Ontology-Based Word Sense Disambiguation for Scientific Literature

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Ontology-Based Word Sense Disambiguation for Scientific Literature

  1. 1. Ontology-based Word Sense Disambiguation For Scientific Literature Roman Prokofyev, Gianluca Demartini, Philippe Cudre-Mauroux, Alexey Boyarsky and Oleg Ruchayskiy eXascale Infolab University of Fribourg, Switzerland March 25, ECIR 2013, Moscow
  2. 2. Problem definition Supersymmetric Standard Model State Space Model Sequential Standard Model •  Machine translation: correct lexical choice. •  Information retrieval: ambiguity in queries, result diversification, etc. •  Knowledge extraction: proper text analysis and classification (our case). Our contribution: leveraging the structure of communitybased ontology to improve correct sense identification. Datasets •  ScienceWISE abstract dataset + SW ontology http://sciencewise.info •  MSH abstract dataset + ontology from bioontology.org Available at http://exascale.info/papers/ecir2013disambig
  3. 3. Base models •  Concept Context Vectors Star formation efficiency (SFE) (Instability, 4), (Supernova, 2), (Milky Way, 3),… •  Document Concept Context Vectors 1 (Milky way, 1), (Electron neutrino, 1), (Electron antineutrino, 1),… 2 (Local analysis, 1), (White dwarf, 3), (Poynting-Robertson effect, 1), … Min distance Minimum over the ontological paths to other concepts in the document
  4. 4. Ontology shortest path Average distance to other concepts in the document Nearest neighbors Co-occurring 1-hop neighbors from the ontology
  5. 5. Graph models evaluation Approach Precision (ScienceWISE) Precision (MSH) Min Distance 0.8882 0.6728 Ontology Shortest Path 0.8646 0.5677 Nearest neighbors 0.7393 0.7237 Combined approaches Approach Precision (ScienceWISE) Precision (MSH) Naïve Bayes 0.8513 0.6731 Binary CCV 0.9334 0.9077 + Ontology Shortest Path 0.9444 0.8077 + Nearest neighbors 0.9453 0.9060

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