Improving Semantic Similarity with Cross-Lingual Resources: A Study in Bangla—A Low Resourced Language
| Autor: | Mohini Mohan Sardar, Sudip Kumar Naskar, Saptarshi Sengupta, Rajat Pandit, Niladri Sekhar Dash |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
semantic similarity
Computer Networks and Communications Process (engineering) Computer science WordNet translation 02 engineering and technology computer.software_genre Semantic similarity 0202 electrical engineering electronic engineering information engineering Word2vec Word2Vec low-resource languages lcsh:T58.5-58.64 lcsh:Information technology business.industry Communication 020207 software engineering language.human_language Human-Computer Interaction Bengali Path (graph theory) language 020201 artificial intelligence & image processing Artificial intelligence business computer Word (computer architecture) Natural language processing Meaning (linguistics) |
| Zdroj: | Informatics Volume 6 Issue 2 Informatics, Vol 6, Iss 2, p 19 (2019) |
| ISSN: | 2227-9709 |
| DOI: | 10.3390/informatics6020019 |
| Popis: | Semantic similarity is a long-standing problem in natural language processing (NLP). It is a topic of great interest as its understanding can provide a look into how human beings comprehend meaning and make associations between words. However, when this problem is looked at from the viewpoint of machine understanding, particularly for under resourced languages, it poses a different problem altogether. In this paper, semantic similarity is explored in Bangla, a less resourced language. For ameliorating the situation in such languages, the most rudimentary method (path-based) and the latest state-of-the-art method (Word2Vec) for semantic similarity calculation were augmented using cross-lingual resources in English and the results obtained are truly astonishing. In the presented paper, two semantic similarity approaches have been explored in Bangla, namely the path-based and distributional model and their cross-lingual counterparts were synthesized in light of the English WordNet and Corpora. The proposed methods were evaluated on a dataset comprising of 162 Bangla word pairs, which were annotated by five expert raters. The correlation scores obtained between the four metrics and human evaluation scores demonstrate a marked enhancement that the cross-lingual approach brings into the process of semantic similarity calculation for Bangla. |
| Databáze: | OpenAIRE |
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