Automatic Generation of Synsets for Wordnet of Hindi Language

Priyanka Pandey, Manju Khari, Raghavendra Kumar, Dac-Nhuong Le
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引用次数: 1

Abstract

India is a land of 122 languages and numerous dialects. Lack of competent lexical resources for Indian languages is a ubiquitous fact, which negatively affects the development of tools for NLP of Indian languages. Recent advancements like the Indo WordNet project has significantly contributed to dealing with the scarcity of lexicons, but the progress and coverage is a matter of dispute. The bottlenecks, cost, time, and skilled lexicographers further slackens the progress. In this article, the authors propose a technique to automate the generation of lexical entries using a machine learning approach which visibly expedites the process of lexicon generation like WordNet. The reluctance to adopt an automated approach is majorly credited to a lack of accuracy, the inability to capture a regional touch of a language, incorrect back-translation, etc. To overcome this issue, the author will use Wikipedia to validate the synsets.
印地语Wordnet词集的自动生成
印度是一个有122种语言和众多方言的国家。印度语言缺乏合适的词汇资源是一个普遍存在的事实,这对印度语言自然语言处理工具的发展产生了负面影响。最近的进展,如Indo WordNet项目,对处理词汇的稀缺性做出了重大贡献,但进展和覆盖范围是一个有争议的问题。瓶颈、成本、时间和熟练的词典编纂者进一步减缓了进展。在这篇文章中,作者提出了一种使用机器学习方法自动生成词汇条目的技术,这种方法明显加快了像WordNet这样的词汇生成过程。不愿意采用自动化方法的主要原因是缺乏准确性,无法捕捉语言的区域接触,不正确的反向翻译等。为了克服这个问题,作者将使用Wikipedia来验证同义词集。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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