{"title":"Multi-role identification of sentences using Relevance Vector Space","authors":"A. Prayote, Watcharet Kuntichod","doi":"10.1109/JCSSE.2017.8025946","DOIUrl":null,"url":null,"abstract":"This paper presents a solution to classifying sentences with multi-labels. This problem is an essential part to a semantic search process. Sentences or keywords with correctly automated labelling can enhance the efficiency and performance of the search. The technique introduces a vector space of relevance for keywords and sentences with necessary operations. Concepts and motivation are explained with mathematical models for the two vectors and their operations. Experiments of multi-role identification of sentences are conducted with abstracts of scientific articles. Procedures of executing models in the experiment are explained step by step. In comparison, nine other techniques of multi-label classification are used on the same data set. The evaluation is done on 4-fold cross validation basis. The study reveals a successful result of multi-role identification of sentences with higher accuracy than other nine techniques.","PeriodicalId":6460,"journal":{"name":"2017 14th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"49 1","pages":"1-6"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 14th International Joint Conference on Computer Science and Software Engineering (JCSSE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/JCSSE.2017.8025946","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This paper presents a solution to classifying sentences with multi-labels. This problem is an essential part to a semantic search process. Sentences or keywords with correctly automated labelling can enhance the efficiency and performance of the search. The technique introduces a vector space of relevance for keywords and sentences with necessary operations. Concepts and motivation are explained with mathematical models for the two vectors and their operations. Experiments of multi-role identification of sentences are conducted with abstracts of scientific articles. Procedures of executing models in the experiment are explained step by step. In comparison, nine other techniques of multi-label classification are used on the same data set. The evaluation is done on 4-fold cross validation basis. The study reveals a successful result of multi-role identification of sentences with higher accuracy than other nine techniques.