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Master in Artificial Intelligence (MIA). Structure Learning for NLP Named-entity recognition using generative models. An Algorithm that Learns What’s in a Name (IdentiFinder TM ) Dan M. Bikel, Richard Schwartz, Ralph Weischedel Machine Learning Special Issue on Natural Language Learning

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Master in Artificial Intelligence (MIA)

  • Structure Learning for NLP

    • Named-entity recognition using generative models


An Algorithm that Learns What’s in a Name (IdentiFinderTM)

Dan M. Bikel, Richard Schwartz, Ralph Weischedel

Machine Learning

Special Issue on Natural Language Learning

C. Cardie and R. Mooney eds. 1999


Generalities
Generalities

  • HMM-based model for NERC

  • Evolution from the Nymble system (1997)

  • Applied in MUC conferences and other corpora with very good results

  • Competitive (and better in some cases) to the best hand-developed rule-based NERC systems

  • State-of-the-art for the task until CoNLL conferences (ML)



HMMs

Decoding: The argmax function can be computed in linear time O(n) using dynamic programming (Viterbi algorithm)



Identifinder hmm
IdentiFinder HMM

  • Generative model

  • Probability distributions needed:

    • P(NC|NC-1,w-1)

    • P(<w,f>first |NC,NC-1)

    • P(<w,f> |<w,f>-1,NC-1)


Identifinder hmm1
IdentiFinder HMM

  • Example: Mr. [John]E-PERSON eats

    Probability of the previous annotated sequence:


Identifinder hmm2
IdentiFinder HMM

  • Parameter Estimation

    • Maximum likelihood estimates

    • Incorporates smoothing and back-off to face sparseness

  • Decoding

    • argmaxNC (NC1…NCn|w1…wn)

    • Viterbi algorithm (dynamic programming)




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