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SemRelData – Multilingual Contextual Annotation of Semantic Relations between Nominals: Dataset and Guidelines

Benikova, Darina and Biemann, Chris (2016):
SemRelData – Multilingual Contextual Annotation of Semantic Relations between Nominals: Dataset and Guidelines.
In: Proceedings Tenth International Conference on Language Resources and Evaluation (LREC 2016),
ELRA, [Conference or Workshop Item]

Abstract

Semantic relations play an important role in linguistic knowledge representation. Although their role is relevant in the context of written text, there is no approach or dataset that makes use of contextuality of classic semantic relations beyond the boundary of one sentence. We present the SemRelData dataset that contains annotations of semantic relations between nominals in the context of one paragraph. To be able to analyse the universality of this context notion, the annotation was performed on a multi-lingual and multi-genre corpus. To evaluate the dataset, it is compared to large, manually created knowledge resources in the respective languages. The comparison shows that knowledge bases not only have coverage gaps; they also do not account for semantic relations that are manifested in particular contexts only, yet still play an important role for text cohesion.

Item Type: Conference or Workshop Item
Erschienen: 2016
Creators: Benikova, Darina and Biemann, Chris
Title: SemRelData – Multilingual Contextual Annotation of Semantic Relations between Nominals: Dataset and Guidelines
Language: German
Abstract:

Semantic relations play an important role in linguistic knowledge representation. Although their role is relevant in the context of written text, there is no approach or dataset that makes use of contextuality of classic semantic relations beyond the boundary of one sentence. We present the SemRelData dataset that contains annotations of semantic relations between nominals in the context of one paragraph. To be able to analyse the universality of this context notion, the annotation was performed on a multi-lingual and multi-genre corpus. To evaluate the dataset, it is compared to large, manually created knowledge resources in the respective languages. The comparison shows that knowledge bases not only have coverage gaps; they also do not account for semantic relations that are manifested in particular contexts only, yet still play an important role for text cohesion.

Title of Book: Proceedings Tenth International Conference on Language Resources and Evaluation (LREC 2016)
Publisher: ELRA
Divisions: 20 Department of Computer Science > Sprachtechnologie
20 Department of Computer Science
Date Deposited: 31 Dec 2016 09:42
Identification Number: TUD-CS-2016-0063
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