1. Text classification Day 35 LING 681.02 Computational Linguistics Harry Howard Tulane University

2. 18-Nov-2009LING 681.02, Prof. Howard, Tulane University2 Course organization  http://www.tulane.edu/~ling/NLP/ http://www.tulane.edu/~ling/NLP/

3. Learning to classify text NLPP §6

4. 18-Nov-2009LING 681.02, Prof. Howard, Tulane University4 Classification  What is it?  Supervision  A classifier is supervised if it is built on training corpora containing the correct label for each input.  This usually means that the program can calculate an error when the predicted label does not match the correct label.  A classifier is unsupervised if it is built on training corpora that does not contain the correct label for each input.  There is no way to calculate an error.

5. 18-Nov-2009LING 681.02, Prof. Howard, Tulane University5 Diagram of supervised classification

6. 18-Nov-2009LING 681.02, Prof. Howard, Tulane University6 Philosophical question  Does supervised classification work for the majority of stuff that you learned spontaneously as a child?  NO, life does not come neatly labelled.

7. 18-Nov-2009LING 681.02, Prof. Howard, Tulane University7 Algorithm  Divide the corpus into three sets:  training set  test set  development (dev-test) set  Choose an initial set of features that will be used to classify the corpus.  The part of the program that looks for the features in the corpus is called a feature extractor.  Train the classifier on the training set.  Run it on the development set.  Refine the feature extractor from any errors produced on the development set.  Run the improved classifier on the test set.

8. 18-Nov-2009LING 681.02, Prof. Howard, Tulane University8 Choosing the right features  Use too few, and the data will be underfitted.  The classifier is too vague and makes too many mistakes.  Use too many, and the data will be overfitted.  The classifier is too specific and will not generalize to new examples.

9. 18-Nov-2009LING 681.02, Prof. Howard, Tulane University9 Example: gender id  What would the features be?  A female name ends in a, e, i.  A male name ends in k, o, r, s, t.  Explain how classification would work.  NLTK code pp. 223-4.

10. 18-Nov-2009LING 681.02, Prof. Howard, Tulane University10 More examples  Classify movie reviews as positive or negative.  How?  Classify POS of words.  How?

11. 18-Nov-2009LING 681.02, Prof. Howard, Tulane University11 Beyond the word  Look at word's context.  As we have seen, this is crucial to POS tagging.  Classify IMs as to dialogue acts that they instantiate.  What could be some such acts?  statement, emotion, yes-no question  How?  Recognizing textual entailment  … is the task of determining whether a given piece of text T entails another text called the "hypothesis".  How?

12. 18-Nov-2009LING 681.02, Prof. Howard, Tulane University12 RTE example  T: Parviz Davudi was representing Iran at a meeting of the Shanghai Co-operation Organisation (SCO), the fledgling association that binds Russia, China and four former Soviet republics of central Asia together to fight terrorism.  H: China is a member of SCO.

13. Next time Finish NLPP §6 Go on to NLPP §7 Extracting info from text