{"title":"Semantic role labeling for Bengali using 5Ws","authors":"Amitava Das, Aniruddha Ghosh, Sivaji Bandyopadhyay","doi":"10.1109/NLPKE.2010.5587772","DOIUrl":null,"url":null,"abstract":"In this paper we present different methodologies to extract semantic role labels of Bengali nouns using 5W distilling. The 5W task seeks to extract the semantic information of nouns in a natural language sentence by distilling it into the answers to the 5W questions: Who, What, When, Where and Why. As Bengali is a resource constraint language, the building of annotated gold standard corpus and acquisition of linguistics tools for features extraction are described in this paper. The tag label wise reported precision values of the present system are: 79.56% (Who), 65.45% (What), 73.35% (When), 77.66% (Where) and 63.50% (Why).","PeriodicalId":259975,"journal":{"name":"Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering(NLPKE-2010)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering(NLPKE-2010)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NLPKE.2010.5587772","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7
Abstract
In this paper we present different methodologies to extract semantic role labels of Bengali nouns using 5W distilling. The 5W task seeks to extract the semantic information of nouns in a natural language sentence by distilling it into the answers to the 5W questions: Who, What, When, Where and Why. As Bengali is a resource constraint language, the building of annotated gold standard corpus and acquisition of linguistics tools for features extraction are described in this paper. The tag label wise reported precision values of the present system are: 79.56% (Who), 65.45% (What), 73.35% (When), 77.66% (Where) and 63.50% (Why).