Abstract
This paper describes our participation in the SemEval-2016 Task 1: Semantic Textual Similarity (STS). We developed three methods for the English subtask (STS Core). The first method is unsupervised and uses WordNet and word2vec to measure a token-based overlap. In our second approach, we train a neural network on two features. The third method uses word2vec and LDA with regression splines.
Cite
CITATION STYLE
Liebeck, M., Pollack, P., Modaresi, P., & Conrad, S. (2016). HHU at SemEval-2016 Task 1: Multiple approaches to measuring semantic textual similarity. In SemEval 2016 - 10th International Workshop on Semantic Evaluation, Proceedings (pp. 595–601). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/s16-1090
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