Multi-corpus-Based Model for Measuring the Semantic Relatedness in Short Texts (SRST)

摘要

Semantic Relatedness (SR) defines a relation between linguistic items. These items could be words, phrases, or documents. There are many interesting related applications such as information extraction, words sense disambiguation, text summarization, and text clustering. The task of quantifying SR manually is fairly natural and axiomatic, whereas it is complex automatically because of human's background experience and external domain concepts that are not available for the computational methods. This paper focuses on the Semantic Relatedness in Short Texts (SRST). A Vector Space Modelthat is based on multi-corpusis proposed to measure the SRST. Word synonyms and anaphoric information are used to improve the semantic representation of the document. Since the set of verses in the Holy Quran is a precious sample of the short texts., it is used as the main case study in this paper to measure the degree of relatedness between these verses. Experiments are conducted where their results proved the efficiency of the proposed model in improving SR measurement. The results show an improvement to the recall to be 60% rather than 11.3% as the best previous studies.