Evaluating semantic similarity in short texts using related context and DISCO
DOI:
https://doi.org/10.30973/progmat/2019.11.1/1Keywords:
distributional hypothesis, mutual information, natural language processing, related context, semantic similarityAbstract
Measuring the degree of semantic similarity between texts or concepts is a challenge task and important in several applications in Information Retrieval and Natural Language Processing. Given the importance of the task, a method is proposed to measure the semantic similarity between a pair of sentences using the technique Distributional Hypothesis to extract from the web related contexts to the training set. The related contexts are an important component to calculate the semantic similarity between pairs of sentences. The article presents the results obtained from a standard training set. The empirical evaluation shows that the proposed approach exceeds the baseline, as well as some methods previously proposed in the standard training set.
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