Kentaro Torisawa

Artificial Intelligence · 2007–2009

A researcher in the Science corpus: 2 papers; trusts 9 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Kentaro Torisawa trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

  1. Lawrence R. Rabiner
  2. Ellen Riloff
  3. E. Tjong Kim Sang
  4. Michael Thelen
  5. John D. Lafferty
  6. Oren Etzioni
  7. Rosie Jones
  8. Fien De Meulder

Who trusts Kentaro Torisawa overall

  1. Chenliang Li
  2. Greg Durrett
  3. Lev-Arie Ratinov
  4. Jianfei Yu
  5. Shachar Mirkin
  6. Aixin Sun
  7. Ido Dagan
  8. Xiaoman Pan
  9. Dan Roth
  10. Daniel Klein

Papers from Kentaro Torisawa's perspective

Ordered by how strongly the researchers Kentaro Torisawa trusts endorse each paper. Same corpus, a different order for every researcher.

  1. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
  2. A tutorial on hidden Markov models and selected applications in speech recognition
  3. Learning Dictionaries for Information Extraction by Multi-Level Bootstrapping
  4. Unsupervised named-entity extraction from the Web: An experimental study (Artificial Intelligence)
  5. A Bootstrapping Method for Learning Semantic Lexicons using Extraction Pattern Contexts (Conference on Empirical Methods in Natural Language Processing)
  6. Simple Semi-supervised Dependency Parsing
  7. Using Encyclopedic Knowledge for Named entity Disambiguation (Conference of the European Chapter of the Association for Computational Linguistics)
  8. Online Learning of Approximate Dependency Parsing Algorithms (Conference of the European Chapter of the Association for Computational Linguistics)
  9. WikiRelate! Computing Semantic Relatedness Using Wikipedia (AAAI Conference on Artificial Intelligence)
  10. Exploiting Semantic Role Labeling, WordNet and Wikipedia for Coreference Resolution (North American Chapter of the Association for Computational Linguistics)

Their papers