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  1. 1.2 Getting Started with NLTK
  2. Frequency of Use and the Organization of Language - Joan Bybee - Google книги
  3. Numéros en texte intégral
  4. Vocabulary Goal Setting and How to Select Word Size

Paul Nation, Those 10 words make up 0. Shocking, no? But still, the idea that words — now just 0. How far will this take you? The first fruit I could find on the English frequency list was word Where do the benefits stop? This is a hard question to answer. Sources: Tom Cobb , Paul Nation. Fortunately, the vocabulary of a language is highly dependent upon its context. This text is much more readable and you might be able to guess the meanings of the missing words cubic, annually, additional without a dictionary. Take the first most frequent words in your language as a foundation, and then start customizing.

Skim through a vocabulary book and check off any words you expect to need, based on your own career, hobbies and interests. Start there, then move on to your frequency list. Choosing your Vocabulary Vocabulary Size Now you have an incredibly efficient way to memorize words , you know how to teach yourself each word, and you know what each word sounds like.

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 a text that are most informative about the topic and genre of the 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. 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. The table in 3. In general, it could count any kind of observable event. It is a "distribution" because it tells us how the total number of word tokens in the text are distributed across the 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 argument. We can inspect the total number of words "outcomes" that have been counted up — , in the case of Moby Dick. Your Turn: Try the preceding frequency distribution example for yourself, for text2. Be careful to use the 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. 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 times. The rest of the words tell us nothing about the text; they're just English "plumbing. We can generate a cumulative frequency plot for these words, using fdist1. These 50 words account for nearly half the book! If the frequent words don't help us, how about the words that occur once only, the so-called hapaxes? View them by typing fdist1. This list contains lexicographer , cetological , contraband , expostulations , and about 9, 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 any case!

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Since neither frequent nor infrequent words help, we need to try something else. Next, let's look at the long words of a text; perhaps these will be more characteristic and 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 words of interest using mathematical set notation as shown in 1a. This means "the set of all w such that w is an element of 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. Let's go one more step and write executable Python code:. For each word w in the vocabulary V , we check whether len w is 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 text and changing the length condition. Does it make a difference to your results if you change the variable names, e. 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.

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  • 1.1 Getting Started with Python!

Have we succeeded in automatically extracting words that typify a text? Well, these very long words are often hapaxes i.

This seems promising since it eliminates frequent short words e. Here are all words from the chat corpus that are longer than seven characters, that occur more than seven times:. At last we have managed to automatically identify the 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.

1.2 Getting Started with NLTK

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 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' , This is 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 tell Python to convert it into a list, using list.


Frequency of Use and the Organization of Language - Joan Bybee - Google книги

Here we see that the pair of 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 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 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 lengths of words in text1 , and the FreqDist then counts the number of times each of these occurs.

The result is 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. One might wonder how frequent the different lengths of word are e. We can do this as follows:.

Numéros en texte intégral

Although we will not pursue it here, further analysis of word length might help us understand differences between authors, genres, or languages. Table 3. Our discussion of frequency distributions has introduced some important Python concepts, and we will look at them systematically in 4. 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 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 known as control , and is the focus of this section. The full set of these relational operators is shown in 4. We can use these to select different words from a sentence of news text. Here are some examples — only the operator is changed from one line to the next.

Vocabulary Goal Setting and How to Select Word Size

They all use sent7 , the first sentence from text7 Wall Street Journal. As before, if you get an error saying that sent7 is undefined, you need to first type: from nltk. 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 the previous code example, the condition is always a numerical comparison.