1. Classifying text NLTK Chapter 6

2. Chapter 6 topics How can we identify particular features of language data that are salient for classifying it? How can we construct models of language that can be used to perform language processing tasks automatically? What can we learn about language from these models?

3. From words to larger units We looked at how words are indentified with a part of speech. That is an essential part of “understanding” textual material Now, how can we classify whole documents. – These techniques are used for spam detection, for identifying the subject matter of a news feed, and for many other tasks related to categorizing text

4. A supervised classifier We saw a smaller version of this in our part of speech taggers

5. Case study Male and female names Note this is language biased (English) These distinctions are harder given modern naming conventions – I have a granddaughter named Sydney, for example

6. Step 1: features and encoding Deciding what features to look for and how to represent those features is the first step, and is critical. – All the training and classification will be based on these decisions Initial choice for name identification: look at the last letter: >>> def gender_features(word):... return {'last_letter': word[-1]} >>> gender_features('Shrek') {'last_letter': 'k'} returns a dictionary (note the { } ) with a feature name and the corresponding value

7. First gender check import nltk def gender_features(word): return {'last_letter':word[-1]} name=raw_input("What name shall we check?") features=gender_features(name) print "Gender features for ", name, ":", features

8. Step 2: Provide training values We provide a list of examples and their corresponding feature values. >>> from nltk.corpus import names >>> import random >>> names = ([(name,'male') for name in names.words('male.txt')] +... [(name, 'female') for name in names.words('female.txt')]) >>> random.shuffle(names) >>> names [('Kate', 'female'), ('Eleonora', 'female'), ('Germaine', 'male'), ('Helen', 'female'), ('Rachelle', 'female'), ('Nanci', 'female'), ('Aleta', 'female'), ('Catherin', 'female'), ('Clementia', 'female'), ('Keslie', 'female'), ('Callida', 'female'), ('Horatius', 'male'), ('Kraig', 'male'), ('Cindra', 'female'), ('Jayne', 'female'), ('Fortuna', 'female'), ('Yovonnda', 'female'), ('Pam', 'female'), ('Vida', 'female'), ('Margurite', 'female'), ('Maryellen', 'female'), …

9. Try it. Apply the classifier to your name: Try it on the test data and see how it does: >>> featuresets = [(gender_features(n), g) for (n,g) in names] >>> train_set, test_set = featuresets[500:], featuresets[:500] >>> classifier = nltk.NaiveBayesClassifier.train(train_set) >>> classifier.classify(gender_features('Sydney')) 'female' >>> print nltk.classify.accuracy(classifier, test_set) 0.758

10. Your turn Modify the gender_features function to look at more of the name than the last letter. Does it help to look at the last two letters? the first letter? the length of the name? Try a few variations

11. What is most useful There is even a function to show what was most useful in the classification: >>> classifier.show_most_informative_features(10) Most Informative Features last_letter = 'k' male : female = 45.7 : 1.0 last_letter = 'a' female : male = 38.4 : 1.0 last_letter = 'f' male : female = 28.7 : 1.0 last_letter = 'v' male : female = 11.2 : 1.0 last_letter = 'p' male : female = 11.2 : 1.0 last_letter = 'd' male : female = 9.8 : 1.0 last_letter = 'm' male : female = 8.9 : 1.0 last_letter = 'o' male : female = 8.3 : 1.0 last_letter = 'r' male : female = 6.7 : 1.0 last_letter = 'g' male : female = 5.6 : 1.0

12. What features to use Overfitting – Being too specific about the characteristics that you search for – Picks up idiosyncrasies of the training data and may not transfer well to the test data Choose an initial feature set and then test. The chair example. What features would you use?

13. Dev test Divide the corpus into three parts: training, development testing, final testing

14. Testing stages >>> train_set = [(gender_features(n), g) for (n,g) in train_names] >>> devtest_set = [(gender_features(n), g) for (n,g) in devtest_names] >>> test_set = [(gender_features(n), g) for (n,g) in test_names] >>> classifier = nltk.NaiveBayesClassifier.train(train_set) >>> print nltk.classify.accuracy(classifier, devtest_set) 0.765 >>> train_names = names[1500:] >>> devtest_names = names[500:1500] >>> test_names = names[:500] Accuracy noted, but where were the problems? From 1500 to end First 500 items

15. import nltk from nltk.corpus import names import random def gender_features(word): return {'last_letter':word[-1]} names = ([(name, 'male') for name in names.words('male.txt')] + \ [(name, 'female') for name in names.words('female.txt')]) random.shuffle(names) print "Number of names: ", len(names) train_names=names[1500:] devtest_names=names[500:1500] test_names = names[:500] train_set=[(gender_features(n),g) for (n,g) in train_names] devtest_set=[(gender_features(n),g) for (n,g) in devtest_names] test_set = [(gender_features(n),g) for (n,g) in test_names] classifier = nltk.NaiveBayesClassifier.train(train_set) print nltk.classify.accuracy(classifier,devtest_set) print classifier.show_most_informative_features(10)

16. Output from previous code Number of names: 7944 0.771 Most Informative Features last_letter = 'k' male : female = 39.7 : 1.0 last_letter = 'a' female : male = 31.4 : 1.0 last_letter = 'f' male : female = 16.0 : 1.0 last_letter = 'v' male : female = 14.1 : 1.0 last_letter = 'd' male : female = 10.3 : 1.0 last_letter = 'p' male : female = 9.8 : 1.0 last_letter = 'm' male : female = 8.6 : 1.0 last_letter = 'o' male : female = 7.8 : 1.0 last_letter = 'r' male : female = 6.6 : 1.0 last_letter = 'w' male : female = 4.8 : 1.0

17. Checking where the errors are Next slide

18. import nltk from nltk.corpus import names import random def gender_features(word): return {'last_letter':word[-1]} names = ([(name, 'male') for name in names.words('male.txt')] + \ [(name, 'female') for name in names.words('female.txt')]) random.shuffle(names) print "Number of names: ", len(names) train_names=names[1500:] devtest_names=names[500:1500] test_names = names[:500] train_set=[(gender_features(n),g) for (n,g) in train_names] devtest_set=[(gender_features(n),g) for (n,g) in devtest_names] test_set = [(gender_features(n),g) for (n,g) in test_names] classifier = nltk.NaiveBayesClassifier.train(train_set) print "Look for error cases:” errors = [] for (name,tag) in devtest_names: guess = classifier.classify(gender_features(name)) if guess != tag: errors.append((tag, guess, name)) for (tag, guess, name) in sorted(errors): print 'correct= %-8s guess= %-8s name =%-30s'%(tag,guess,name) print "Number of errors: ", len(errors) print nltk.classify.accuracy(classifier,devtest_set)

19. Check the classifier against the known values and see where it failed: Number of names: 7944 Look for error cases: correct= female guess= male name =Abagail correct= female guess= male name =Adrian correct= female guess= male name =Alex correct= female guess= male name =Amargo correct= female guess= male name =Anabel correct= female guess= male name =Annabal correct= female guess= male name =Annabel correct= female guess= male name =Arabel correct= female guess= male name =Ardelis …

20. Finding the error cases Look through the list of error cases. Do you see any patterns? Are there adjustments that we could make in our feature extractor to make it more accurate?

21. Error analysis It turns out that using the last two letters improves the accuracy. Did you find that in your experimentation?

22. Summarize the process Train on a subset of the available data – Look for characteristics that relate to the “right” answer. Write the feature extractor to look at those characteristics Run the classifier on other data – whose characteristics are known! – to see how well it performs – You have to know the answers to know whether the classifier got them right. When satisfied with the performance of the classifier, run it on new data for which you do not know the answer. – How confident can you be? The disease example. If 98% of your cases are disease free …

23. Document classification So far, classified names as Male/Female – Not much to work with, not much to look at Now, look at whole documents – How can you classify a document? – Subject matter in a syllabus collection, positive and negative movie/restaurant/other reviews, bias in a summary or review, subject matter in a news feed, separate works by author, … Case study, classifying movie reviews

24. Classifying documents To classify words (names), we looked at letters. Feature extraction for documents will use words Find the most common words in the document set and see which words are in which types of documents

25. import nltk import random from nltk.corpus import movie_reviews documents = [(list(movie_reviews.words(fileid)), \ category) for category in movie_reviews.categories() for fileid in movie_reviews.fileids(category)] random.shuffle(documents) cats = list(cat for cat in \ movie_reviews.categories()) print "Movie review Categories:", cats print "Number of reviews:", len(documents)

26. Feature extractor. Are the words present in the documents import nltk import random from nltk.corpus import movie_reviews documents = [(list(movie_reviews.words(fileid)), category) for category in movie_reviews.categories() for fileid in movie_reviews.fileids(category)] random.shuffle(documents) all_words= nltk.FreqDist(w.lower() for w in \ movie_reviews.words()) word_features = all_words.keys()[:2000] def document_features(document): document_words = set(document) features = {} for word in word_features: features['contains(%s)'% word] = (word in document_words) return features print document_features(movie_reviews.words('pos/cv957_8737.txt')) Line by line, what does this do? This is something different, but we have seen its like before What is this?

27. And if you are not sure … What do you do? – Enter the code and run it – Go to a search engine and type “Python ”

28. Compute accuracy and see what are the most useful feature values featuresets = [(document_features(d), c) for (d,c) in documents] train_set, test_set = featuresets[100:], featuresets[:100] classifier = nltk.NaiveBayesClassifier.train(train_set) 0.81 Most Informative Features contains(outstanding) = True pos : neg = 11.1 : 1.0 contains(seagal) = True neg : pos = 8.3 : 1.0 contains(mulan) = True pos : neg = 8.3 : 1.0 contains(damon) = True pos : neg = 8.1 : 1.0 contains(wonderfully) = True pos : neg = 6.8 : 1.0 Just as we did with classifying names Create a feature set Create a training set and a testing set Apply to new data

29. import nltk import random from nltk.corpus import movie_reviews documents = [(list(movie_reviews.words(fileid)), category) for category in movie_reviews.categories() for fileid in movie_reviews.fileids(category)] random.shuffle(documents) all_words= nltk.FreqDist(w.lower() for w in movie_reviews.words()) word_features = all_words.keys()[:2000] def document_features(document): document_words = set(document) features = {} for word in word_features: features['contains(%s)'% word] = (word in document_words) return features featuresets = [(document_features(d), c) for (d,c) in documents] train_set, test_set = featuresets[100:], featuresets[:100] classifier = nltk.NaiveBayesClassifier.train(train_set) print nltk.classify.accuracy(classifier, test_set) print classifier.show_most_informative_features(5) Full code for this example

30. From the text This note from the text attracted my attention: What does that suggest? Note The reason that we compute the set of all words in a document in, rather than just checking if word in document, is that checking whether a word occurs in a set is much faster than checking whether it occurs in a list (4.7).

31. The time has come … We have learned a lot of Python Something about object-oriented programming A bit about Text Analysis A bit about network programming, web crawling, servers, etc. There is lots more to all of those subjects. I am happy to review or discuss anything we did this semester. If you are doing some Python programming later and want to discuss it, I will be happy to talk to you about it.