Web-derived resources for web information retrieval: from conceptual hierarchies to attribute hierarchies

ABSTRACT

A weakly-supervised extraction method identifies concepts within conceptual hierarchies, at the appropriate level of specificity (e.g., Bank vs. Institution), to which attributes (e.g., routing number) extracted from unstructured text best apply. The extraction exploits labeled classes of instances acquired from a combination of Web documents and query logs, and inserted into existing conceptual hierarchies. The correct concept is identified within the top three positions on average over gold-standard attributes, which corresponds to higher accuracy than in alternative experiments.

REFERENCES

• 1: M. Banko and O. Etzioni. The tradeoffs between open and traditional relation extraction. In Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics (ACL-08), pages 28--36, Columbus, Ohio, 2008.
• 2: Thorsten Brants, TnT: a statistical part-of-speech tagger, Proceedings of the sixth conference on Applied natural language processing, p.224-231, April 29-May 04, 2000, Seattle, Washington [doi>10.3115/974147.974178]
• 3: C. Fellbaum, editor. WordNet: An Electronic Lexical Database and Some of its Applications. MIT Press, 1998.
• 4: Wei Gao , Cheng Niu , Jian-Yun Nie , Ming Zhou , Jian Hu , Kam-Fai Wong , Hsiao-Wuen Hon, Cross-lingual query suggestion using query logs of different languages, Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, July 23-27, 2007, Amsterdam, The Netherlands [doi>10.1145/1277741.1277821]
• 5: Marti A. Hearst, Automatic acquisition of hyponyms from large text corpora, Proceedings of the 14th conference on Computational linguistics, August 23-28, 1992, Nantes, France [doi>10.3115/992133.992154]
• 6: Dekang Lin , Patrick Pantel, Concept discovery from text, Proceedings of the 19th international conference on Computational linguistics, p.1-7, August 24-September 01, 2002, Taipei, Taiwan [doi>10.3115/1072228.1072372]
• 7: Diana McCarthy , Rob Koeling , Julie Weeds , John Carroll, Unsupervised acquisition of predominant word senses, Computational Linguistics, v.33 n.4, p.553-590, December 2007 [doi>10.1162/coli.2007.33.4.553]
• 8: Marius Paşca, Organizing and searching the world wide web of facts -- step two: harnessing the wisdom of the crowds, Proceedings of the 16th international conference on World Wide Web, May 08-12, 2007, Banff, Alberta, Canada [doi>10.1145/1242572.1242587]
• 9: M. Paşca. Turning Web text and search queries into factual knowledge: Hierarchical class attribute extraction. In Proceedings of the 23rd National Conference on Artificial Intelligence (AAAI-08), pages 1225--1230, Chicago, Illinois, 2008.
• 10: M. Palmer, H. Dang, and C. Fellbaum. Making fine-grained and coarse-grained sense distinctions, both manually and automatically. Natural Language Engineering, 13(2):137--163, 2007.
• 11: S. Pradhan, E. Loper, D. Dligach, and M. Palmer. SemEval-2007 Task-17: English lexical sample, SRL and all words. In Proceedings of the 4th Workshop on Semantic Evaluations (SemEval-07), pages 87--92, Prague, Czech Republic, 2007.
• 12: M. Remy. Wikipedia: The free encyclopedia. Online Information Review, 26(6):434, 2002.
• 13: Rion Snow , Daniel Jurafsky , Andrew Y. Ng, Semantic taxonomy induction from heterogenous evidence, Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics, p.801-808, July 17-18, 2006, Sydney, Australia [doi>10.3115/1220175.1220276]
• 14: Fabian M. Suchanek , Gjergji Kasneci , Gerhard Weikum, Yago: a core of semantic knowledge, Proceedings of the 16th international conference on World Wide Web, May 08-12, 2007, Banff, Alberta, Canada [doi>10.1145/1242572.1242667]
• 15: K. Tokunaga, J. Kazama, and K. Torisawa. Automatic discovery of attribute words from Web documents. In Proceedings of the 2nd International Joint Conference on Natural Language Processing (IJCNLP-05), pages 106--118, Jeju Island, Korea, 2005.
• 16: Benjamin Van Durme , Phillip Michalak , Lenhart K. Schubert, Deriving generalized knowledge from corpora using WordNet abstraction, Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics, p.808-816, March 30-April 03, 2009, Athens, Greece
• 17: Ellen M. Voorhees , Dawn M. Tice, Building a question answering test collection, Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval, p.200-207, July 24-28, 2000, Athens, Greece [doi>10.1145/345508.345577]

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Search process

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.1 Content Analysis and Indexing
          Subjects: Linguistic processing

  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.7 Natural Language Processing
          Subjects: Text analysis

General Terms:
Algorithms, Experimentation

Keywords:
class attributes, conceptual hierarchies, knowledge acquisition, named entities, unstructured text, web search