Classifying text NLTK Chapter 6

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?

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

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

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

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

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'), …

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

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

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

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.

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

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) >>> train_names = names[1500:] >>> devtest_names = names[500:1500] >>> test_names = names[:500] Accuracy noted, but where were the problems?

Check the classifier against the known values and see where it failed: >>> 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)... correct=female guess=male name=Cindely... correct=female guess=male name=Katheryn correct=female guess=male name=Kathryn... correct=male guess=female name=Aldrich... correct=male guess=female name=Mitch

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

Document classification Many uses. Case study, classifying movie reviews >>> 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) Feature extraction for documents will use words Find most common words in the document set and see which words are in which types of documents

Feature extractor. Are the words present in the 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')) {'contains(waste)': False, 'contains(lot)': False,...}

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) >>> print nltk.classify.accuracy(classifier, test_set) 0.81 >>> classifier.show_most_informative_features(5) Most Informative Features contains(outstanding) = True pos : neg = 11.1 : 1.0 contains(seagal) = True neg : pos = 7.7 : 1.0 contains(wonderfully) = True pos : neg = 6.8 : 1.0 contains(damon) = True pos : neg = 5.9 : 1.0 contains(wasted) = True neg : pos = 5.8 : 1.0

There is more As time allows, let’s look at other sections of this chapter. We do not have time to do justice to all the topics, but we can take a few and look into them.