Presentation on theme: "School of something FACULTY OF OTHER School of Computing FACULTY OF ENGINEERING Machine Learning PoS-Taggers COMP3310 Natural Language Processing Eric."— Presentation transcript:
School of something FACULTY OF OTHER 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)
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…
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 dont have to use NLTK – just useful to illustrate
Training and Testing of Machine 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
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 youve gotten your algorithm as good as possible on the cross-validation set.
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.
A Tagging Baseline Find the most likely tag for the most frequent words Frequent words are ambiguous Youre 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
Find the most frequent words and the most likely tag of each
Use our own tagger class
N-Grams The N stands for how many terms are used Unigram: 1 term (0 th order) Bigram: 2 terms (1 st order) Trigrams: 3 terms (2 nd order) Usually dont 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.
Modified from Massio Poesio's lecture 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)
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.
Unigram tagger with Backoff
Whats wrong with unigram? Most frequent tag isnt always right! Need to take the context into account Which sense of to is being used? Which sense of like is being used?
N-gram tagger Uses the preceding N-1 predicted tags Also uses the unigram estimate for the current word
Modified from Diane Litman's version of Steve Bird's notes 16 Bigram Tagging For tagging, in addition to considering the tokens 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 1 st order tagger. If the 1 st order tagger is unable to find a tag for the token, try finding a tag with the 0 th order tagger. If the 0 th 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.
Demonstrating the n-gram taggers Trained on brown.tagged(a), tested on brown.tagged(b) Backs off to a default of nn
Demonstrating the n-gram taggers
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?
Modified from Diane Litman's version of Steve Bird's notes 21 Rule-Based Tagger The Linguistic Complaint Where is the linguistic knowledge of a tagger? Just a massive table of numbers Arent 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
Slide modified from Massimo Poesio's 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
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 transformations to see which improves tagging decisions compared to tagged corpus Re-tag corpus using best transformation Repeat until, e.g., performance doesnt improve Result: tagging procedure (ordered list of transformations) which can be applied to new, untagged text
Slide modified from Massimo Poesio's 24 An example Examples: They are expected to race tomorrow. The race for outer space. Tagging algorithm: 1.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 2.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
Example Rule Transformations
Sample Final Rules
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 dont have to use NLTK – just useful to illustrate