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ElkfEd/IDC alias BART

ElkfEd/IDC alias BART. Yannick Versley Simone Ponzetto Jason Smith Vladimir Eidelman Alan Jern Massimo Poesio Xiaofeng Yang. Introduction. Modularized and revamped version of EML-R system Full coreference including mention detection Will be publically available as Open Source

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ElkfEd/IDC alias BART

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  1. ElkfEd/IDC alias BART Yannick Versley Simone Ponzetto Jason Smith Vladimir Eidelman Alan Jern Massimo Poesio Xiaofeng Yang

  2. Introduction • Modularized and revamped version of EML-R system • Full coreference including mention detection • Will be publically available as Open Source • Useful Java-only subset (e.g. for teaching)‏

  3. Components • Preprocessingaggregate data in MMAX2 annotation layers • Mention extractioncreate markables from chunks/NEs • Extract information about mentionsmention type, semantic class ... • Encode coref into classifier decisions and extract features

  4. Machine Learning Infrastructure • WEKA Machine Learning Toolkit • C4.5, RIPPER, other learning modules • SVMlight-TK • SVMs, different kernels (linear, polynomial, etc.)‏ • Tree kernels • Custom kernels • MaxEnt-based ranking • find best among a set of candidates

  5. Flexibility in Preprocessing • Chunker (YamCha) vs. Parser (Charniak)‏ • Stanford NER vs. Carafe • Mentions from Chunks/NEs vs.Mentions from Mention Tagger (Carafe)‏

  6. Resolution Algorithms • Closest-first decoding (Soon et al., 2001)‏ • Separate classifiers for pronouns/non-pronouns • Ranking-based resolution • Stacking ranking+classifier • Global models

  7. Quantitative / Qualitative Evaluation • MUC scoring (per document, in total)‏ • Link-based scores, by type of anaphora(pronouns, appositions / copula, names, nominals)‏ • Qualitative evaluationinspect results in MMAX2

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