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Next, let's combine regular expressions with conditional frequency distributions. Here we will extract all consonant-vowel sequences from the words of Rotokas, such as ka and si.

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Since each of these is a pair, it can be used to initialize a conditional frequency distribution. We then tabulate the frequency of each pair:. Examining the rows for s and t , we see they are in partial "complementary distribution", which is evidence that they are not distinct phonemes in the language. Thus, we could conceivably drop s from the Rotokas alphabet and simply have a pronunciation rule that the letter t is pronounced s when followed by i. Note that the single entry having su , namely kasuari , 'cassowary' is borrowed from English.

If we want to be able to inspect the words behind the numbers in the above table, it would be helpful to have an index, allowing us to quickly find the list of words that contains a given consonant-vowel pair, e. Here's how we can do this:. In the case of the word kasuari , it finds ka , su and ri.

One further step, using nltk. Index , converts this into a useful index. When we use a web search engine, we usually don't mind or even notice if the words in the document differ from our search terms in having different endings. A query for laptops finds documents containing laptop and vice versa. Indeed, laptop and laptops are just two forms of the same dictionary word or lemma. For some language processing tasks we want to ignore word endings, and just deal with word stems. There are various ways we can pull out the stem of a word.

Here's a simple-minded approach which just strips off anything that looks like a suffix:.

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Although we will ultimately use NLTK's built-in stemmers, it's interesting to see how we can use regular expressions for this task. Our first step is to build up a disjunction of all the suffixes. We need to enclose it in parentheses in order to limit the scope of the disjunction. Here, re. This is because the parentheses have a second function, to select substrings to be extracted. If we want to use the parentheses to specify the scope of the disjunction, but not to select the material to be output, we have to add?

Here's the revised version. However, we'd actually like to split the word into stem and suffix. So we should just parenthesize both parts of the regular expression:. This looks promising, but still has a problem. Let's look at a different word, processes :. The regular expression incorrectly found an -s suffix instead of an -es suffix. This demonstrates another subtlety: the star operator is "greedy" and the. This works even when we allow an empty suffix, by making the content of the second parentheses optional:.

This approach still has many problems can you spot them? Notice that our regular expression removed the s from ponds but also from is and basis. It produced some non-words like distribut and deriv , but these are acceptable stems in some applications. You can use a special kind of regular expression for searching across multiple words in a text where a text is a list of tokens. The angle brackets are used to mark token boundaries, and any whitespace between the angle brackets is ignored behaviors that are unique to NLTK's findall method for texts.

The second example finds three-word phrases ending with the word bro. The last example finds sequences of three or more words starting with the letter l. Your Turn: Consolidate your understanding of regular expression patterns and substitutions using nltk. For more practice, try some of the exercises on regular expressions at the end of this chapter. It is easy to build search patterns when the linguistic phenomenon we're studying is tied to particular words. In some cases, a little creativity will go a long way. For instance, searching a large text corpus for expressions of the form x and other ys allows us to discover hypernyms cf 5 :.

With enough text, this approach would give us a useful store of information about the taxonomy of objects, without the need for any manual labor. However, our search results will usually contain false positives, i.

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For example, the result: demands and other factors suggests that demand is an instance of the type factor , but this sentence is actually about wage demands. Nevertheless, we could construct our own ontology of English concepts by manually correcting the output of such searches. This combination of automatic and manual processing is the most common way for new corpora to be constructed. We will return to this in Searching corpora also suffers from the problem of false negatives, i. It is risky to conclude that some linguistic phenomenon doesn't exist in a corpus just because we couldn't find any instances of a search pattern.

Perhaps we just didn't think carefully enough about suitable patterns. Your Turn: Look for instances of the pattern as x as y to discover information about entities and their properties. In earlier program examples we have often converted text to lowercase before doing anything with its words, e. By using lower , we have normalized the text to lowercase so that the distinction between The and the is ignored. Often we want to go further than this, and strip off any affixes, a task known as stemming. A further step is to make sure that the resulting form is a known word in a dictionary, a task known as lemmatization.

We discuss each of these in turn. First, we need to define the data we will use in this section:. NLTK includes several off-the-shelf stemmers, and if you ever need a stemmer you should use one of these in preference to crafting your own using regular expressions, since these handle a wide range of irregular cases. The Porter and Lancaster stemmers follow their own rules for stripping affixes.

Observe that the Porter stemmer correctly handles the word lying mapping it to lie , while the Lancaster stemmer does not. Stemming is not a well-defined process, and we typically pick the stemmer that best suits the application we have in mind. The Porter Stemmer is a good choice if you are indexing some texts and want to support search using alternative forms of words illustrated in 3.

Example 3. The WordNet lemmatizer only removes affixes if the resulting word is in its dictionary. This additional checking process makes the lemmatizer slower than the above stemmers. Notice that it doesn't handle lying , but it converts women to woman. The WordNet lemmatizer is a good choice if you want to compile the vocabulary of some texts and want a list of valid lemmas or lexicon headwords.

Another normalization task involves identifying non-standard words including numbers, abbreviations, and dates, and mapping any such tokens to a special vocabulary. For example, every decimal number could be mapped to a single token 0. This keeps the vocabulary small and improves the accuracy of many language modeling tasks. Tokenization is the task of cutting a string into identifiable linguistic units that constitute a piece of language data.

Although it is a fundamental task, we have been able to delay it until now because many corpora are already tokenized, and because NLTK includes some tokenizers. Now that you are familiar with regular expressions, you can learn how to use them to tokenize text, and to have much more control over the process. The very simplest method for tokenizing text is to split on whitespace.

Consider the following text from Alice's Adventures in Wonderland :. We could split this raw text on whitespace using raw. Other whitespace characters, such as carriage-return and form-feed should really be included too. The above statement can be rewritten as re. Important: Remember to prefix regular expressions with the letter r meaning "raw" , which instructs the Python interpreter to treat the string literally, rather than processing any backslashed characters it contains.

Splitting on whitespace gives us tokens like ' not' and 'herself,'. Observe that this gives us empty strings at the start and the end to understand why, try doing 'xx'. We get the same tokens, but without the empty strings, with re.


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Now that we're matching the words, we're in a position to extend the regular expression to cover a wider range of cases. This means that punctuation is grouped with any following letters e. We need to include? We'll also add a pattern to match quote characters so these are kept separate from the text they enclose.

However, nltk. For readability we break up the regular expression over several lines and add a comment about each line. The special? When set to True , the regular expression specifies the gaps between tokens, as with re. We can evaluate a tokenizer by comparing the resulting tokens with a wordlist, and reporting any tokens that don't appear in the wordlist, using set tokens. You'll probably want to lowercase all the tokens first. Tokenization turns out to be a far more difficult task than you might have expected. No single solution works well across-the-board, and we must decide what counts as a token depending on the application domain.

When developing a tokenizer it helps to have access to raw text which has been manually tokenized, in order to compare the output of your tokenizer with high-quality or "gold-standard" tokens. A final issue for tokenization is the presence of contractions, such as didn't. If we are analyzing the meaning of a sentence, it would probably be more useful to normalize this form to two separate forms: did and n't or not. We can do this work with the help of a lookup table. This section discusses more advanced concepts, which you may prefer to skip on the first time through this chapter.

Tokenization is an instance of a more general problem of segmentation. In this section we will look at two other instances of this problem, which use radically different techniques to the ones we have seen so far in this chapter. Manipulating texts at the level of individual words often presupposes the ability to divide a text into individual sentences. As we have seen, some corpora already provide access at the sentence level. In the following example, we compute the average number of words per sentence in the Brown Corpus:.

In other cases, the text is only available as a stream of characters.

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Before tokenizing the text into words, we need to segment it into sentences. Here is an example of its use in segmenting the text of a novel. Note that if the segmenter's internal data has been updated by the time you read this, you will see different output :.

Notice that this example is really a single sentence, reporting the speech of Mr Lucian Gregory. However, the quoted speech contains several sentences, and these have been split into individual strings. This is reasonable behavior for most applications. Sentence segmentation is difficult because period is used to mark abbreviations, and some periods simultaneously mark an abbreviation and terminate a sentence, as often happens with acronyms like U. For another approach to sentence segmentation, see 2.

For some writing systems, tokenizing text is made more difficult by the fact that there is no visual representation of word boundaries. A similar problem arises in the processing of spoken language, where the hearer must segment a continuous speech stream into individual words. A particularly challenging version of this problem arises when we don't know the words in advance. This is the problem faced by a language learner, such as a child hearing utterances from a parent. Consider the following artificial example, where word boundaries have been removed:.

Our first challenge is simply to represent the problem: we need to find a way to separate text content from the segmentation. We can do this by annotating each character with a boolean value to indicate whether or not a word-break appears after the character an idea that will be used heavily for "chunking" in 7. Let's assume that the learner is given the utterance breaks, since these often correspond to extended pauses.

Here is a possible representation, including the initial and target segmentations:. Observe that the segmentation strings consist of zeros and ones. They are one character shorter than the source text, since a text of length n can only be broken up in n-1 places. The segment function in 3. Now the segmentation task becomes a search problem: find the bit string that causes the text string to be correctly segmented into words.

We assume the learner is acquiring words and storing them in an internal lexicon. Given a suitable lexicon, it is possible to reconstruct the source text as a sequence of lexical items. Following Brent, , we can define an objective function , a scoring function whose value we will try to optimize, based on the size of the lexicon number of characters in the words plus an extra delimiter character to mark the end of each word and the amount of information needed to reconstruct the source text from the lexicon. We illustrate this in 3.

It is a simple matter to implement this objective function, as shown in 3. The final step is to search for the pattern of zeros and ones that minimizes this objective function, shown in 3. Notice that the best segmentation includes "words" like thekitty , since there's not enough evidence in the data to split this any further. As this search algorithm is non-deterministic, you may see a slightly different result.

With enough data, it is possible to automatically segment text into words with a reasonable degree of accuracy. Such methods can be applied to tokenization for writing systems that don't have any visual representation of word boundaries. Often we write a program to report a single data item, such as a particular element in a corpus that meets some complicated criterion, or a single summary statistic such as a word-count or the performance of a tagger.

More often, we write a program to produce a structured result; for example, a tabulation of numbers or linguistic forms, or a reformatting of the original data. When the results to be presented are linguistic, textual output is usually the most natural choice. However, when the results are numerical, it may be preferable to produce graphical output. In this section you will learn about a variety of ways to present program output.

The simplest kind of structured object we use for text processing is lists of words. When we want to output these to a display or a file, we must convert these lists into strings. To do this in Python we use the join method, and specify the string to be used as the "glue". Many people find this notation for join counter-intuitive. The join method only works on a list of strings — what we have been calling a text — a complex type that enjoys some privileges in Python.

The print command yields Python's attempt to produce the most human-readable form of an object. The second method — naming the variable at a prompt — shows us a string that can be used to recreate this object. It is important to keep in mind that both of these are just strings, displayed for the benefit of you, the user. They do not give us any clue as to the actual internal representation of the object.

There are many other useful ways to display an object as a string of characters. This may be for the benefit of a human reader, or because we want to export our data to a particular file format for use in an external program. Formatted output typically contains a combination of variables and pre-specified strings, e. Print statements that contain alternating variables and constants can be difficult to read and maintain. Another solution is to use string formatting.

To understand what is going on here, let's test out the format string on its own. By now this will be your usual method of exploring new syntax. A string containing replacement fields is called a format string. We can have any number of placeholders, but the str. Arguments to format are consumed left to right, and any superfluous arguments are simply ignored.

The field name in a format string can start with a number, which refers to a positional argument of format. We can also provide the values for the placeholders indirectly. Here's an example using a for loop:. So far our format strings generated output of arbitrary width on the page or screen. We can add padding to obtain output of a given width by inserting into the brackets a colon ':' followed by an integer.

An important use of formatting strings is for tabulating data. Recall that in 1 we saw data being tabulated from a conditional frequency distribution. Let's perform the tabulation ourselves, exercising full control of headings and column widths, as shown in 3. Note the clear separation between the language processing work, and the tabulation of results. Recall from the listing in 3. This allows us to specify the width of a field using a variable.

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We have seen how to read text from files 3. It is often useful to write output to files as well. The following code opens a file output. When we write non-text data to a file we must convert it to a string first. We can do this conversion using formatting strings, as we saw above. Let's write the total number of words to our file:. You should avoid filenames that contain space characters like output file. When the output of our program is text-like, instead of tabular, it will usually be necessary to wrap it so that it can be displayed conveniently.

Consider the following output, which overflows its line, and which uses a complicated print statement:. We can take care of line wrapping with the help of Python's textwrap module. For maximum clarity we will separate each step onto its own line:. Notice that there is a linebreak between more and its following number.

If we wanted to avoid this, we could redefine the formatting string so that it contained no spaces, e. For example, this documentation covers "universal newline support," explaining how to work with the different newline conventions used by various operating systems. For more extensive discussion of text processing with Python see Mertz, For information about normalizing non-standard words see Sproat et al, There are many references for regular expressions, both practical and theoretical. For a comprehensive and detailed manual in using regular expressions, covering their syntax in most major programming languages, including Python, see Friedl, Other presentations include Section 2.

There are many online resources for Unicode. Useful discussions of Python's facilities for handling Unicode are:. Our method for segmenting English text follows Brent, ; this work falls in the area of language acquisition Niyogi, Collocations are a special case of multiword expressions.

3.2 Strings: Text Processing at the Lowest Level

A multiword expression is a small phrase whose meaning and other properties cannot be predicted from its words alone, e. Simulated annealing is a heuristic for finding a good approximation to the optimum value of a function in a large, discrete search space, based on an analogy with annealing in metallurgy.

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