TY - GEN
T1 - Generating coherent event schemas at scale
AU - Balasubramanian, Niranjan
AU - Soderland, Stephen
AU - Mausam,
AU - Etzioni, Oren
N1 - Publisher Copyright:
© 2013 Association for Computational Linguistics.
PY - 2013
Y1 - 2013
N2 - Chambers and Jurafsky (2009) demonstrated that event schemas can be automatically induced from text corpora. However, our analysis of their schemas identifies several weaknesses, e.g., some schemas lack a common topic and distinct roles are incorrectly mixed into a single actor. It is due in part to their pair-wise representation that treats subject-verb independently from verb-object. This often leads to subject-verb-object triples that are not meaningful in the real-world. We present a novel approach to inducing open-domain event schemas that overcomes these limitations. Our approach uses cooccurrence statistics of semantically typed relational triples, which we call Rel-grams (relational n-grams). In a human evaluation, our schemas outperform Chambers's schemas by wide margins on several evaluation criteria. Both Rel-grams and event schemas are freely available to the research community.
AB - Chambers and Jurafsky (2009) demonstrated that event schemas can be automatically induced from text corpora. However, our analysis of their schemas identifies several weaknesses, e.g., some schemas lack a common topic and distinct roles are incorrectly mixed into a single actor. It is due in part to their pair-wise representation that treats subject-verb independently from verb-object. This often leads to subject-verb-object triples that are not meaningful in the real-world. We present a novel approach to inducing open-domain event schemas that overcomes these limitations. Our approach uses cooccurrence statistics of semantically typed relational triples, which we call Rel-grams (relational n-grams). In a human evaluation, our schemas outperform Chambers's schemas by wide margins on several evaluation criteria. Both Rel-grams and event schemas are freely available to the research community.
UR - https://www.scopus.com/pages/publications/84926383480
M3 - Conference contribution
AN - SCOPUS:84926383480
T3 - EMNLP 2013 - 2013 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference
SP - 1721
EP - 1731
BT - EMNLP 2013 - 2013 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference
PB - Association for Computational Linguistics (ACL)
T2 - 2013 Conference on Empirical Methods in Natural Language Processing, EMNLP 2013
Y2 - 18 October 2013 through 21 October 2013
ER -