Abstract
There is much research on Semantic Textual Similarity (STS) in English, specially since its inclusion in the SemEval evaluations. For other languages, it is not as common, mostly due to the unavailability of benchmarks. Recently, the ASSIN shared task targeted STS in Portuguese and released training and test collections. This paper describes an incremental approach to ASSIN, where the computed similarity is gradually improved by exploiting different features (e.g., token overlap, semantic relations, chunks, and negation) and approaches. The best reported results, obtained with a supervised approach, would get second place overall in ASSIN.
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Notes
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NLP tools available from https://github.com/rikarudo/.
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PTStemmer is available from https://code.google.com/archive/p/ptstemmer/.
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Acknowledgements
This work was financed by the ERDF European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within project REMINDS – UTAP-ICDT/EEI-CTP/0022/2014.
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Gonçalo Oliveira, H., Oliveira Alves, A., Rodrigues, R. (2017). Gradually Improving the Computation of Semantic Textual Similarity in Portuguese. In: Oliveira, E., Gama, J., Vale, Z., Lopes Cardoso, H. (eds) Progress in Artificial Intelligence. EPIA 2017. Lecture Notes in Computer Science(), vol 10423. Springer, Cham. https://doi.org/10.1007/978-3-319-65340-2_68
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