Question: How Many Six letter words can be made from the English alphabet (without reusing any letters)
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What are some useful text corpora and lexical resources, and how can we access them with Python? Which Python constructs are most helpful for this work? How do we avoid repeating ourselves when writing Python code? This chapter continues to present programming concepts by example, in the context of a linguistic processing task. We will wait until later before exploring each Python construct systematically. 1 Accessing Text Corpora As just mentioned, a text corpus is a large body of text.
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Many corpora are designed to contain a careful balance of material in one or more genres. We examined some small text collections in 1. However, since we want to be able to work with other texts, this section examines a variety of text corpora. We’ll see how to select individual texts, and how to work with them. We begin by getting the Python interpreter to load the NLTK package, then ask to see nltk.
Now that you have started examining data from nltk. By contrast average sentence length and lexical diversity appear to be characteristics of particular authors. 2 Web and Chat Text Although Project Gutenberg contains thousands of books, it represents established literature. It is important to consider less formal language as well.
SCENE 1: KING ARTHUR: Whoa there! 3 Brown Corpus The Brown Corpus was the first million-word electronic corpus of English, created in 1961 at Brown University. This corpus contains text from 500 sources, and the sources have been categorized by genre, such as news, editorial, and so on. Let’s compare genres in their usage of modal verbs.
The first step is to produce the counts for a particular genre. Next, we need to obtain counts for each genre of interest. We’ll use NLTK’s support for conditional frequency distributions. These are presented systematically in 2, where we also unpick the following code line by line. For the moment, you can ignore the details and just concentrate on the output. The idea that word counts might distinguish genres will be taken up again in chap-data-intensive. 4 Reuters Corpus The Reuters Corpus contains 10,788 news documents totaling 1.
This split is for training and testing algorithms that automatically detect the topic of a document, as we will see in chap-data-intensive. Unlike the Brown Corpus, categories in the Reuters corpus overlap with each other, simply because a news story often covers multiple topics. We can ask for the topics covered by one or more documents, or for the documents included in one or more categories. For convenience, the corpus methods accept a single fileid or a list of fileids.
Similarly, we can specify the words or sentences we want in terms of files or categories. The first handful of words in each of these texts are the titles, which by convention are stored as upper case. 5 Inaugural Address Corpus In 1, we looked at the Inaugural Address Corpus, but treated it as a single text. Notice that the year of each text appears in its filename. The following code converts the words in the Inaugural corpus to lowercase using w. 6 Annotated Text Corpora Many text corpora contain linguistic annotations, representing POS tags, named entities, syntactic structures, semantic roles, and so forth. NLTK provides convenient ways to access several of these corpora, and has data packages containing corpora and corpus samples, freely downloadable for use in teaching and research.
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2 lists some of the corpora. Some of the Corpora and Corpus Samples Distributed with NLTK: For information about downloading and using them, please consult the NLTK website. Universal Declaration of Human Rights in over 300 languages. The output is shown in 1. Your Turn: Pick a language of interest in udhr. Now plot a frequency distribution of the letters of the text using nltk.
Unfortunately, for many languages, substantial corpora are not yet available. Often there is insufficient government or industrial support for developing language resources, and individual efforts are piecemeal and hard to discover or re-use. Some languages have no established writing system, or are endangered. See 7 for suggestions on how to locate language resources. The simplest kind lacks any structure: it is just a collection of texts. Often, texts are grouped into categories that might correspond to genre, source, author, language, etc. NLTK’s corpus readers support efficient access to a variety of corpora, and can be used to work with new corpora.
3 lists functionality provided by the corpus readers. 2 Conditional Frequency Distributions We introduced frequency distributions in 3. Here we will generalize this idea. When the texts of a corpus are divided into several categories, by genre, topic, author, etc, we can maintain separate frequency distributions for each category. This will allow us to study systematic differences between the categories.
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The condition will often be the category of the text. 1 depicts a fragment of a conditional frequency distribution having just two conditions, one for news text and one for romance text. 1 Conditions and Events A frequency distribution counts observable events, such as the appearance of words in a text. A conditional frequency distribution needs to pair each event with a condition. 2 Counting Words by Genre In 1 we saw a conditional frequency distribution where the condition was the section of the Brown Corpus, and for each condition we counted words.
Let’s break this down, and look at just two genres, news and romance. 1 was based on a conditional frequency distribution reproduced in the code below. It exploits the fact that the filename for each speech, e. 2 was also based on a conditional frequency distribution, reproduced below.
When we omit it, we get all the conditions. This makes it possible to load a large quantity of data into a conditional frequency distribution, and then to explore it by plotting or tabulating selected conditions and samples. It also gives us full control over the order of conditions and samples in any displays. For example, we can tabulate the cumulative frequency data just for two languages, and for words less than 10 characters long, as shown below.
Your Turn: Working with the news and romance genres from the Brown Corpus, find out which days of the week are most newsworthy, and which are most romantic. Now tabulate the counts for these words using cfd. You may have noticed that the multi-line expressions we have been using with conditional frequency distributions look like list comprehensions, but without the brackets. See the discussion of “generator expressions” in 4. 2, we treat each word as a condition, and for each one we effectively create a frequency distribution over the following words. 7, ‘thing’: 4, ‘substance’: 2, ‘,’: 1, ‘.
Conditional frequency distributions are a useful data structure for many NLP tasks. Their commonly-used methods are summarized in 2. NLTK’s Conditional Frequency Distributions: commonly-used methods and idioms for defining, accessing, and visualizing a conditional frequency distribution of counters. 3 More Python: Reusing Code By this time you’ve probably typed and retyped a lot of code in the Python interactive interpreter.
If you mess up when retyping a complex example you have to enter it again. Using the arrow keys to access and modify previous commands is helpful but only goes so far. In this section we see two important ways to reuse code: text editors and Python functions. 1 Creating Programs with a Text Editor The Python interactive interpreter performs your instructions as soon as you type them. Often, it is better to compose a multi-line program using a text editor, then ask Python to run the whole program at once.
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Save this program in a file called monty. We’ll learn what modules are shortly. From now on, you have a choice of using the interactive interpreter or a text editor to create your programs. It is often convenient to test your ideas using the interpreter, revising a line of code until it does what you expect.
Give the file a short but descriptive name, using all lowercase letters and separating words with underscore, and using the . As they get more complicated, you should instead type them into the editor, without the prompts, and run them from the editor as shown above. When we provide longer programs in this book, we will leave out the prompts to remind you to type them into a file rather than using the interpreter. You can see this already in 2. 2 Functions Suppose that you work on analyzing text that involves different forms of the same word, and that part of your program needs to work out the plural form of a given singular noun. Suppose it needs to do this work in two places, once when it is processing some texts, and again when it is processing user input.
A function is just a named block of code that performs some well-defined task, as we saw in 1. Here’s an equivalent definition which does the same work using multiple lines of code. Notice that we’ve created some new variables inside the body of the function. But just defining it won’t produce any output! Let’s return to our earlier scenario, and actually define a simple function to work out English plurals.
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1 takes a singular noun and generates a plural form, though it is not always correct. We’ll discuss functions at greater length in 4. To call such functions, we give the name of the object, a period, and then the name of the function. 3 Modules Over time you will find that you create a variety of useful little text processing functions, and you end up copying them from old programs to new ones. Which file contains the latest version of the function you want to use? It makes life a lot easier if you can collect your work into a single place, and access previously defined functions without making copies. Instead of typing in a new version of the function, we can simply edit the existing one.
Thus, at every stage, there is only one version of our plural function, and no confusion about which one is being used. NLTK’s code for processing the Brown Corpus is an example of a module, and its collection of code for processing all the different corpora is an example of a package. If you are creating a file to contain some of your Python code, do not name your file nltk. Lexical resources are secondary to texts, and are usually created and enriched with the help of texts. Similarly, a concordance like the one we saw in 1 gives us information about word usage that might help in the preparation of a dictionary. Standard terminology for lexicons is illustrated in 4.
The simplest kind of lexicon is nothing more than a sorted list of words. Sophisticated lexicons include complex structure within and across the individual entries. In this section we’ll look at some lexical resources included with NLTK. 1 Wordlist Corpora NLTK includes some corpora that are nothing more than wordlists. Unix, used by some spell checkers.
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We can use it to find unusual or mis-spelt words in a text corpus, as shown in 4. Filtering a Text: this program computes the vocabulary of a text, then removes all items that occur in an existing wordlist, leaving just the uncommon or mis-spelt words. Stopwords usually have little lexical content, and their presence in a text fails to distinguish it from other texts. Thus, with the help of stopwords we filter out over a quarter of the words of the text. Notice that we’ve combined two different kinds of corpus here, using a lexical resource to filter the content of a text corpus.
A wordlist is useful for solving word puzzles, such as the one in 4. Our program iterates through every word and, for each one, checks whether it meets the conditions. One more wordlist corpus is the Names corpus, containing 8,000 first names categorized by gender. The male and female names are stored in separate files. Let’s find names which appear in both files, i.
We can see this and some other patterns in the graph in 4. 4, produced by the following code. NLTK includes the CMU Pronouncing Dictionary for US English, which was designed for use by speech synthesizers. You could use this method to find rhyming words. Let’s look for some other mismatches between pronunciation and writing.
Can you summarize the purpose of the following examples and explain how they work? As our final example, we define a function to extract the stress digits and then scan our lexicon to find words having a particular stress pattern. There’s a lot going on here and you might want to return to this once you’ve had more experience using list comprehensions. We can use a conditional frequency distribution to help us find minimally-contrasting sets of words. Rather than iterating over the whole dictionary, we can also access it by looking up particular words. We will use Python’s dictionary data structure, which we will study systematically in 3. We can use any lexical resource to process a text, e.
For example, the following text-to-speech function looks up each word of the text in the pronunciation dictionary. 200 common words in several languages. The languages are identified using an ISO 639 two-letter code. We can make our simple translator more useful by adding other source languages.
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4 Shoebox and Toolbox Lexicons Perhaps the single most popular tool used by linguists for managing data is Toolbox, previously known as Shoebox since it replaces the field linguist’s traditional shoebox full of file cards. A Toolbox file consists of a collection of entries, where each entry is made up of one or more fields. Most fields are optional or repeatable, which means that this kind of lexical resource cannot be treated as a table or spreadsheet. Here is a dictionary for the Rotokas language. The last three pairs contain an example sentence in Rotokas and its translations into Tok Pisin and English. The loose structure of Toolbox files makes it hard for us to do much more with them at this stage. XML provides a powerful way to process this kind of corpus and we will return to this topic in 11.
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The Rotokas language is spoken on the island of Bougainville, Papua New Guinea. This lexicon was contributed to NLTK by Stuart Robinson. English, similar to a traditional thesaurus but with a richer structure. Benz is credited with the invention of the motorcar. Benz is credited with the invention of the automobile. Each word of a synset can have several meanings, e. However, we are only interested in the single meaning that is common to all words of the above synset.
To eliminate ambiguity, we will identify these words as car. This pairing of a synset with a word is called a lemma. These concepts are linked together in a hierarchy. Others, such as gas guzzler and hatchback, are much more specific.
A small portion of a concept hierarchy is illustrated in 5. We can also navigate up the hierarchy by visiting hypernyms. Some words have multiple paths, because they can be classified in more than one way. There are two paths between car.