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The ‘label bias’ problem: MEMMs and CRFs

The ‘label bias’ problem: MEMMs and CRFs. notes for CSCI-GA.2590 Prof. Grishman. Consider a simple MEMM for person and location names all names are two tokens states: other b -person and e -person for person names b-locn and e-locn for location names. corpus: Harvey Ford

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The ‘label bias’ problem: MEMMs and CRFs

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  1. The ‘label bias’ problem:MEMMs and CRFs notes for CSCI-GA.2590 Prof. Grishman

  2. Consider a simple MEMMfor person and location names • all names are two tokens • states: • other • b-person and e-person for person names • b-locn and e-locn for location names

  3. corpus: Harvey Ford (person 9 times, location 1 time) Harvey Park (location 9 times, person 1 time) Myrtle Ford (person 9 times, location 1 time) Myrtle Park (location 9 times, person 1 time) second token a good indicator of person vs. location b-person e-person other e-locn b-locn

  4. Conditional probabilities: p(b-person | other, w = Harvey) = 0.5 p(b-locn | other, w = Harvey) = 0.5 p(b-person | other, w = Myrtle) = 0.5 p(b-locn | other, w = Myrtle) = 0.5 p(e-person | b-person, w = Ford) = 1 p(e-person | b-person, w = Park) = 1 p(e-locn | b-locn, w = Ford) = 1 p(e-locn | b-locn, w = Park) = 1 b-person e-person other e-locn b-locn

  5. Role of second token in distinguishing person vs. location completely lost b-person e-person other e-locn b-locn

  6. Problem: • Probabilities of outgoing arcs normalized separately for each state: • the “label bias” problem

  7. Conditional Random Fields • Conditional Random Fields (CRFs) address this problem: • MEMMs use a per-state exponential model • CRFs have a single exponential model for the joint probability of the entire label sequence

  8. Graphical comparison among HMMs, MEMMs and CRFs(from Lafferty et al.) HMM MEMM CRF

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