Abstract
| Original language | English |
|---|---|
| Title of host publication | Advances in Information Retrieval |
| Subtitle of host publication | 31th European Conference on IR Research, ECIR 2009, Toulouse, France, April 6-9, 2009. Proceedings |
| Editors | Mohand Boughanem, Catherine Berrut, Josiane Mothe, Chantal Soule-Dupuy |
| Place of Publication | Berlin, Heidelberg |
| Publisher | Springer |
| Pages | 810-813 |
| Number of pages | 4 |
| ISBN (Electronic) | 978-3-642-00958-7 |
| ISBN (Print) | 978-3-642-00957-0 |
| DOIs | |
| Publication status | Published - Apr 2009 |
| Event | 31th European Conference on Information Retrieval, ECIR 2009: (IR Research) - Toulouse, France Duration: 6 Apr 2009 → 9 Apr 2009 Conference number: 31 |
Publication series
| Name | Lecture Notes in Computer Science |
|---|---|
| Publisher | Springer |
| Volume | 5478 |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Conference
| Conference | 31th European Conference on Information Retrieval, ECIR 2009 |
|---|---|
| Abbreviated title | ECIR |
| Country | France |
| City | Toulouse |
| Period | 6/04/09 → 9/04/09 |
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Keywords
- METIS-264416
- EWI-15897
- IR-67847
Cite this
}
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 proceeding › Conference contribution › Academic › peer-review
TY - GEN
T1 - Information Extraction and Linking in a Retrieval Context
AU - Moens, Marie-Francine
AU - Hiemstra, Djoerd
PY - 2009/4
Y1 - 2009/4
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.
KW - METIS-264416
KW - EWI-15897
KW - IR-67847
U2 - 10.1007/978-3-642-00958-7_93
DO - 10.1007/978-3-642-00958-7_93
M3 - Conference contribution
SN - 978-3-642-00957-0
T3 - Lecture Notes in Computer Science
SP - 810
EP - 813
BT - Advances in Information Retrieval
A2 - Boughanem, Mohand
A2 - Berrut, Catherine
A2 - Mothe, Josiane
A2 - Soule-Dupuy, Chantal
PB - Springer
CY - Berlin, Heidelberg
ER -