Pattern learning for relation extraction with a hierarchical topic model
E. Alfonseca, K. Filippova, J. Delort, und G. Garrido. Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2, Seite 54--59. Stroudsburg, PA, USA, Association for Computational Linguistics, (2012)
Zusammenfassung
We describe the use of a hierarchical topic model for automatically identifying syntactic and lexical patterns that explicitly state ontological relations. We leverage distant supervision using relations from the knowledge base FreeBase, but do not require any manual heuristic nor manual seed list selections. Results show that the learned patterns can be used to extract new relations with good precision.
Beschreibung
Pattern learning for relation extraction with a hierarchical topic model
%0 Conference Paper
%1 Alfonseca:2012:PLR:2390665.2390679
%A Alfonseca, Enrique
%A Filippova, Katja
%A Delort, Jean-Yves
%A Garrido, Guillermo
%B Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
%C Stroudsburg, PA, USA
%D 2012
%I Association for Computational Linguistics
%K 2012 alfonseca extraction model relation topic
%P 54--59
%T Pattern learning for relation extraction with a hierarchical topic model
%U http://dl.acm.org/ft_gateway.cfm?id=2390679&ftid=1304477&dwn=1&CFID=203278172&CFTOKEN=56557103
%X We describe the use of a hierarchical topic model for automatically identifying syntactic and lexical patterns that explicitly state ontological relations. We leverage distant supervision using relations from the knowledge base FreeBase, but do not require any manual heuristic nor manual seed list selections. Results show that the learned patterns can be used to extract new relations with good precision.
@inproceedings{Alfonseca:2012:PLR:2390665.2390679,
abstract = {We describe the use of a hierarchical topic model for automatically identifying syntactic and lexical patterns that explicitly state ontological relations. We leverage distant supervision using relations from the knowledge base FreeBase, but do not require any manual heuristic nor manual seed list selections. Results show that the learned patterns can be used to extract new relations with good precision.},
acmid = {2390679},
added-at = {2013-04-14T13:02:40.000+0200},
address = {Stroudsburg, PA, USA},
author = {Alfonseca, Enrique and Filippova, Katja and Delort, Jean-Yves and Garrido, Guillermo},
biburl = {https://www.bibsonomy.org/bibtex/273fa4303a96404d5a95f2f66e0fc4e22/jil},
booktitle = {Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2},
description = {Pattern learning for relation extraction with a hierarchical topic model},
interhash = {9f27ce54d50c0f3af60512fdd1bd1709},
intrahash = {73fa4303a96404d5a95f2f66e0fc4e22},
keywords = {2012 alfonseca extraction model relation topic},
location = {Jeju Island, Korea},
numpages = {6},
pages = {54--59},
publisher = {Association for Computational Linguistics},
series = {ACL '12},
timestamp = {2013-11-23T20:11:51.000+0100},
title = {Pattern learning for relation extraction with a hierarchical topic model},
url = {http://dl.acm.org/ft_gateway.cfm?id=2390679&ftid=1304477&dwn=1&CFID=203278172&CFTOKEN=56557103},
year = 2012
}