1. 0 Search the Web for "spoof newspaper headlines," to find such gems as: British Left Waffles on Falkland Islands, and Juvenile Court to Try Shooting Defendant. Manually tag these headlines to see whether knowledge of the part-of-speech tags removes the ambiguity.
2. o Working with someone else, take turns picking a word that can be either a noun or a verb (e.g., contest); the opponent has to predict which one is likely to be the most frequent in the Brown Corpus. Check the opponent's prediction, and tally the score over several turns.
3. o Tokenize and tag the following sentence: They wind back the clock, while we chase after the wind. What different pronunciations and parts-of-speech are involved?
4. o Review the mappings in Table 5-4. Discuss any other examples of mappings you can think of. What type of information do they map from and to?
5. o Using the Python interpreter in interactive mode, experiment with the dictionary examples in this chapter. Create a dictionary d, and add some entries. What happens whether you try to access a non-existent entry, e.g., d['xyz']?
6. o Try deleting an element from a dictionary d, using the syntax del d[' abc' ]. Check that the item was deleted.
7. o Create two dictionaries, d1 and d2, and add some entries to each. Now issue the command d1.update(d2). What did this do? What might it be useful for?
8. o Create a dictionary e, to represent a single lexical entry for some word of your choice. Define keys such as headword, part-of-speech, sense, and example, and assign them suitable values.
9. o Satisfy yourself that there are restrictions on the distribution of go and went, in the sense that they cannot be freely interchanged in the kinds of contexts illustrated in (3), Section 5.7.
10. o Train a unigram tagger and run it on some new text. Observe that some words are not assigned a tag. Why not?
11. o Learn about the affix tagger (type help(nltk.AffixTagger)). Train an affix tagger and run it on some new text. Experiment with different settings for the affix length and the minimum word length. Discuss your findings.
12. o Train a bigram tagger with no backoff tagger, and run it on some of the training data. Next, run it on some new data. What happens to the performance of the tagger? Why?
13. o We can use a dictionary to specify the values to be substituted into a formatting string. Read Python's library documentation for formatting strings (http://docs.py
thon.org/lib/typesseq-strings.html) and use this method to display today's date in two different formats.
14. ® Use sorted() and set() to get a sorted list of tags used in the Brown Corpus, removing duplicates.
15. ® Write programs to process the Brown Corpus and find answers to the following questions:
a. Which nouns are more common in their plural form, rather than their singular form? (Only consider regular plurals, formed with the -s suffix.)
b. Which word has the greatest number of distinct tags? What are they, and what do they represent?
c. List tags in order of decreasing frequency. What do the 20 most frequent tags represent?
d. Which tags are nouns most commonly found after? What do these tags represent?
16. ® Explore the following issues that arise in connection with the lookup tagger:
a. What happens to the tagger performance for the various model sizes when a backoff tagger is omitted?
b. Consider the curve in Figure 5-4; suggest a good size for a lookup tagger that balances memory and performance. Can you come up with scenarios where it would be preferable to minimize memory usage, or to maximize performance with no regard for memory usage?
17. ® What is the upper limit of performance for a lookup tagger, assuming no limit to the size of its table? (Hint: write a program to work out what percentage of tokens of a word are assigned the most likely tag for that word, on average.)
18. ® Generate some statistics for tagged data to answer the following questions:
a. What proportion of word types are always assigned the same part-of-speech tag?
b. How many words are ambiguous, in the sense that they appear with at least two tags?
c. What percentage of word tokens in the Brown Corpus involve these ambiguous words?
19. ® The evaluate() method works out how accurately the tagger performs on this text. For example, if the supplied tagged text was [('the', 'DT'), ('dog', 'NN')] and the tagger produced the output [('the', 'NN'), ('dog', 'NN')], then the score would be 0.5. Let's try to figure out how the evaluation method works:
a. A tagger t takes a list of words as input, and produces a list of tagged words as output. However, t.evaluate() is given correctly tagged text as its only parameter. What must it do with this input before performing the tagging?
b. Once the tagger has created newly tagged text, how might the evaluate() method go about comparing it with the original tagged text and computing the accuracy score?
c. Now examine the source code to see how the method is implemented. Inspect nltk.tag.api._file__to discover the location of the source code, and open this file using an editor (be sure to use the api.py file and not the compiled api.pyc binary file).
20. ® Write code to search the Brown Corpus for particular words and phrases according to tags, to answer the following questions:
a. Produce an alphabetically sorted list of the distinct words tagged as MD.
b. Identify words that can be plural nouns or third person singular verbs (e.g., deals, flies).
c. Identify three-word prepositional phrases of the form IN + DET + NN (e.g., in the lab).
d. What is the ratio of masculine to feminine pronouns?
21. ® In Table 3-1, we saw a table involving frequency counts for the verbs adore, love, like, and prefer, and preceding qualifiers such as really. Investigate the full range of qualifiers (Brown tag QL) that appear before these four verbs.
22. ® We defined the regexp_tagger that can be used as a fall-back tagger for unknown words. This tagger only checks for cardinal numbers. By testing for particular prefix or suffix strings, it should be possible to guess other tags. For example, we could tag any word that ends with -s as a plural noun. Define a regular expression tagger (using RegexpTagger()) that tests for at least five other patterns in the spelling of words. (Use inline documentation to explain the rules.)
23. ® Consider the regular expression tagger developed in the exercises in the previous section. Evaluate the tagger using its accuracy() method, and try to come up with ways to improve its performance. Discuss your findings. How does objective evaluation help in the development process?
24. ® How serious is the sparse data problem? Investigate the performance of n-gram taggers as n increases from 1 to 6. Tabulate the accuracy score. Estimate the training data required for these taggers, assuming a vocabulary size of 105 and a tagset size of 102.
25. ® Obtain some tagged data for another language, and train and evaluate a variety of taggers on it. If the language is morphologically complex, or if there are any orthographic clues (e.g., capitalization) to word classes, consider developing a regular expression tagger for it (ordered after the unigram tagger, and before the default tagger). How does the accuracy of your tagger(s) compare with the same taggers run on English data? Discuss any issues you encounter in applying these methods to the language.
26. ® Example 5-4 plotted a curve showing change in the performance of a lookup tagger as the model size was increased. Plot the performance curve for a unigram tagger, as the amount of training data is varied.
27. ® Inspect the confusion matrix for the bigram tagger t2 defined in Section 5.5, and identify one or more sets of tags to collapse. Define a dictionary to do the mapping, and evaluate the tagger on the simplified data.
28. ® Experiment with taggers using the simplified tagset (or make one of your own by discarding all but the first character of each tag name). Such a tagger has fewer distinctions to make, but much less information on which to base its work. Discuss your findings.
29. ® Recall the example of a bigram tagger which encountered a word it hadn't seen during training, and tagged the rest of the sentence as None. It is possible for a bigram tagger to fail partway through a sentence even if it contains no unseen words (even if the sentence was used during training). In what circumstance can this happen? Can you write a program to find some examples of this?
30. ® Preprocess the Brown News data by replacing low-frequency words with UNK, but leaving the tags untouched. Now train and evaluate a bigram tagger on this data. How much does this help? What is the contribution of the unigram tagger and default tagger now?
31. ® Modify the program in Example 5-4 to use a logarithmic scale on the x-axis, by replacing pylab.plot() with pylab.semilogx(). What do you notice about the shape of the resulting plot? Does the gradient tell you anything?
32. ® Consult the documentation for the Brill tagger demo function, using help(nltk.tag.brill.demo). Experiment with the tagger by setting different values for the parameters. Is there any trade-off between training time (corpus size) and performance?
33. ® Write code that builds a dictionary of dictionaries of sets. Use it to store the set of POS tags that can follow a given word having a given POS tag, i.e., wordi ^ tagi ^ tagi+1-
34. • There are 264 distinct words in the Brown Corpus having exactly three possible tags.
a. Print a table with the integers 1..10 in one column, and the number of distinct words in the corpus having 1..10 distinct tags in the other column.
b. For the word with the greatest number of distinct tags, print out sentences from the corpus containing the word, one for each possible tag.
35. • Write a program to classify contexts involving the word must according to the tag of the following word. Can this be used to discriminate between the epistemic and deontic uses of must?
36. • Create a regular expression tagger and various unigram and n-gram taggers, incorporating backoff, and train them on part of the Brown Corpus.
a. Create three different combinations of the taggers. Test the accuracy of each combined tagger. Which combination works best?
b. Try varying the size of the training corpus. How does it affect your results?
37. • Our approach for tagging an unknown word has been to consider the letters of the word (using RegexpTagger()), or to ignore the word altogether and tag it as a noun (using nltk.DefaultTagger()). These methods will not do well for texts having new words that are not nouns. Consider the sentence I like to blog on Kim's blog. If blog is a new word, then looking at the previous tag (TO versus NP$) would probably be helpful, i.e., we need a default tagger that is sensitive to the preceding tag.
a. Create a new kind of unigram tagger that looks at the tag of the previous word, and ignores the current word. (The best way to do this is to modify the source code for UnigramTagger(), which presumes knowledge of object-oriented programming in Python.)
b. Add this tagger to the sequence of backoff taggers (including ordinary trigram and bigram taggers that look at words), right before the usual default tagger.
c. Evaluate the contribution of this new unigram tagger.
38. • Consider the code in Section 5.5, which determines the upper bound for accuracy of a trigram tagger. Review Abney's discussion concerning the impossibility of exact tagging (Abney, 2006). Explain why correct tagging of these examples requires access to other kinds of information than just words and tags. How might you estimate the scale of this problem?
39. • Use some of the estimation techniques in nltk.probability, such as Lidstone or Laplace estimation, to develop a statistical tagger that does a better job than n-gram backoff taggers in cases where contexts encountered during testing were not seen during training.
40. • Inspect the diagnostic files created by the Brill tagger rules.out and errors.out. Obtain the demonstration code by accessing the source code (at http: //www.nltk.org/code) and create your own version of the Brill tagger. Delete some of the rule templates, based on what you learned from inspecting rules.out. Add some new rule templates which employ contexts that might help to correct the errors you saw in errors.out.
41. • Develop an n-gram backoff tagger that permits "anti-n-grams" such as ["the", "the"] to be specified when a tagger is initialized. An anti-n-gram is assigned a count of zero and is used to prevent backoff for this n-gram (e.g., to avoid estimating P(the | the) as just P(the)).
42. • Investigate three different ways to define the split between training and testing data when developing a tagger using the Brown Corpus: genre (category), source (fileid), and sentence. Compare their relative performance and discuss which method is the most legitimate. (You might use n-fold cross validation, discussed in Section 6.3, to improve the accuracy of the evaluations.)
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