Details
| Original language | English |
|---|---|
| Title of host publication | Long and Short Papers |
| Publisher | Association for Computational Linguistics (ACL) |
| Pages | 2336-2346 |
| Number of pages | 11 |
| ISBN (electronic) | 9781950737130 |
| Publication status | Published - 2019 |
| Event | 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2019 - Minneapolis, United States Duration: 2 Jun 2019 → 7 Jun 2019 |
Publication series
| Name | NAACL HLT 2019 - 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference |
|---|---|
| Volume | 1 |
Abstract
Short texts challenge NLP tasks such as named entity recognition, disambiguation, linking and relation inference because they do not provide sufficient context or are partially malformed (e.g. wrt. capitalization, long tail entities, implicit relations). In this work, we present the Falcon approach which effectively maps entities and relations within a short text to its mentions of a background knowledge graph. Falcon overcomes the challenges of short text using a light-weight linguistic approach relying on a background knowledge graph. Falcon performs joint entity and relation linking of a short text by leveraging several fundamental principles of English morphology (e.g. compounding, headword identification) and utilizes an extended knowledge graph created by merging entities and relations from various knowledge sources. It uses the context of entities for finding relations and does not require training data. Our empirical study using several standard benchmarks and datasets show that Falcon significantly outperforms state-of-the-art entity and relation linking for short text query inventories.
ASJC Scopus subject areas
- Arts and Humanities(all)
- Language and Linguistics
- Computer Science(all)
- Computer Science Applications
- Social Sciences(all)
- Linguistics and Language
Cite this
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Long and Short Papers. Association for Computational Linguistics (ACL), 2019. p. 2336-2346 (NAACL HLT 2019 - 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference; Vol. 1).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Old is gold
T2 - 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2019
AU - Sakor, Ahmad
AU - Mulang, Isaiah Onando
AU - Singh, Kuldeep
AU - Shekarpour, Saeedeh
AU - Vidal, Maria Esther
AU - Lehmann, Jens
AU - Auer, Sören
N1 - Funding information: This work has received funding from the EU H2020 Project No. 727658 (IASIS) and partially funded from Fraunhofer IAIS KDDS project No. 500747.
PY - 2019
Y1 - 2019
N2 - Short texts challenge NLP tasks such as named entity recognition, disambiguation, linking and relation inference because they do not provide sufficient context or are partially malformed (e.g. wrt. capitalization, long tail entities, implicit relations). In this work, we present the Falcon approach which effectively maps entities and relations within a short text to its mentions of a background knowledge graph. Falcon overcomes the challenges of short text using a light-weight linguistic approach relying on a background knowledge graph. Falcon performs joint entity and relation linking of a short text by leveraging several fundamental principles of English morphology (e.g. compounding, headword identification) and utilizes an extended knowledge graph created by merging entities and relations from various knowledge sources. It uses the context of entities for finding relations and does not require training data. Our empirical study using several standard benchmarks and datasets show that Falcon significantly outperforms state-of-the-art entity and relation linking for short text query inventories.
AB - Short texts challenge NLP tasks such as named entity recognition, disambiguation, linking and relation inference because they do not provide sufficient context or are partially malformed (e.g. wrt. capitalization, long tail entities, implicit relations). In this work, we present the Falcon approach which effectively maps entities and relations within a short text to its mentions of a background knowledge graph. Falcon overcomes the challenges of short text using a light-weight linguistic approach relying on a background knowledge graph. Falcon performs joint entity and relation linking of a short text by leveraging several fundamental principles of English morphology (e.g. compounding, headword identification) and utilizes an extended knowledge graph created by merging entities and relations from various knowledge sources. It uses the context of entities for finding relations and does not require training data. Our empirical study using several standard benchmarks and datasets show that Falcon significantly outperforms state-of-the-art entity and relation linking for short text query inventories.
UR - http://www.scopus.com/inward/record.url?scp=85081090168&partnerID=8YFLogxK
U2 - 10.18653/v1/N19-1243
DO - 10.18653/v1/N19-1243
M3 - Conference contribution
AN - SCOPUS:85081090168
T3 - NAACL HLT 2019 - 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference
SP - 2336
EP - 2346
BT - Long and Short Papers
PB - Association for Computational Linguistics (ACL)
Y2 - 2 June 2019 through 7 June 2019
ER -