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Autor:
Bär, Daniel; Biemann, Chris; Gurevych, Iryna; Zesch, Torsten:

Titel:
UKP: Computing Semantic Textual Similarity by Combining Multiple Content Similarity Measures

Quelle:
In: Agirre, Eneko (Hrsg.): *SEM First Joint Conference on Lexical and Computational Semantics Montreal : ACL (2012) , 435-440

URL des Volltextes:
http://aclweb.org/anthology-new/S/S12/S12-1059.pdf

Sprache:
Englisch

Dokumenttyp:
4. Beiträge in Sammelwerken; Tagungsband/Konferenzbeitrag/Proceedings

Schlagwörter:
Analyse, Computerlinguistik, Semantik, Textanalyse, Verfahren


Abstract(englisch):
We present the UKP system which performed best in the Semantic Textual Similarity (STS) task at SemEval-2012 in two out of three metrics. It uses a simple log-linear regression model, trained on the training data, to combine multiple text similarity measures of varying complexity. These range from simple character and word n-grams and common subsequences to complex features such as Explicit Semantic Analysis vector comparisons and aggregation of word similarity based on lexical-semantic resources. Further, we employ a lexical substitution system and statistical machine translation to add additional lexemes, which alleviates lexical gaps. Our final models, one per dataset, consist of a log-linear combination of about 20 features, out of the possible 300+ features implemented.


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last modified Nov 11, 2016