2017
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Generating Pattern-Based Entailment Graphs for Relation Extraction
Kathrin Eichler
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Feiyu Xu
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Hans Uszkoreit
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Sebastian Krause
Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)
Relation extraction is the task of recognizing and extracting relations between entities or concepts in texts. A common approach is to exploit existing knowledge to learn linguistic patterns expressing the target relation and use these patterns for extracting new relation mentions. Deriving relation patterns automatically usually results in large numbers of candidates, which need to be filtered to derive a subset of patterns that reliably extract correct relation mentions. We address the pattern selection task by exploiting the knowledge represented by entailment graphs, which capture semantic relationships holding among the learned pattern candidates. This is motivated by the fact that a pattern may not express the target relation explicitly, but still be useful for extracting instances for which the relation holds, because its meaning entails the meaning of the target relation. We evaluate the usage of both automatically generated and gold-standard entailment graphs in a relation extraction scenario and present favorable experimental results, exhibiting the benefits of structuring and selecting patterns based on entailment graphs.
2016
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TEG-REP: A corpus of Textual Entailment Graphs based on Relation Extraction Patterns
Kathrin Eichler
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Feiyu Xu
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Hans Uszkoreit
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Leonhard Hennig
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Sebastian Krause
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)
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.
2015
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Multi-Level Alignments As An Extensible Representation Basis for Textual Entailment Algorithms
Tae-Gil Noh
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Sebastian Padó
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Vered Shwartz
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Ido Dagan
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Vivi Nastase
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Kathrin Eichler
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Lili Kotlerman
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Meni Adler
Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics
2014
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The Excitement Open Platform for Textual Inferences
Bernardo Magnini
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Roberto Zanoli
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Ido Dagan
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Kathrin Eichler
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Guenter Neumann
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Tae-Gil Noh
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Sebastian Pado
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Asher Stern
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Omer Levy
Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations
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An analysis of textual inference in German customer emails
Kathrin Eichler
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Aleksandra Gabryszak
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Günter Neumann
Proceedings of the Third Joint Conference on Lexical and Computational Semantics (*SEM 2014)
2010
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DFKI KeyWE: Ranking Keyphrases Extracted from Scientific Articles
Kathrin Eichler
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Günter Neumann
Proceedings of the 5th International Workshop on Semantic Evaluation
2008
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Unsupervised Relation Extraction From Web Documents
Kathrin Eichler
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Holmer Hemsen
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Günter Neumann
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)
The IDEX system is a prototype of an interactive dynamic Information Extraction (IE) system. A user of the system expresses an information request in the form of a topic description, which is used for an initial search in order to retrieve a relevant set of documents. On basis of this set of documents, unsupervised relation extraction and clustering is done by the system. The results of these operations can then be interactively inspected by the user. In this paper we describe the relation extraction and clustering components of the IDEX system. Preliminary evaluation results of these components are presented and an overview is given of possible enhancements to improve the relation extraction and clustering components.