Machine Learning PoS-Taggers

1. School of Computing FACULTY OF ENGINEERING Machine Learning PoS-Taggers COMP3310 Natural Language Processing Eric Atwell, Language Research Group (with thanks to Katja Markert, Marti Hearst, and other contributors)

2. Reminder • Puns play on our assumptions of the next word… • … eg they present us with an unexpected homonym (bends) • ConditionalFreqDist() counts word-pairs: word bigrams • Used for story generation, Speech recognition, … • Parts of Speech: groups words into grammatical categories • … and separates different functions of a word • In English, many words are ambiguous: 2 or more PoS-tags • Very simple tagger: tag with the likeliest tag for the word • Better Pos-Taggers: to come…

3. Taking context into account • Theory behind some example Machine Learning PoS-taggers • Example implementations in NLTK • Machine Learning from a PoS-tagged training corpus • Statistical (N-Gram/Markov) taggers: • learn table of 1/2/3/N-tag sequence frequencies • Brill (transformation-based) tagger: • learn likeliest tag for each word ignoring context, • then learn rules to change tag to fit context • NB you don’t have to use NLTK – just useful to illustrate

4. Training and Testing ofMachine Learning Algorithms • Algorithms that “learn” from data see a set of examples and try to generalize from them. • Training set: • Examples trained on • Test set: • Also called held-out data and unseen data • Use this for evaluating your algorithm • Must be separate from the training set; otherwise, you cheated! • “Gold standard” evaluation corpus • An evaluation set that a community has agreed on and uses as a common benchmark. • Not “seen” until development is finished – ONLY for evaluation

5. Cross-Validation of Learning Algorithms • Cross-validation set • Part of the training set. • Used for tuning parameters of the algorithm without “polluting” (tuning to) the test data. • You can train on x%, and then cross-validate on the remaining 1-x% • E.g., train on 90% of the training data, cross-validate (test) on the remaining 10% • Repeat several times with different splits • This allows you to choose the best settings to then use on the real test set. • You should only evaluate on the test set at the very end, after you’ve gotten your algorithm as good as possible on the cross-validation set.

6. Strong Baselines • When designing NLP algorithms, you need to evaluate them by comparing to others. • Baseline Algorithm: • An algorithm that is relatively simple but can be expected to do “ok” • Should get the best score possible by doing the obvious thing.

7. A Tagging Baseline • Find the most likely tag for the most frequent words • Frequent words are ambiguous • You’re likely to see frequent words in any collection • Will always see “to” but might not see “armadillo” • How to do this? • First find the most likely words and their tags in the training data • Train a tagger that looks up these results in a table

8. Find the most frequent words and the most likely tag of each

9. Use our own tagger class

10. N-Grams • The N stands for how many terms are used • Unigram: 1 term (0th order) • Bigram: 2 terms (1st order) • Trigrams: 3 terms (2nd order) • Usually don’t go beyond this • You can use different kinds of terms, e.g.: • Character based n-grams • Word-based n-grams • POS-based n-grams • Ordering • Often adjacent, but not required • We use n-grams to help determine the context in which some linguistic phenomenon happens. E.g., look at the words before and after period to see if it is the end of sentence or not.

11. Tagging with lexical frequencies • Secretariat/NNP is/VBZ expected/VBN to/TO race/VB tomorrow/NN • People/NNS continue/VBP to/TO inquire/VB the/DT reason/NN for/IN the/DT race/NN for/IN outer/JJ space/NN • Problem: assign a tag to race given its lexical frequency • Solution: we choose the tag that has the greater probability • P(race|VB) • P(race|NN) Modified from Massio Poesio's lecture

12. Unigram Tagger • Train on a set of sentences • Keep track of how many times each word is seen with each tag. • After training, associate with each word its most likely tag. • Problem: many words never seen in the training data. • Solution: have a default tag to “backoff” to.

13. Unigram tagger with Backoff

14. What’s wrong with unigram? • Most frequent tag isn’t always right! • Need to take the context into account • Which sense of “to” is being used? • Which sense of “like” is being used?

15. N-gram tagger • Uses the preceding N-1 predicted tags • Also uses the unigram estimate for the current word

16. Bigram Tagging • For tagging, in addition to considering the token’s type, the context also considers the tags of the n preceding tokens • What is the most likely tag for word n, given word n-1 and tag n-1? • The tagger picks the tag which is most likely for that context. Modified from Diane Litman's version of Steve Bird's notes

17. Combining Taggers using Backoff • Use more accurate algorithms when we can, backoff to wider coverage when needed. • Try tagging the token with the 1st order tagger. • If the 1st order tagger is unable to find a tag for the token, try finding a tag with the 0th order tagger. • If the 0th order tagger is also unable to find a tag, use the default tagger to find a tag. • Important point: • Bigram and trigram taggers use the previous tag context to assign new tags. If they see a tag of “None” in the previous context, they will output “None” too. Modified from Diane Litman's version of Steve Bird's notes

18. Demonstrating the n-gram taggers • Trained on brown.tagged(‘a’), tested on brown.tagged(‘b’) • Backs off to a default of ‘nn’

19. Demonstrating the n-gram taggers

20. Combining Taggers • The bigram backoff tagger did worse than the unigram! Why? • Why does it get better again with trigrams? • How can we improve these scores?

21. Rule-Based Tagger • The Linguistic Complaint • Where is the linguistic knowledge of a tagger? • Just a massive table of numbers • Aren’t there any linguistic insights that could emerge from the data? • Could thus use handcrafted sets of rules to tag input sentences, for example, if input follows a determiner tag it as a noun. • Constraint Grammar (CG) tagger: PhD student spends 3+ years coding a large set of these rules (for English, Finnish, …) • Machine Learning researchers would prefer to use ML to extract a large set of such rules from a PoS-tagged training corpus Modified from Diane Litman's version of Steve Bird's notes

22. The Brill tagger • An example of Transformation-Based Learning • Basic idea: do a quick job first (using frequency), then revise it using contextual rules. • Very popular (freely available, works fairly well) • A supervised method: requires a tagged corpus Slide modified from Massimo Poesio's

23. Brill Tagging: In more detail • Start with simple (less accurate) rules…learn better ones from tagged corpus • Tag each word initially with most likely POS • Examine set of transformationsto see which improves tagging decisions compared to tagged corpus • Re-tag corpus using best transformation • Repeat until, e.g., performance doesn’t improve • Result: tagging procedure (ordered list of transformations) which can be applied to new, untagged text

24. An example • Examples: • They are expected to racetomorrow. • Therace for outer space. • Tagging algorithm: • Tag all uses of “race” as NN (most likely tag in the Brown corpus) • They are expected to race/NN tomorrow • the race/NN for outer space • Use a transformation rule to replace the tag NN with VB for all uses of “race” preceded by the tag TO: • They are expected to race/VB tomorrow • the race/NN for outer space Slide modified from Massimo Poesio's

25. Example Rule Transformations

26. Sample Final Rules

27. Summary: N-gram/Markov and Transformation/Brill PoS-Taggers • Theory behind some example Machine Learning PoS-taggers • Example implementations in NLTK • Machine Learning from a PoS-tagged training corpus • Statistical (N-Gram/Markov) taggers: • learn table of 1/2/3/N-tag sequence frequencies • If not enough data for N, back off to N-1 patterns • Brill (transformation-based) tagger: • learn likeliest tag for each word ignoring context, • then learn rules to change tag to fit context • NB you don’t have to use NLTK – just useful to illustrate