, since Python interprets the - as a minus sign. Some of the methods we used to access the elements of a list also work with individual words, or strings . For example, we can assign a string to a variable , index a string , and slice a string : We can also perform multiplication and addition with strings: We can join the words of athena personality traits a list to make a single string, or split a string into a list, as follows: We will come back to Different of Unemployment, the topic of strings in athena traits 3. Essay About Of Simulation For Nursing! For the time being, we have two important building blocks lists and strings and are ready to get back to traits, some language analysis. 3 Computing with Language: Simple Statistics. Let's return to our exploration of the ways we can bring our computational resources to compromise, bear on large quantities of text. We began this discussion in 1, and saw how to athena, search for words in why was important context, how to compile the vocabulary of a text, how to generate random text in the same style, and so on.

In this section we pick up the question of what makes a text distinct, and athena traits use automatic methods to was the, find characteristic words and athena expressions of a text. As in 1, you can try new features of the Essay about Advantages Based Eductation for Nursing Python language by copying them into the interpreter, and you'll learn about personality these features systematically in the following section. Before continuing further, you might like to check your understanding of the last section by predicting the output of the following code. You can use the interpreter to check whether you got it right. If you're not sure how to do this task, it would be a good idea to review the previous section before continuing further. How can we automatically identify the words of when tet offensive a text that are most informative about the topic and genre of the athena personality traits text? Imagine how you might go about finding the 50 most frequent words of a book. One method would be to keep a tally for each vocabulary item, like that shown in 3.1. The tally would need thousands of rows, and it would be an exceedingly laborious process so laborious that we would rather assign the task to a machine. Figure 3.1 : Counting Words Appearing in restaurant industry a Text (a frequency distribution)

The table in 3.1 is known as a frequency distribution , and it tells us the frequency of each vocabulary item in the text. (In general, it could count any kind of observable event.) It is a distribution because it tells us how the total number of personality word tokens in the text are distributed across the industry vocabulary items. Since we often need frequency distributions in language processing, NLTK provides built-in support for them. Let's use a FreqDist to find the 50 most frequent words of Moby Dick : When we first invoke FreqDist , we pass the name of the text as an athena traits, argument . We can inspect the total number of words (outcomes) that have been counted up 260,819 in technology the case of Moby Dick . The expression most_common(50) gives us a list of the 50 most frequently occurring types in the text . Your Turn: Try the preceding frequency distribution example for personality traits yourself, for text2 . Be careful to use the industry correct parentheses and uppercase letters. If you get an error message NameError: name 'FreqDist' is not defined , you need to start your work with from nltk.book import * Do any words produced in the last example help us grasp the topic or genre of this text? Only one word, whale , is slightly informative! It occurs over 900 times. The rest of the words tell us nothing about the text; they're just English plumbing. What proportion of the personality text is taken up with such words?

We can generate a cumulative frequency plot for these words, using fdist1.plot(50, cumulative=True) , to produce the graph in 3.2. These 50 words account for nearly half the book! Figure 3.2 : Cumulative Frequency Plot for 50 Most Frequently Words in benefits of information Moby Dick : these account for athena nearly half of the tokens. If the frequent words don't help us, how about the when tet offensive words that occur once only, the so-called hapaxes ? View them by typing fdist1.hapaxes() . This list contains lexicographer , cetological , contraband , expostulations , and traits about 9,000 others. It seems that there are too many rare words, and without seeing the context we probably can't guess what half of the hapaxes mean in of information technology any case! Since neither frequent nor infrequent words help, we need to try something else. 3.2 Fine-grained Selection of Words. Next, let's look at the long words of a text; perhaps these will be more characteristic and athena informative. For this we adapt some notation from set theory. We would like to find the words from the vocabulary of the text that are more than 15 characters long.

Let's call this property P , so that P(w) is true if and only if w is more than 15 characters long. Now we can express the when tet offensive words of interest using mathematical set notation as shown in (1a). This means the personality set of all w such that w is an element of benefits of information V (the vocabulary) and w has property P . The corresponding Python expression is given in (1b). (Note that it produces a list, not a set, which means that duplicates are possible.) Observe how similar the two notations are. Athena Personality! Let's go one more step and restaurant industry write executable Python code: For each word w in the vocabulary V , we check whether len(w) is athena personality traits, greater than 15; all other words will be ignored.

We will discuss this syntax more carefully later. Your Turn: Try out the previous statements in the Python interpreter, and experiment with changing the when tet offensive text and changing the length condition. Does it make a difference to your results if you change the variable names, e.g., using [word for word in vocab if . ] ? Let's return to our task of finding words that characterize a text. Notice that the long words in text4 reflect its national focus constitutionally , transcontinental whereas those in text5 reflect its informal content: boooooooooooglyyyyyy and yuuuuuuuuuuuummmmmmmmmmmm . Have we succeeded in automatically extracting words that typify a text? Well, these very long words are often hapaxes (i.e., unique) and perhaps it would be better to find frequently occurring long words. This seems promising since it eliminates frequent short words (e.g., the ) and infrequent long words (e.g. antiphilosophists ). Athena Personality! Here are all words from the chat corpus that are longer than seven characters, that occur more than seven times: Notice how we have used two conditions: len(w) 7 ensures that the words are longer than seven letters, and fdist5[w] 7 ensures that these words occur more than seven times. At last we have managed to Advantages of Simulation Based, automatically identify the athena personality traits frequently-occurring content-bearing words of the text.

It is a modest but important milestone: a tiny piece of code, processing tens of thousands of words, produces some informative output. A collocation is a sequence of words that occur together unusually often. Thus red wine is a collocation, whereas the wine is not. A characteristic of collocations is that they are resistant to substitution with words that have similar senses; for example, maroon wine sounds definitely odd. To get a handle on restaurant industry collocations, we start off by extracting from a text a list of word pairs, also known as bigrams . This is easily accomplished with the function bigrams() : If you omitted list() above, and just typed bigrams([ 'more' , . ]) , you would have seen output of the form generator object bigrams at 0x10fb8b3a8 . Athena Traits! This is Types of Unemployment Essay, Python's way of saying that it is ready to compute a sequence of items, in this case, bigrams. For now, you just need to know to athena traits, tell Python to convert it into restaurant, a list, using list() . Here we see that the pair of traits words than-done is a bigram, and we write it in Python as ( 'than' , 'done' ) . Now, collocations are essentially just frequent bigrams, except that we want to pay more attention to why was compromise important, the cases that involve rare words. In particular, we want to find bigrams that occur more often than we would expect based on the frequency of the athena traits individual words. The collocations() function does this for us.

We will see how it works later. The collocations that emerge are very specific to the genre of the texts. In order to find red wine as a collocation, we would need to process a much larger body of text. Counting words is useful, but we can count other things too. For example, we can look at the distribution of word lengths in a text, by creating a FreqDist out of a long list of numbers, where each number is the length of the corresponding word in the text: We start by deriving a list of the Different Types Essay lengths of words in text1 , and the FreqDist then counts the number of times each of these occurs . The result is personality traits, a distribution containing a quarter of a million items, each of which is a number corresponding to a word token in the text. But there are at most only 20 distinct items being counted, the numbers 1 through 20, because there are only 20 different word lengths. I.e., there are words consisting of just one character, two characters, . twenty characters, but none with twenty one or more characters.

One might wonder how frequent the different lengths of word are (e.g., how many words of length four appear in benefits of information technology the text, are there more words of length five than length four, etc). We can do this as follows: From this we see that the most frequent word length is athena traits, 3, and that words of length 3 account for roughly 50,000 (or 20%) of the words making up the book. Although we will not pursue it here, further analysis of word length might help us understand differences between authors, genres, or languages. 3.1 summarizes the functions defined in The Relationship American "Native frequency distributions. Functions Defined for personality NLTK's Frequency Distributions. Our discussion of frequency distributions has introduced some important Python concepts, and we will look at them systematically in 4. 4 Back to Python: Making Decisions and Taking Control. So far, our little programs have had some interesting qualities: the ability to work with language, and the potential to save human effort through automation.

A key feature of programming is the restaurant ability of machines to make decisions on our behalf, executing instructions when certain conditions are met, or repeatedly looping through text data until some condition is satisfied. This feature is athena personality traits, known as control , and The Relationship American and Modern Medicine, As Explained "Native American is the focus of this section. Python supports a wide range of operators, such as and personality = , for testing the relationship between values. The full set of these relational operators is shown in compromise important 4.1. Numerical Comparison Operators. We can use these to select different words from a sentence of news text.

Here are some examples only the athena traits operator is changed from one line to the next. They all use sent7 , the first sentence from text7 ( Wall Street Journal ). As before, if you get an and Modern As Explained American Medicine", error saying that sent7 is undefined, you need to personality traits, first type: from about Advantages Eductation for Nursing, nltk.book import * There is a common pattern to all of these examples: [w for w in text if condition ] , where condition is a Python test that yields either true or false. In the cases shown in athena the previous code example, the condition is restaurant, always a numerical comparison. However, we can also test various properties of athena personality traits words, using the functions listed in 4.2. Some Word Comparison Operators. Here are some examples of these operators being used to select words from benefits technology, our texts: words ending with -ableness ; words containing gnt ; words having an initial capital; and words consisting entirely of athena personality traits digits. We can also create more complex conditions. Tet Offensive! If c is a condition, then not c is also a condition. Personality Traits! If we have two conditions c 1 and c 2 , then we can combine them to form a new condition using conjunction and Different of Unemployment disjunction: c 1 and c 2 , c 1 or c 2 . Your Turn: Run the following examples and try to explain what is going on in each one.

Next, try to make up some conditions of your own. In 3, we saw some examples of counting items other than words. Let's take a closer look at the notation we used: These expressions have the form [f(w) for . ] or [w.f() for . ] , where f is personality traits, a function that operates on a word to compute its length, or to convert it to about Advantages of Simulation Eductation, uppercase. For now, you don't need to athena, understand the difference between the notations f(w) and w.f() . Instead, simply learn this Python idiom which performs the The Relationship American and Modern "Native same operation on every element of a list.

In the preceding examples, it goes through each word in text1 , assigning each one in turn to the variable w and performing the specified operation on the variable. The notation just described is athena, called a list comprehension. This is our first example of a Python idiom, a fixed notation that we use habitually without bothering to analyze each time. Mastering such idioms is an important part of becoming a fluent Python programmer. Let's return to the question of vocabulary size, and apply the same idiom here: Now that we are not double-counting words like This and this , which differ only in capitalization, we've wiped 2,000 off the vocabulary count! We can go a step further and eliminate numbers and punctuation from the vocabulary count by filtering out any non-alphabetic items: This example is slightly complicated: it lowercases all the purely alphabetic items. Perhaps it would have been simpler just to count the lowercase-only items, but this gives the wrong answer (why?). Don't worry if you don't feel confident with list comprehensions yet, since you'll see many more examples along with explanations in the following chapters. Most programming languages permit us to execute a block of restaurant code when a conditional expression , or if statement, is satisfied.

We already saw examples of conditional tests in code like [w for w in sent7 if len(w) 4] . In the following program, we have created a variable called word containing the string value 'cat' . Personality! The if statement checks whether the test len(word) 5 is true. When Was The! It is, so the body of the if statement is invoked and the print statement is executed, displaying a message to the user. Remember to indent the print statement by typing four spaces. When we use the Python interpreter we have to add an extra blank line in order for it to athena traits, detect that the nested block is complete. If you are using Python 2.6 or 2.7, you need to include the following line in order for the above print function to be recognized: If we change the The Relationship Native American and Modern Medicine, conditional test to len(word) = 5 , to check that the length of word is greater than or equal to 5 , then the test will no longer be true. This time, the traits body of the if statement will not be executed, and no message is shown to the user: An if statement is known as a control structure because it controls whether the code in the indented block will be run.

Another control structure is the for of information technology loop. Try the traits following, and remember to about of Simulation for Nursing, include the colon and athena personality traits the four spaces: This is compromise important, called a loop because Python executes the code in circular fashion. It starts by traits performing the assignment word = 'Call' , effectively using the word variable to name the first item of the list. Then, it displays the value of word to the user. Next, it goes back to the for Essay of Simulation Eductation for Nursing statement, and performs the assignment word = 'me' , before displaying this new value to the user, and so on. It continues in this fashion until every item of the list has been processed. Now we can combine the if and for statements. We will loop over every item of the traits list, and print the item only restaurant industry, if it ends with the letter l . We'll pick another name for the variable to demonstrate that Python doesn't try to make sense of variable names. You will notice that if and for athena personality statements have a colon at the end of the line, before the indentation begins.

In fact, all Python control structures end with a colon. The colon indicates that the Types Essay current statement relates to the indented block that follows. We can also specify an action to be taken if the condition of the if statement is athena traits, not met. Here we see the elif (else if) statement, and the else statement. Notice that these also have colons before the indented code. As you can see, even with this small amount of Essay about of Simulation for Nursing Python knowledge, you can start to build multiline Python programs. It's important to develop such programs in pieces, testing that each piece does what you expect before combining them into a program. Athena Personality Traits! This is why the was the tet offensive Python interactive interpreter is so invaluable, and why you should get comfortable using it.

Finally, let's combine the idioms we've been exploring. Athena Personality Traits! First, we create a list of cie and cei words, then we loop over each item and print it. Notice the extra information given in the print statement: end=' ' . This tells Python to print a space (not the default newline) after each word. 5 Automatic Natural Language Understanding. We have been exploring language bottom-up, with the help of texts and Medicine, As Explained "Native American Medicine" the Python programming language. However, we're also interested in exploiting our knowledge of language and computation by building useful language technologies. We'll take the opportunity now to athena, step back from the nitty-gritty of code in order to of Unemployment Essay, paint a bigger picture of natural language processing. At a purely practical level, we all need help to personality traits, navigate the universe of information locked up in text on the Web. Search engines have been crucial to the growth and popularity of the benefits of information Web, but have some shortcomings. It takes skill, knowledge, and some luck, to extract answers to such questions as: What tourist sites can I visit between Philadelphia and Pittsburgh on a limited budget? What do experts say about digital SLR cameras?

What predictions about the steel market were made by athena traits credible commentators in the past week? Getting a computer to answer them automatically involves a range of language processing tasks, including information extraction, inference, and restaurant summarization, and would need to be carried out on a scale and with a level of robustness that is still beyond our current capabilities. On a more philosophical level, a long-standing challenge within artificial intelligence has been to build intelligent machines, and a major part of intelligent behaviour is understanding language. For many years this goal has been seen as too difficult. Athena Personality! However, as NLP technologies become more mature, and robust methods for analyzing unrestricted text become more widespread, the prospect of natural language understanding has re-emerged as a plausible goal. In this section we describe some language understanding technologies, to give you a sense of the Different Essay interesting challenges that are waiting for you. In word sense disambiguation we want to athena, work out which sense of Essay of Simulation a word was intended in a given context. Consider the ambiguous words serve and dish : In a sentence containing the phrase: he served the dish , you can detect that both serve and dish are being used with their food meanings. It's unlikely that the topic of discussion shifted from personality traits, sports to crockery in the space of three words.

This would force you to invent bizarre images, like a tennis pro taking out his or her frustrations on restaurant industry a china tea-set laid out beside the court. In other words, we automatically disambiguate words using context, exploiting the simple fact that nearby words have closely related meanings. As another example of this contextual effect, consider the word by , which has several meanings, e.g.: the book by Chesterton (agentive Chesterton was the author of the book); the cup by the stove (locative the stove is where the cup is); and submit by Friday (temporal Friday is the time of the submitting). Athena! Observe in (3c) that the meaning of the italicized word helps us interpret the restaurant industry meaning of by . A deeper kind of language understanding is to personality traits, work out who did what to whom i.e., to detect the Different Types Essay subjects and objects of verbs. You learnt to do this in elementary school, but it's harder than you might think. In the sentence the thieves stole the paintings it is easy to tell who performed the traits stealing action. Consider three possible following sentences in (4c), and the great compromise important try to determine what was sold, caught, and found (one case is ambiguous). Answering this question involves finding the antecedent of the traits pronoun they , either thieves or paintings. Computational techniques for tackling this problem include anaphora resolution identifying what a pronoun or noun phrase refers to and about of Simulation Based semantic role labeling identifying how a noun phrase relates to the verb (as agent, patient, instrument, and so on). If we can automatically solve such problems of athena language understanding, we will be able to move on to tasks that involve generating language output, such as question answering and machine translation . Essay Based Eductation! In the first case, a machine should be able to answer a user's questions relating to collection of texts:

The machine's answer demonstrates that it has correctly worked out that they refers to paintings and not to thieves. In the personality second case, the machine should be able to translate the text into another language, accurately conveying the when was the meaning of the original text. In translating the example text into French, we are forced to choose the gender of the pronoun in the second sentence: ils (masculine) if the thieves are found, and elles (feminine) if the paintings are found. Traits! Correct translation actually depends on correct understanding of the pronoun. In all of these examples, working out the sense of a word, the subject of a verb, and the antecedent of a pronoun are steps in Essay about Eductation establishing the meaning of a sentence, things we would expect a language understanding system to be able to do. For a long time now, machine translation (MT) has been the holy grail of language understanding, ultimately seeking to provide high-quality, idiomatic translation between any pair of languages. Athena! Its roots go back to the early days of the Cold War, when the promise of automatic translation led to substantial government sponsorship, and benefits with it, the genesis of NLP itself. Today, practical translation systems exist for particular pairs of languages, and some are integrated into web search engines. However, these systems have some serious shortcomings, which are starkly revealed by translating a sentence back and forth between a pair of languages until equilibrium is reached, e.g.:

Observe that the system correctly translates Alice Springs from English to German (in the athena line starting 1 ), but on the way back to English, this ends up as Alice jump (line 2 ). The preposition before is initially translated into the corresponding German preposition vor , but later into the conjunction bevor (line 5). After line 5 the sentences become nonsensical (but notice the various phrasings indicated by the commas, and benefits of information technology the change from personality, jump to leap ). The translation system did not recognize when a word was part of a proper name, and it misinterpreted the grammatical structure. Your Turn: Try this yourself using http://translationparty.com/ Machine translation is difficult because a given word could have several possible translations (depending on technology its meaning), and because word order must be changed in keeping with the grammatical structure of the target language. Today these difficulties are being faced by collecting massive quantities of athena personality traits parallel texts from news and government websites that publish documents in two or more languages. Given a document in German and English, and possibly a bilingual dictionary, we can automatically pair up the sentences, a process called text alignment . Once we have a million or more sentence pairs, we can detect corresponding words and phrases, and build a model that can be used for about of Simulation Based Eductation for Nursing translating new text.

In the athena history of artificial intelligence, the tet offensive chief measure of intelligence has been a linguistic one, namely the Turing Test : can a dialogue system, responding to a user's text input, perform so naturally that we cannot distinguish it from a human-generated response? In contrast, today's commercial dialogue systems are very limited, but still perform useful functions in narrowly-defined domains, as we see here: You could not ask this system to provide driving instructions or details of nearby restaurants unless the required information had already been stored and suitable question-answer pairs had been incorporated into the language processing system. Observe that this system seems to understand the user's goals: the user asks when a movie is athena traits, showing and the system correctly determines from this that the user wants to see the movie. This inference seems so obvious that you probably didn't notice it was made, yet a natural language system needs to be endowed with this capability in order to interact naturally. Without it, when asked Do you know when Saving Private Ryan is the great compromise important, playing? , a system might unhelpfully respond with a cold Yes . However, the developers of athena traits commercial dialogue systems use contextual assumptions and business logic to ensure that the Essay of Simulation different ways in athena traits which a user might express requests or provide information are handled in a way that makes sense for the particular application. So, if you type When is . Restaurant! , or I want to know when . , or Can you tell me when . , simple rules will always yield screening times. This is enough for the system to provide a useful service. Figure 5.1 : Simple Pipeline Architecture for a Spoken Dialogue System: Spoken input (top left) is analyzed, words are recognized, sentences are parsed and interpreted in context, application-specific actions take place (top right); a response is planned, realized as a syntactic structure, then to athena personality traits, suitably inflected words, and finally to spoken output; different types of The Relationship Between Native and Modern Medicine" linguistic knowledge inform each stage of the process.

Dialogue systems give us an opportunity to athena personality, mention the commonly assumed pipeline for of Unemployment Essay NLP. 5.1 shows the architecture of a simple dialogue system. Along the top of the diagram, moving from left to right, is a pipeline of some language understanding components . These map from speech input via syntactic parsing to some kind of meaning representation. Along the middle, moving from traits, right to left, is the why was reverse pipeline of components for athena converting concepts to speech. These components make up the dynamic aspects of the Types of Unemployment Essay system.

At the athena personality traits bottom of the diagram are some representative bodies of static information: the industry repositories of language-related data that the processing components draw on to do their work. Your Turn: For an example of athena personality traits a primitive dialogue system, try having a conversation with an NLTK chatbot. To see the available chatbots, run nltk.chat.chatbots() . (Remember to Essay Advantages Eductation, import nltk first.) The challenge of language understanding has been brought into focus in recent years by a public shared task called Recognizing Textual Entailment (RTE). Personality! The basic scenario is simple.

Suppose you want to of information technology, find evidence to support the hypothesis: Sandra Goudie was defeated by Max Purnell , and that you have another short text that seems to be relevant, for example, Sandra Goudie was first elected to Parliament in the 2002 elections, narrowly winning the traits seat of Coromandel by when tet offensive defeating Labour candidate Max Purnell and pushing incumbent Green MP Jeanette Fitzsimons into third place . Does the text provide enough evidence for you to accept the hypothesis? In this particular case, the answer will be No. You can draw this conclusion easily, but it is very hard to come up with automated methods for making the right decision. The RTE Challenges provide data that allow competitors to develop their systems, but not enough data for brute force machine learning techniques (a topic we will cover in chap-data-intensive). Consequently, some linguistic analysis is crucial. In the previous example, it is athena, important for the system to note that Sandra Goudie names the person being defeated in the hypothesis, not the person doing the defeating in the text. As another illustration of the difficulty of the task, consider the following text-hypothesis pair: In order to why was, determine whether the hypothesis is supported by the text, the system needs the following background knowledge: (i) if someone is an author of a book, then he/she has written that book; (ii) if someone is an editor of traits a book, then he/she has not written (all of) that book; (iii) if someone is editor or author of eighteen books, then one cannot conclude that he/she is author of eighteen books. Despite the research-led advances in tasks like RTE, natural language systems that have been deployed for benefits of information real-world applications still cannot perform common-sense reasoning or draw on world knowledge in a general and robust manner. We can wait for these difficult artificial intelligence problems to be solved, but in the meantime it is necessary to live with some severe limitations on the reasoning and personality knowledge capabilities of natural language systems. Accordingly, right from the beginning, an important goal of NLP research has been to make progress on the difficult task of building technologies that understand language, using superficial yet powerful techniques instead of unrestricted knowledge and reasoning capabilities.

Indeed, this is one of the goals of this book, and we hope to of Simulation for Nursing, equip you with the knowledge and skills to build useful NLP systems, and to contribute to the long-term aspiration of building intelligent machines. Texts are represented in traits Python using lists: [ 'Monty' , 'Python' ] . We can use indexing, slicing, and the len() function on lists. The Great Compromise! A word token is a particular appearance of a given word in athena traits a text; a word type is the unique form of the Different of Unemployment Essay word as a particular sequence of letters. We count word tokens using len(text) and word types using len(set(text)) . We obtain the vocabulary of a text t using sorted(set(t)) . We operate on each item of a text using [f(x) for athena personality traits x in Types of Unemployment Essay text] . To derive the vocabulary, collapsing case distinctions and ignoring punctuation, we can write set(w.lower() for athena personality w in text if w.isalpha()) . We process each word in a text using a for statement, such as for w in t: or for word in when tet offensive text: . Personality Traits! This must be followed by the colon character and an indented block of code, to be executed each time through the loop. We test a condition using an if statement: if len(word) 5: . This must be followed by the colon character and Essay about Based for Nursing an indented block of code, to personality, be executed only if the condition is true. A frequency distribution is a collection of items along with their frequency counts (e.g., the words of a text and their frequency of appearance). Why Was The Great Compromise Important! A function is a block of code that has been assigned a name and can be reused. Functions are defined using the def keyword, as in def mult (x, y) ; x and athena personality y are parameters of the function, and act as placeholders for actual data values. A function is called by specifying its name followed by benefits of information zero or more arguments inside parentheses, like this: texts() , mult(3, 4) , len(text1) . This chapter has introduced new concepts in programming, natural language processing, and linguistics, all mixed in together. Many of them are consolidated in the following chapters. However, you may also want to consult the online materials provided with this chapter (at http://nltk.org/ ), including links to additional background materials, and personality links to The Relationship Native and Modern As Explained American, online NLP systems.

You may also like to read up on some linguistics and NLP-related concepts in personality traits Wikipedia (e.g., collocations, the Turing Test, the type-token distinction). You should acquaint yourself with the why was Python documentation available at http://docs.python.org/ , including the many tutorials and athena comprehensive reference materials linked there. A Beginner's Guide to Essay about for Nursing, Python is available at http://wiki.python.org/moin/BeginnersGuide . Miscellaneous questions about Python might be answered in the FAQ at http://python.org/doc/faq/general/ . As you delve into NLTK, you might want to personality traits, subscribe to the mailing list where new releases of the toolkit are announced. There is also an NLTK-Users mailing list, where users help each other as they learn how to use Python and NLTK for language analysis work. Details of these lists are available at http://nltk.org/ . For more information on of Unemployment Essay the topics covered in 5, and on traits NLP more generally, you might like to benefits, consult one of the following excellent books: Indurkhya, Nitin and Fred Damerau (eds, 2010) Handbook of Natural Language Processing (Second Edition) Chapman Hall/CRC. 2010. Athena Personality Traits! (Indurkhya Damerau, 2010)(Dale, Moisl, Somers, 2000) Jurafsky, Daniel and James Martin (2008) Speech and Language Processing (Second Edition). Prentice Hall. (Jurafsky Martin, 2008) Mitkov, Ruslan (ed, 2003) The Oxford Handbook of The Relationship American and Modern As Explained "Native Medicine" Computational Linguistics . Oxford University Press. Athena Personality! (second edition expected in 2010). (Mitkov, 2002) The Association for Computational Linguistics is the international organization that represents the field of NLP.

The ACL website ( http://www.aclweb.org/ ) hosts many useful resources, including: information about international and regional conferences and benefits of information technology workshops; the ACL Wiki with links to hundreds of athena useful resources; and the ACL Anthology , which contains most of the NLP research literature from the past 50+ years, fully indexed and Native Medicine, "Native American freely downloadable. Some excellent introductory Linguistics textbooks are: [Finegan2007]_ , (O'Grady et al, 2004), (OSU, 2007). Athena Personality! You might like to consult LanguageLog , a popular linguistics blog with occasional posts that use the industry techniques described in this book. #9788; Try using the Python interpreter as a calculator, and typing expressions like 12 / (4 + 1) . #9788; Given an alphabet of 26 letters, there are 26 to traits, the power 10, or 26 ** 10 , ten-letter strings we can form. That works out to 141167095653376 . How many hundred-letter strings are possible? #9788; The Python multiplication operation can be applied to lists. What happens when you type [ 'Monty' , 'Python' ] * 20 , or 3 * sent1 ? #9788; Review 1 on computing with language.

How many words are there in text2 ? How many distinct words are there? #9788; Compare the lexical diversity scores for humor and romance fiction in The Relationship American and Modern "Native 1.1. Which genre is more lexically diverse? #9788; Produce a dispersion plot of the four main protagonists in athena Sense and Sensibility : Elinor, Marianne, Edward, and Willoughby. What can you observe about the restaurant different roles played by the males and females in athena this novel? Can you identify the couples? #9788; Find the collocations in text5 . #9788; Consider the following Python expression: len(set(text4)) . State the Different Types Essay purpose of athena traits this expression. Describe the two steps involved in the great performing this computation. #9788; Review 2 on traits lists and strings.

Define a string and assign it to a variable, e.g., my_string = 'My String' (but put something more interesting in the string). Print the contents of this variable in two ways, first by simply typing the of Unemployment Essay variable name and pressing enter, then by using the print statement. Try adding the string to itself using my_string + my_string , or multiplying it by a number, e.g., my_string * 3 . Notice that the strings are joined together without any spaces. How could you fix this? #9788; Define a variable my_sent to be a list of words, using the syntax my_sent = [ My , sent ] (but with your own words, or a favorite saying). Use ' ' .join(my_sent) to convert this into a string. Use split() to split the string back into the list form you had to start with. #9788; Define several variables containing lists of words, e.g., phrase1 , phrase2 , and so on. Join them together in athena personality traits various combinations (using the plus operator) to form whole sentences.

What is the relationship between len(phrase1 + phrase2) and len(phrase1) + len(phrase2) ? #9788; Consider the following two expressions, which have the same value. Which one will typically be more relevant in NLP? Why? #9788; We have seen how to represent a sentence as a list of words, where each word is a sequence of characters. What does sent1[2][2] do? Why?

Experiment with other index values. #9788; The first sentence of text3 is provided to you in the variable sent3 . The index of the in sent3 is 1, because sent3[1] gives us 'the' . What are the indexes of the benefits two other occurrences of this word in sent3 ? #9788; Review the discussion of conditionals in 4. Find all words in the Chat Corpus ( text5 ) starting with the letter b . Show them in alphabetical order. #9788; Type the expression list(range(10)) at the interpreter prompt. Now try list(range(10, 20)) , list(range(10, 20, 2)) , and list(range(20, 10, -2)) . We will see a variety of traits uses for this built-in function in later chapters. #9681; Use text9.index() to find the index of the word sunset . You'll need to Based for Nursing, insert this word as an argument between the personality traits parentheses. When Was The Tet Offensive! By a process of trial and error, find the slice for the complete sentence that contains this word.

#9681; Using list addition, and the set and sorted operations, compute the vocabulary of the sentences sent1 . sent8 . #9681; What is the difference between the following two lines? Which one will give a larger value? Will this be the case for other texts? #9681; What is the difference between the following two tests: w.isupper() and not w.islower() ? #9681; Write the slice expression that extracts the last two words of text2 . #9681; Find all the four-letter words in the Chat Corpus ( text5 ). With the help of a frequency distribution ( FreqDist ), show these words in traits decreasing order of frequency. #9681; Review the discussion of The Relationship Medicine, As Explained looping with conditions in 4. Use a combination of for and if statements to personality, loop over the words of the movie script for Monty Python and the Holy Grail ( text6 ) and print all the uppercase words, one per line. #9681; Write expressions for finding all words in Types Essay text6 that meet the conditions listed below.

The result should be in the form of athena traits a list of words: [ 'word1' , 'word2' , . ] . Ending in ize Containing the Advantages Eductation letter z Containing the sequence of letters pt Having all lowercase letters except for an initial capital (i.e., titlecase ) #9681; Define sent to athena, be the restaurant industry list of words [ 'she' , 'sells' , 'sea' , 'shells' , 'by' , 'the' , 'sea' , 'shore' ] . Athena! Now write code to perform the following tasks: Print all words beginning with sh Print all words longer than four characters. #9681; What does the following Python code do? sum(len(w) for w in text1) Can you use it to work out the when was the tet offensive average word length of a text? #9681; Define a function called vocab_size(text) that has a single parameter for the text, and which returns the vocabulary size of the text. #9681; Define a function percent(word, text) that calculates how often a given word occurs in a text, and expresses the result as a percentage. #9681; We have been using sets to store vocabularies. Try the following Python expression: set(sent3) set(text1) . Experiment with this using different arguments to set() . What does it do?

Can you think of a practical application for this? About this document. UPDATED FOR NLTK 3.0. This is a chapter from Natural Language Processing with Python , by Steven Bird, Ewan Klein and Edward Loper, Copyright 2014 the athena personality authors. It is distributed with the Natural Language Toolkit [ http://nltk.org/ ], Version 3.0, under the terms of the Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License [http://creativecommons.org/licenses/by-nc-nd/3.0/us/].

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Curtis (2012) argues that there is a logical argument against homosexuality based on the fact that man cannot make love to another man , and a woman cannot make love to another woman. They further argue that it was Gods will that human procreate and homosexuality seems. Journey with Faith Analysis of Young Goodman Brown Nathaniel Hawthorne is a revered writer who allows the reader to of information imagine and paint a picture in the mind of the events taking place in a story. His writings intrigue the athena personality traits brain and give way to attaining fresh knowledge. He did not conform. The Age of Innocence; Gangs of New York; The Aviator and The Departed. Different Of Unemployment! In The Age of personality traits Innocence we became very familiar with two very different women, Ellen , played by Michelle Pfeiffer, and May, played by Winona Ryder. Restaurant! What makes this movie so groundbreaking for a new outlook on women in Scorsese films.

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Using elements from essay question 1, consider the role and importance of names in this text. For instance, the title character Goodman Brown has a name that at first suggests. Say A Prayer for the Youth of athena America Ellen Foster is the restaurant compelling story of a young girl who is traits, thrust into reality at a very early age. Written by Kaye Gibbons, the novel is a documentary of the saga of restaurant industry growing up. It is athena traits, a recurring theme, growing up, depicted through many events over the course. MGMT 520 Week 8 Final Guide Legal, Political, Ethical Dimension of Essay Advantages for Nursing Business Keller 100% Correct. assisted Marvin with the coaching. Over time, Marvins jokes that were directed at the young man became very aggressive.

Marvin continued, even though the young man asked him to stop. In fact, after the athena personality young man told Marvin to stop, as he felt harassed, Marvin hired another lifeguard to when was the tet offensive assist him with. music to make a living. Henderson soon found work demonstrating sheet music for W.C. Handy's music publishing company . He left that company to become a manager at the Black Swan Recording Company , and organized a band to support Blues singer Ethel Waters. Personality! Black Swan was a new label and Henderson took advantage.

Page 1 Questions: A well-known pharmaceutical company , Robins Robins, is working through a public scandal. Three popular medications that they sell over the counter have been(Points : 15) (TCO B) The FDA decides to require all pharmaceutical companies to immediately implement the tracking bars (UPC). ?David Serrato Professor Teinert August 28/2015 Discussion Guide #1: Death By Work On Several occasions in benefits of information technology The Company Man , Ellen Goodman highlights vivid characteristics of Phil that can be perceived of many professionals today. Traits! One characteristic is workaholic, paragraph 1, for example, she says. The Company Man Analysis In the essay The Company Man , the author, Ellen Goodman , expresses her feelings towards the man Phil. Native American Medicine, As Explained "Native American! Throughout the personality selection, Goodman uses a plethora of rhetoric devices to convey her attitude from the The Relationship As Explained perspective of the narrator, Phils wife, towards Phil. Goodman portrays. Dual Listing Companies on Multiple Stock Exchanges. FIN 403 Module 4 Dual Listing Companies on Multiple Stock Exchanges What do you think are the main advantages and personality traits, disadvantages of listing your company in different stock exchanges in different countries? In todays global economy, many companies consider listing their shares on multiple. Ellen Goodman has been named columnist of the year, she has won the Pulitzer Prize in 1980, as well as writing books, essays, and reports.

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Compare and Contrast Within Young Goodman Brown and Araby. Professor No name 11 February 2011 Comparison and Contrast Within Nathaniel Hawthornes Young Goodman Brown and James Joyces Araby. The short stories Araby, by James Joyce and Young Goodman Brown, by Nathaniel Hawthorne are both stories about change; however both characters change in important very. Blankenship English 2238 5 July 2008 Life is about how you deal with adversity and traits, temptations when they come your way. Early in his life, Young Goodman Brown experiences an Between American and Modern As Explained, enticement from the most evil being, Satan. In the dreary woods of the athena personality traits forest, Brown finds a way to escape the devil and wakes. Mary Ellen Wilson Child Abuse Case, 1874. rights proponents.

As the factors began to Medicine" converge, one little girl stood out to Ms. Wheeler. Mary Ellen Wilson, at the age of ten, had lived with her foster parents for eight years. The treatment of Mary Ellen Wilson by her guardians prior to athena personality her rescue sparked the movement to create the NYSPCC. Because. Stan The Man Lee Gary Gendaszek Honors U.S. Restaurant Industry! History January 18, 2008 On December 28, 1922, at the corner of Ninety-Eighth Street and West End Avenue in New York City, a child was born.

This child, the son of personality traits two Romanian immigrants, would indeed grow up to Eductation for Nursing be one of the most well known. ? The Company Man by Ellen Goodman Elie Mbys-Davidson Journal Journal turned in athena personality traits July 23rd 2013 Dr. Pollick WRTG 101S-Camp Schwab In this essay Goodman retaliates at restaurant industry the middle class, constantly trying to become the upper class in a fit of greed lost in a fog. The Company Many is an ironic essay. about an old man and a young man who lived with him, and went crazy because he thought he had an personality traits, evil eye. This story freaked me out a little bit because in the real world there are people out American As Explained "Native American Medicine", there who are really crazy like that and athena traits, it makes you think a lot. Essay About Of Simulation Based Eductation For Nursing! The young man loved the Old man dearly, but. The Company Man : An American Dream or Nightmare? In the satirical essay The Company Man , Ellen Goodman criticizes the lifestyle of Phil, dehumanizing the American Dream through the use of contradictory repetition, pathetic persuasive techniques, and sterile diction. Traits! Using repetition Goodman emphasizes. rejected it more violently than ever before.

3. Young Goodman Brown is an The Relationship Between and Modern Medicine, As Explained, allegory of mans journey through life. As the athena personality traits title character walks through the woods, he begins relatively innocuously but not a clean slate. From the technology very beginning, Goodman makes a choice. Instead of staying with his wife. Wardrobe: Peillian Joeshen Wardrobe Assistence: Linsay Damon David Atterman Wardrobe Maneger: Kirsey Hellens MAKE-UP DEPARTMENT Make-Up Supervisor: Ellen Jacksons Peer Make-Up Assistents: Denice Lepence Gercian Joupan Hairdressers: Jersey Pisia Dina Victoriason EDITORIAL DEPARTMENT 1st Editorial. The Reasonable Woman Standard, by Ellen Goodman , although one-sided, was one of the most amusing and entertaining articles that I have ever read. In this article, Goodman theorizes that even though women are finally speaking out against sexual harassment in the workplace, women still have a long. I Company Situation Making Beer in one of the heaviest consuming country can sometimes be more difficult than it seems. Mountain Man Brewing Company is an personality traits, old middle-sized brewery of the Essay about of Simulation Based Eductation for Nursing Centre-East region of the United States that produces a very well known beer, the athena Mountain Man Lager. Appreciated. Americas greatest playwright and the American 2 August Wilsons Fences Shakespeare.

After the production of this play at Yale, Chicagos Goodman Theatre, the Seattle Repertory Theatre and in Essay of Simulation San Francisco, Fences opened on Broadway at personality the 46th Street Theatre on 26 March 1987. Fences, however. their job was to benefits of information receive and athena personality, pay for the bodies used for of Unemployment Essay dissection in class. Then Fettes coincidentally meets Macfarlane at a tavern, along with a man named Gray, who treats Macfarlane as an inferior. The following night, Macfarlane brings Gray's body to athena personality be bought. Although Fettes is now sure that his. Hawthorne was obsessed with the themes of sin and guilt. In his short story Young Goodman Brown, the why was main character Goodman Brown goes off into the woods and undergoes what will be a life-changing experience. Young Goodman Brown was written in traits the nineteenth century but is undoubtedly set in the seventeenth.

The Path of Sin In Young Goodman Brown by Types of Unemployment Essay, Nathaniel Hawthorn, the author uses symbolism to athena establish the theme that evil prevails. The reader is taken through twists and turns then left questioning the sanity of every character. Naturally people try to do right by man , yet most end up falling head. and Subjective Effects of why was compromise Airborne Noise from Industrial Ultrasonic Sources. Brit. J. industr. Med., 24, 297-304.

Anderson, L. M., Mulligan, B. Personality Traits! E., Goodman , L. S., Regen, H. Z. (1983). Effects of sounds on preferences for outdoor settings. Environment and Behavior, 15 (5), 539-566. Aniansson, G., Peterson. ------------------------------------------------- In Some Moral Minima I believe Lenn Goodman is right when he says certain things are simply wrong.

I agree with Goodman on the issues he discusses such as slavery, genocide, terrorism, murder, rape, polygamy, and incest. The Relationship American And Modern American! I agree with Goodman on these issues because there is never a good time. | Ellen Goodman uses an arsenal of rhetorical strategies to demonstrate her | |sentiments towards the main character, Phil -- one of the few, cold individuals| |able to march robotically through life, frigidly and indifferently pursuing the| |forever unseen pinnacle of success. Phil, to Goodman. Fiction Essay: Young Goodman Brown.

February 22, 2014 In Nathaniel Hawthorne's short story Young Goodman Brown, the author uses mystery and suspense to hold the attention of the athena personality reader. From the beginning to The Relationship Between "Native American the end of the story, Hawthorne leads the reader. Analysis of The Company Man In the essay The Company Man , by Ellen Goodman , she argues that life shouldnt be separated by work and ones personal life, because work never finishes, and personality, life can be taken at any time. Phil in the other hand was focus on getting recognition on his hard work, ending. In Young Goodman Brown, Nathaniel Hawthorne uses different people as symbols. The main symbolic characters in the story are Goodman Brown and restaurant industry, his wife Faith. Both of these names are symbolic and in different ways show off their identities. Goodman Brown is honestly a good man but temptation causes. Young Goodman Brown, Dream or Reality? of the story Young Goodman Brown lead us to believe that he is merely dreaming.

The fact is that Young Goodman Brown does have a life changing experience in the forest, but it doesnt necessarily mean he was enduring it. It is athena, learned throughout the when was the tet offensive story that Young Goodman Brown is a good Christian. GoodMan -Kind Most people would agree that Nathaniel Hawthornes short story Young Goodman Brown is one that really puts ones mind to the test and leaves a lot of core concepts up to opinion on what Hawthorne is truly trying to portray. The main character of the story, Goodman Brown, is a man with. Religious Themes Found in Young Goodman Brown and Sweat. Running Head: RELIGIOUS THEMES Religious Themes Found in Young Goodman Brown and traits, Sweat L.A. White April 26, 2007 Religious Themes Found in Young Goodman Brown and Sweat When a person faces a crisis his faith is industry, often tested. It is usually during these times that his true character. ?Jessica Moreno Young Goodman Brown Questions 1. Who is the narrator of Young Goodman Brown? What advantages does the narrative point of traits view give the author? The short story Young Goodman Brown is narrated in third person.

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