TEG-REP: A corpus of Textual Entailment Graphs based on Relation Extraction Patterns

Kathrin Eichler, Feiyu Xu, Hans Uszkoreit, Leonhard Hennig, Sebastian Krause

Abstract
The task of relation extraction is to recognize and extract relations between entities or concepts in texts. Dependency parse trees have become a popular source for discovering extraction patterns, which encode the grammatical relations among the phrases that jointly express relation instances. State-of-the-art weakly supervised approaches to relation extraction typically extract thousands of unique patterns only potentially expressing the target relation. Among these patterns, some are semantically equivalent, but differ in their morphological, lexical-semantic or syntactic form. Some express a relation that entails the target relation. We propose a new approach to structuring extraction patterns by utilizing entailment graphs, hierarchical structures representing entailment relations, and present a novel resource of gold-standard entailment graphs based on a set of patterns automatically acquired using distant supervision. We describe the methodology used for creating the dataset and present statistics of the resource as well as an analysis of inference types underlying the entailment decisions.
Anthology ID:
L16-1537
Volume:
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)
Month:
May
Year:
2016
Address:
Portorož, Slovenia
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
3367–3372
Language:
URL:
https://aclanthology.org/L16-1537
DOI:
Bibkey:
Cite (ACL):
Kathrin Eichler, Feiyu Xu, Hans Uszkoreit, Leonhard Hennig, and Sebastian Krause. 2016. TEG-REP: A corpus of Textual Entailment Graphs based on Relation Extraction Patterns. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 3367–3372, Portorož, Slovenia. European Language Resources Association (ELRA).
Cite (Informal):
TEG-REP: A corpus of Textual Entailment Graphs based on Relation Extraction Patterns (Eichler et al., LREC 2016)
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https://preview.aclanthology.org/paclic-22-ingestion/L16-1537.pdf
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