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

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 regressionmodel, 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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Conference:
SEM 2012: The First Joint Conference on Lexical and Computational Semantics, June 7-8, 2012 Montréal, Canada. Sixth International Workshop on Semantic Evaluation (SemEval 2012)
Date Issued:
2012
URN:
urn:nbn:de:hbz:464-20211028-151631-5
DOI:
10.17185/duepublico/72186
Language:
English
Type of Resource:
Text
Collection:
E-Publications
Subject categories of the Deutsche Nationalbibliografie:
004 Data processing Computer sciences
URL:
https://aclanthology.org/S12-1059
Hosting institution:
Faculty of Engineering, Computer Science and Applied Cognitive Science, Computer Science, Language Technology Lab
Info on primary publication:
Bär, D., Biemann, C., Gurevych, I., Zesch, T. (2012) UKP: Computing Semantic Textual Similarity by Combining Multiple Content Similarity Measures.. In: *SEM 2012: The First Joint Conference on Lexical and Computational Semantics.Vol. 2: Proceedings of the Sixth International Workshop on Semantic Evaluation (SemEval 2012), pp. 435–440. Association for Computational Linguistics. https://aclanthology.org/S12-1059

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