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Named Entity Disambiguation Based on Explicit Semantics
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  1. Slovak University of TechnologyBratislava, Slovakia Named Entity Disambiguation Based on Explicit Semantics Martin Jačala and Jozef Tvarožek ŠpindlerůvMlýn, Czech Republic January 23, 2012

  2. Problem • Given an input text, detect and decide on correct meaning of named entites The jaguar is a big cat, a feline in the Panthera genus, and is the only Panthera species found in the Americas. Jaguar (animal) Jaguar cars today are designed in Jaguar Land Rover's engineering centres at the Whitley plant in Coventry. Jaguar Cars Named Entity Disambiguation Based on Explicit Semantics

  3. Current solutions • Charles-Miller hypothesis • Similar words occur in semantically similar contexts • Hence, we should be able to distinguish the correct sense. • Dictionary approach, sense clustering • Semantic similarity • Use of BigData – Wikipedia, Open Directory Project Named Entity Disambiguation Based on Explicit Semantics

  4. General scheme • In detecting the most probable sense • we rank the possible senses, and • pick the best Entity boundary detection Candidate meanings selection Disambiguation dictionary Document transformation Semantic space Similarity computation Named Entity Disambiguation Based on Explicit Semantics

  5. Entity boundary detection • Stanford named entity recognizer • Conditional random fields • F1 score 88 to 93% (CoNLL 2003 dataset) • Used only for detection of surface forms Jenny Rose Finkel, TrondGrenager, and Christopher Manning. 2005. Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling. Proceedings of the 43nd Annual Meeting of the Association for Computational Linguistics (ACL 2005), pp. 363-370. Named Entity Disambiguation Based on Explicit Semantics

  6. Disambiguation dictionary • Build from explicit semantics in Wikipedia • Disambiguation and redirect pages • hyperlinks in articles’ text • Candidate meanings for each surface form,e.g. Jaguar = {Jaguar, Jaguar Cars, Mac OS X 10.2, ...} Big Blue = {IBM, Pacific Ocean, New York Giants, ...} Jordan = {Jordan, Jordan River, Michael Jordan, ...} Named Entity Disambiguation Based on Explicit Semantics

  7. Vector space • Concept space = Wikipedia’s articles • Explicit (human-defined concepts) • We construct term-concept matrix • terms = rows, concepts = columns • tf-idf values • For input text, the “semantic” vector is • Sum of term vectors for all terms in the input Named Entity Disambiguation Based on Explicit Semantics

  8. Example Concepts: Mammal, Britain, Technology, Racing, Wilderness, Amazonia, Sports Car, Leopard The jaguar is a big cat, a feline in the Panthera genus, and is the only Panthera species found in the Americas. (30, 0, 0, 0, 430, 320, 0, 100) Jaguar cars today are designed in Jaguar Land Rover's engineering centres at the Whitley plant in Coventry. (0, 20, 473, 845, 0, 0, 304, 0) Named Entity Disambiguation Based on Explicit Semantics

  9. Ranking • Candidate meaning description = wiki page • Description is transformed to the vector space • Ranking algorithm:Given a surface form, entity meanings are ranked according to the cosine similarity between the input text and each of the candidate meaning descriptions • Extension: add entity occurrence context as input to similarity computation Named Entity Disambiguation Based on Explicit Semantics

  10. Baseline • Dictionary contains surface forms with context • extracted from Wikipedia using letter capitalization • Context = sliding window of 50 words around each occurrence of hypertext link • Article names are the entity labels • Description is the average of contexts in Wikipedia • Disambiguation result is the best similarity match of the input text to the entity descriptions Bunescu, R., Pasca, M.: Using encyclopedic knowledge for named entity disambiguation. In: Proceedings of EACL. pp. 9-16 (2006) Named Entity Disambiguation Based on Explicit Semantics

  11. Datasets • Three datasets – manually disambiguated • Development, Evaluation1, Evaluation2 • Each contains 20 “random” newswire articles containing ambiguous entities • Columbia, Texas, Jaguar, etc. • Discarded refs to entities not in Wikipedia • Each entity has avg. 18 meanings • 78% entities have more than two meanings Named Entity Disambiguation Based on Explicit Semantics

  12. Evaluation results Named Entity Disambiguation Based on Explicit Semantics

  13. Evaluation discussion • Baseline method fails to capture the Wikipedia content due to the way it is written • Hyperlinks are usually early in the article • Latent semantic analysis (250 dims) • Speeds up the process • Decreased accuracy Named Entity Disambiguation Based on Explicit Semantics

  14. Summary • Named entity disambiguation based on explicit semantics • Explicit semantics within Wikipedia • Link descriptions • Redirect and disambiguation pages • 7 to 8% improvement • 86 to 92% accuracy (up from 76 to 85%) Named Entity Disambiguation Based on Explicit Semantics