Abstract
In this paper, we present a method for measuring semantic similarity between short texts by combining two different kinds of features: (1) distributed representation of word, (2) knowledge-based and corpus-based metrics. Then, we present experiments to evaluate our method on two popular datasets - Microsoft Research Paraphrase Corpus and SemEval-2015. The experimental results show that our method achieves state-of-the-art performance.
Cite
CITATION STYLE
Duong, P. H., Nguyen, H. T., & Huynh, N. T. (2016). Measuring similarity for short texts on social media. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9795, pp. 249–259). Springer Verlag. https://doi.org/10.1007/978-3-319-42345-6_22
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