Berkeley COMPSCI 294 - Lecture Notes (7 pages)

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Lecture Notes



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Lecture Notes

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Pages:
7
School:
University of California, Berkeley
Course:
Compsci 294 - Special Topics
Special Topics Documents

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CS 294 5 Statistical Natural Language Processing Last Time Language models for text categorization Na ve Bayes conditional LMs c START w1 w2 wn Generative models Dan Klein MF 1 2 30pm Soda Hall 310 Word Senses Words have multiple distinct meanings or senses Plant living plant manufacturing plant Title name of a work ownership document form of address material at the start of a film Many levels of sense distinctions Homonymy totally unrelated meanings river bank money bank Polysemy related meanings star in sky star on tv Systematic polysemy productive meaning extensions organizations to their buildings or metaphor Sense distinctions can be extremely subtle or not Granularity of senses needed depends a lot on the task Why is it importat to model word senses Translation parsing information retrieval Verb WSD Why are verbs harder Verbal senses less topical More sensitive to structure argument choice Verb Example Serve function The tree stump serves as a table enable The scandal served to increase his popularity dish We serve meals for the homeless enlist He served his country jail He served six years for embezzlement tennis It was Agassi s turn to serve legal He was served by the sheriff Rest of today a maximum entropy approach Break a complex structure down into derivation steps Each step is a multinomial choice conditioned on some history We estimate those multinomials by collecting counts and smoothing Backbone of statistical NLP until very recently Today maximum entropy a discriminative approach Word Sense Disambiguation Example living plant vs manufacturing plant How do we tell these senses apart context The manufacturing plant which had previously sustained the town s economy shut down after an extended labor strike Maybe it s just text categorization Each word sense represents a topic Run the naive bayes classifier from last class Bag of words classification works ok for noun senses 90 on classic shockingly easy examples line interest star 80 on senseval 1 nouns



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