Counting What Counts: Decompounding for Keyphrase Extraction

A core assumption of keyphrase extraction is that a concept is more important if it is mentioned more often in a document. Especially in languages like German that form large noun compounds, frequency counts might be misleading as concepts “hidden” in compounds are not counted. We hypothesize that using decompounding before counting term frequencies may lead to better keyphrase extraction. We identified two effects of decompounding: (i) enhanced frequency counts, and (ii) more keyphrase candidates. We created two German evaluation datasets to test our hypothesis and analyzed the effect of additional decompounding for keyphrase extraction.

Vorschau

Einordnung

Konferenz:
ACL 2015 Workshop on Novel Computational Approaches to Keyphrase Extraction, July 30, 2015 Beijing, China
Datum der Veröffentlichung:
2015
URN:
urn:nbn:de:hbz:464-20211022-111641-1
DOI:
10.18653/v1/W15-3603
Sprache:
Englisch
Ressourcentyp:
Text
Kollektion:
E-Publikationen
Sachgruppen der Deutschen Nationalbibliographie:
004 Informatik
Link URL:
https://www.aclweb.org/anthology/W15-3603
Einrichtung:
Fakultät für Ingenieurwissenschaften, Informatik und Angewandte Kognitionswissenschaft, Informatik, Sprachtechnologie
Informationen zur Erstveröffentlichung:
Erbs, N., Santos, P.B., Zesch, T., Gurevych, I. (2015). Counting What Counts: Decompounding for Keyphrase Extraction. In: Proceedings of the ACL 2015 Workshop on Novel Computational Approaches to Keyphrase Extraction, pp. 10-17. Association for Computational Linguistics. https://doi.org/10.18653/v1/W15-3603

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