{"title":"利用词典句法模式和词语嵌入自动构建区间值模糊印地语词网","authors":"Minni Jain, Rajni Jindal, Amita Jain","doi":"10.1145/3643132","DOIUrl":null,"url":null,"abstract":"<p>A computational lexicon is the backbone of any language processing system. It helps computers to understand the language complexity as a human does by inculcating words and their semantic associations. Manually constructed famous Hindi WordNet (HWN) consists of various classical semantic relations (crisp relations). To handle uncertainty and represent Hindi WordNet more semantically, Type- 1 fuzzy graphs are applied to relations of Hindi WordNet. But uncertainty in the crisp membership degree is not considered in Type 1 fuzzy set (T1FS). Also collecting billions (5,55,69,51,753 relations in HWN) of membership values from experts (humans) is not feasible. This paper applied the concept of Interval-Valued Fuzzy graphs and proposed Interval- Valued Fuzzy Hindi WordNet (IVFHWN). IVFHWN automatically identifies Interval- Valued Fuzzy relations between words and their degree of membership using word embeddings and lexico-syntactic patterns. The experimental results for the word sense disambiguation problem show better outcomes when IVFHWN is being used in place of Type 1 Fuzzy Hindi WordNet and classical Hindi WordNet.</p>","PeriodicalId":54312,"journal":{"name":"ACM Transactions on Asian and Low-Resource Language Information Processing","volume":"42 1","pages":""},"PeriodicalIF":1.8000,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Automatic Construction of Interval-Valued Fuzzy Hindi WordNet using Lexico-Syntactic Patterns and Word Embeddings\",\"authors\":\"Minni Jain, Rajni Jindal, Amita Jain\",\"doi\":\"10.1145/3643132\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>A computational lexicon is the backbone of any language processing system. It helps computers to understand the language complexity as a human does by inculcating words and their semantic associations. Manually constructed famous Hindi WordNet (HWN) consists of various classical semantic relations (crisp relations). To handle uncertainty and represent Hindi WordNet more semantically, Type- 1 fuzzy graphs are applied to relations of Hindi WordNet. But uncertainty in the crisp membership degree is not considered in Type 1 fuzzy set (T1FS). Also collecting billions (5,55,69,51,753 relations in HWN) of membership values from experts (humans) is not feasible. This paper applied the concept of Interval-Valued Fuzzy graphs and proposed Interval- Valued Fuzzy Hindi WordNet (IVFHWN). IVFHWN automatically identifies Interval- Valued Fuzzy relations between words and their degree of membership using word embeddings and lexico-syntactic patterns. The experimental results for the word sense disambiguation problem show better outcomes when IVFHWN is being used in place of Type 1 Fuzzy Hindi WordNet and classical Hindi WordNet.</p>\",\"PeriodicalId\":54312,\"journal\":{\"name\":\"ACM Transactions on Asian and Low-Resource Language Information Processing\",\"volume\":\"42 1\",\"pages\":\"\"},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2024-02-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACM Transactions on Asian and Low-Resource Language Information Processing\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1145/3643132\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Transactions on Asian and Low-Resource Language Information Processing","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1145/3643132","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Automatic Construction of Interval-Valued Fuzzy Hindi WordNet using Lexico-Syntactic Patterns and Word Embeddings
A computational lexicon is the backbone of any language processing system. It helps computers to understand the language complexity as a human does by inculcating words and their semantic associations. Manually constructed famous Hindi WordNet (HWN) consists of various classical semantic relations (crisp relations). To handle uncertainty and represent Hindi WordNet more semantically, Type- 1 fuzzy graphs are applied to relations of Hindi WordNet. But uncertainty in the crisp membership degree is not considered in Type 1 fuzzy set (T1FS). Also collecting billions (5,55,69,51,753 relations in HWN) of membership values from experts (humans) is not feasible. This paper applied the concept of Interval-Valued Fuzzy graphs and proposed Interval- Valued Fuzzy Hindi WordNet (IVFHWN). IVFHWN automatically identifies Interval- Valued Fuzzy relations between words and their degree of membership using word embeddings and lexico-syntactic patterns. The experimental results for the word sense disambiguation problem show better outcomes when IVFHWN is being used in place of Type 1 Fuzzy Hindi WordNet and classical Hindi WordNet.
期刊介绍：
The ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) publishes high quality original archival papers and technical notes in the areas of computation and processing of information in Asian languages, low-resource languages of Africa, Australasia, Oceania and the Americas, as well as related disciplines. The subject areas covered by TALLIP include, but are not limited to:
-Computational Linguistics: including computational phonology, computational morphology, computational syntax (e.g. parsing), computational semantics, computational pragmatics, etc.
-Linguistic Resources: including computational lexicography, terminology, electronic dictionaries, cross-lingual dictionaries, electronic thesauri, etc.
-Hardware and software algorithms and tools for Asian or low-resource language processing, e.g., handwritten character recognition.
-Information Understanding: including text understanding, speech understanding, character recognition, discourse processing, dialogue systems, etc.
-Machine Translation involving Asian or low-resource languages.
-Information Retrieval: including natural language processing (NLP) for concept-based indexing, natural language query interfaces, semantic relevance judgments, etc.
-Information Extraction and Filtering: including automatic abstraction, user profiling, etc.
-Speech processing: including text-to-speech synthesis and automatic speech recognition.
-Multimedia Asian Information Processing: including speech, image, video, image/text translation, etc.
-Cross-lingual information processing involving Asian or low-resource languages.
-Papers that deal in theory, systems design, evaluation and applications in the aforesaid subjects are appropriate for TALLIP. Emphasis will be placed on the originality and the practical significance of the reported research.
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