@inproceedings{fialho-etal-2017-l2f,
title = "{L}2{F}/{INESC}-{ID} at {S}em{E}val-2017 Tasks 1 and 2: Lexical and semantic features in word and textual similarity",
author = "Fialho, Pedro and
Patinho Rodrigues, Hugo and
Coheur, Lu{\'\i}sa and
Quaresma, Paulo",
editor = "Bethard, Steven and
Carpuat, Marine and
Apidianaki, Marianna and
Mohammad, Saif M. and
Cer, Daniel and
Jurgens, David",
booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S17-2032",
doi = "10.18653/v1/S17-2032",
pages = "213--219",
abstract = "This paper describes our approach to the SemEval-2017 {``}Semantic Textual Similarity{''} and {``}Multilingual Word Similarity{''} tasks. In the former, we test our approach in both English and Spanish, and use a linguistically-rich set of features. These move from lexical to semantic features. In particular, we try to take advantage of the recent Abstract Meaning Representation and SMATCH measure. Although without state of the art results, we introduce semantic structures in textual similarity and analyze their impact. Regarding word similarity, we target the English language and combine WordNet information with Word Embeddings. Without matching the best systems, our approach proved to be simple and effective.",
}
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%0 Conference Proceedings
%T L2F/INESC-ID at SemEval-2017 Tasks 1 and 2: Lexical and semantic features in word and textual similarity
%A Fialho, Pedro
%A Patinho Rodrigues, Hugo
%A Coheur, Luísa
%A Quaresma, Paulo
%Y Bethard, Steven
%Y Carpuat, Marine
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y Cer, Daniel
%Y Jurgens, David
%S Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F fialho-etal-2017-l2f
%X This paper describes our approach to the SemEval-2017 “Semantic Textual Similarity” and “Multilingual Word Similarity” tasks. In the former, we test our approach in both English and Spanish, and use a linguistically-rich set of features. These move from lexical to semantic features. In particular, we try to take advantage of the recent Abstract Meaning Representation and SMATCH measure. Although without state of the art results, we introduce semantic structures in textual similarity and analyze their impact. Regarding word similarity, we target the English language and combine WordNet information with Word Embeddings. Without matching the best systems, our approach proved to be simple and effective.
%R 10.18653/v1/S17-2032
%U https://aclanthology.org/S17-2032
%U https://doi.org/10.18653/v1/S17-2032
%P 213-219
Markdown (Informal)
[L2F/INESC-ID at SemEval-2017 Tasks 1 and 2: Lexical and semantic features in word and textual similarity](https://aclanthology.org/S17-2032) (Fialho et al., SemEval 2017)
ACL