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Part-of-speech Tagging

Part-of-speech Tagging. cs224n Final project Spring, 2008 Tim Lai. POS Tagging – 3 general techniques. 1. Rule based system Relies on a hand-picked set of rules Performance is not very good 2. Stochastic methods HMM with Viterbi algorithm to determine best tagging

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Part-of-speech Tagging

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  1. Part-of-speech Tagging cs224n Final project Spring, 2008 Tim Lai

  2. POS Tagging – 3 general techniques • 1. Rule based system • Relies on a hand-picked set of rules • Performance is not very good • 2. Stochastic methods • HMM with Viterbi algorithm to determine best tagging • Uses emission probabilities, i.e. P(word | tag) • and transition probabilities, i.e. P(prevTag | currentTag) • Maximum Entropy models also useful • 3. Hybrid of the two • Rules-based system to do POS tagging • Uses rule templates and learns useful rules during training

  3. Simple HMM vs Max-Ent • HMM using bigrams for transition probabilities • Max-Ent using simple features such as previous tag and current word

  4. Error Analysis • HMM and Max-Ent both perform well when tested on data from same domain • Only 6.6 % of words were ambiguous, making known words easy to tag • Accuracy drops when using test data from another domain • Most errors are caused by unknown words, or the POS tagging of words near unknown words. • In sentences without unknown words, accuracy ~ 99%! • Most common mistake is mis-tagging JJ as NN • Need to enhance both taggers to deal with unknowns.

  5. Enhancement ideas • For HMM – • Transition probabilities can be modeled using trigrams, taking more context information into account when word is unknown • For Max-Ent – • Word shapes, word features, and more context can help • Results: • HMM – Switching from Unigram to Bigram helps a lot, but using Trigram doesn’t help much. • Max-Ent – Hand picked features did not help much, but adding prefixes and suffixes were most helpful.

  6. Transformation-based tagging • One more idea to try – using rule-based templates to learn POS tagging rules • Sample rule template: • Change tag A to tag B when the [preceding | following] word is tagged Z. • Change tag A to tag B when the the tag Z appears within [N] positions of the current word. • Result • Using a very restricted set of rule templates, accuracy went up 0.5 %

  7. Final results • HMM with bigram and rule-based adjustments • Max-Ent with prefix/suffix, word shape features and rule-based adjustments • Max-Ent performs better, with 97% accuracy achievable

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