Abstract
In Semantic Textual Similarity, systems rate the degree of semantic equivalence on a graded scale from 0 to 5, with 5 being the most similar. For the English subtask, we present a system which relies on several resources for token-to-token and phrase-to-phrase similarity to build a data-structure which holds all the information, and then combine the information to get a similarity score. We also participated in the pilot on Interpretable STS, where we apply a pipeline which first aligns tokens, then chunks, and finally uses supervised systems to label and score each chunk alignment.
Cite
CITATION STYLE
Agirre, E., Gonzalez-Agirre, A., Lopez-Gazpio, I., Maritxalar, M., Rigau, G., & Uria, L. (2015). UBC: Cubes for English Semantic Textual Similarity and Supervised Approaches for Interpretable STS. In SemEval 2015 - 9th International Workshop on Semantic Evaluation, co-located with the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2015 - Proceedings (pp. 178–183). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/s15-2032
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