Benikova, Darina ; Biemann, Chris (2016)
SemRelData – Multilingual Contextual Annotation of Semantic Relations between Nominals: Dataset and Guidelines.
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2016 |
| Autor(en): | Benikova, Darina ; Biemann, Chris |
| Art des Eintrags: | Bibliographie |
| Titel: | SemRelData – Multilingual Contextual Annotation of Semantic Relations between Nominals: Dataset and Guidelines |
| Sprache: | Deutsch |
| Publikationsjahr: | Mai 2016 |
| Verlag: | ELRA |
| Buchtitel: | Proceedings Tenth International Conference on Language Resources and Evaluation (LREC 2016) |
| Zugehörige Links: | |
| Kurzbeschreibung (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. |
| ID-Nummer: | TUD-CS-2016-0063 |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik > Sprachtechnologie 20 Fachbereich Informatik |
| Hinterlegungsdatum: | 31 Dez 2016 09:42 |
| Letzte Änderung: | 30 Mai 2018 12:51 |
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