Information Extraction and Linking in a Retrieval Context

Marie-Francine Moens, Djoerd Hiemstra

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

2 Citations (Scopus)
9 Downloads (Pure)

Abstract

We witness a growing interest and capabilities of automatic content recognition (often referred to as information extraction) in various media sources that identify entities (e.g. persons, locations and products) and their semantic attributes (e.g., opinions expressed towards persons or products, relations between entities).These extraction techniques are most advanced for text sources, but they are also researched for other media, for instance for recognizing persons and objects in images or video. The extracted information enriches and adds semantic meaning to document and queries (the latter e.g., in a relevance feedback setting). In addition, content recognition techniques trigger automated linking of information across documents and even across media. This situation poses a number of opportunities and challenges for retrieval and ranking models. For instance, instead of returning full documents, information extraction provides the means to return very focused results in the form of entities such as persons and locations. Another challenge is to integrate content recognition and content retrieval as much as possible, for instance by using the probabilistic output from the information extraction tools in the retrieval phase. These approaches are important steps towards semantic search, i.e., retrieval approaches that truly use the semantics of the data.
Original languageEnglish
Title of host publicationAdvances in Information Retrieval
Subtitle of host publication31th European Conference on IR Research, ECIR 2009, Toulouse, France, April 6-9, 2009. Proceedings
EditorsMohand Boughanem, Catherine Berrut, Josiane Mothe, Chantal Soule-Dupuy
Place of PublicationBerlin, Heidelberg
PublisherSpringer
Pages810-813
Number of pages4
ISBN (Electronic)978-3-642-00958-7
ISBN (Print)978-3-642-00957-0
DOIs
Publication statusPublished - Apr 2009
Event31th European Conference on Information Retrieval, ECIR 2009: (IR Research) - Toulouse, France
Duration: 6 Apr 20099 Apr 2009
Conference number: 31

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume5478
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference31th European Conference on Information Retrieval, ECIR 2009
Abbreviated titleECIR
CountryFrance
CityToulouse
Period6/04/099/04/09

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Keywords

  • METIS-264416
  • EWI-15897
  • IR-67847

Cite this

Moens, M-F., & Hiemstra, D. (2009). Information Extraction and Linking in a Retrieval Context. In M. Boughanem, C. Berrut, J. Mothe, & C. Soule-Dupuy (Eds.), Advances in Information Retrieval: 31th European Conference on IR Research, ECIR 2009, Toulouse, France, April 6-9, 2009. Proceedings (pp. 810-813). (Lecture Notes in Computer Science; Vol. 5478). Berlin, Heidelberg: Springer. https://doi.org/10.1007/978-3-642-00958-7_93
Moens, Marie-Francine ; Hiemstra, Djoerd. / Information Extraction and Linking in a Retrieval Context. Advances in Information Retrieval: 31th European Conference on IR Research, ECIR 2009, Toulouse, France, April 6-9, 2009. Proceedings. editor / Mohand Boughanem ; Catherine Berrut ; Josiane Mothe ; Chantal Soule-Dupuy. Berlin, Heidelberg : Springer, 2009. pp. 810-813 (Lecture Notes in Computer Science).
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Moens, M-F & Hiemstra, D 2009, Information Extraction and Linking in a Retrieval Context. in M Boughanem, C Berrut, J Mothe & C Soule-Dupuy (eds), Advances in Information Retrieval: 31th European Conference on IR Research, ECIR 2009, Toulouse, France, April 6-9, 2009. Proceedings. Lecture Notes in Computer Science, vol. 5478, Springer, Berlin, Heidelberg, pp. 810-813, 31th European Conference on Information Retrieval, ECIR 2009, Toulouse, France, 6/04/09. https://doi.org/10.1007/978-3-642-00958-7_93

Information Extraction and Linking in a Retrieval Context. / Moens, Marie-Francine; Hiemstra, Djoerd.

Advances in Information Retrieval: 31th European Conference on IR Research, ECIR 2009, Toulouse, France, April 6-9, 2009. Proceedings. ed. / Mohand Boughanem; Catherine Berrut; Josiane Mothe; Chantal Soule-Dupuy. Berlin, Heidelberg : Springer, 2009. p. 810-813 (Lecture Notes in Computer Science; Vol. 5478).

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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N2 - We witness a growing interest and capabilities of automatic content recognition (often referred to as information extraction) in various media sources that identify entities (e.g. persons, locations and products) and their semantic attributes (e.g., opinions expressed towards persons or products, relations between entities).These extraction techniques are most advanced for text sources, but they are also researched for other media, for instance for recognizing persons and objects in images or video. The extracted information enriches and adds semantic meaning to document and queries (the latter e.g., in a relevance feedback setting). In addition, content recognition techniques trigger automated linking of information across documents and even across media. This situation poses a number of opportunities and challenges for retrieval and ranking models. For instance, instead of returning full documents, information extraction provides the means to return very focused results in the form of entities such as persons and locations. Another challenge is to integrate content recognition and content retrieval as much as possible, for instance by using the probabilistic output from the information extraction tools in the retrieval phase. These approaches are important steps towards semantic search, i.e., retrieval approaches that truly use the semantics of the data.

AB - We witness a growing interest and capabilities of automatic content recognition (often referred to as information extraction) in various media sources that identify entities (e.g. persons, locations and products) and their semantic attributes (e.g., opinions expressed towards persons or products, relations between entities).These extraction techniques are most advanced for text sources, but they are also researched for other media, for instance for recognizing persons and objects in images or video. The extracted information enriches and adds semantic meaning to document and queries (the latter e.g., in a relevance feedback setting). In addition, content recognition techniques trigger automated linking of information across documents and even across media. This situation poses a number of opportunities and challenges for retrieval and ranking models. For instance, instead of returning full documents, information extraction provides the means to return very focused results in the form of entities such as persons and locations. Another challenge is to integrate content recognition and content retrieval as much as possible, for instance by using the probabilistic output from the information extraction tools in the retrieval phase. These approaches are important steps towards semantic search, i.e., retrieval approaches that truly use the semantics of the data.

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PB - Springer

CY - Berlin, Heidelberg

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Moens M-F, Hiemstra D. Information Extraction and Linking in a Retrieval Context. In Boughanem M, Berrut C, Mothe J, Soule-Dupuy C, editors, Advances in Information Retrieval: 31th European Conference on IR Research, ECIR 2009, Toulouse, France, April 6-9, 2009. Proceedings. Berlin, Heidelberg: Springer. 2009. p. 810-813. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-642-00958-7_93