{"title":"基于混合深度学习方法的越南语关键词提取","authors":"Bui Thanh Hung","doi":"10.1109/NICS.2018.8606906","DOIUrl":null,"url":null,"abstract":"Keywords provide a short way of reflecting a main idea of the document, making it easier for the readers in reading. Extracting keyword is the main task in natural language processing. Since it is not only time consuming but also requires lots of efforts to extract the keywords manually, it arises the need for the automated approaches. This paper has proposed a solution for the automatic keyword extraction in Vietnamese language using hybrid deep learning approaches. Every existing deep learning approach has its own advantages; and the hybrid deep learning model we are introducing is the combination of the superior features of CNN and LSTM models. The proposed model shows enhanced accuracy and f1-score over another approach.","PeriodicalId":137666,"journal":{"name":"2018 5th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"515 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Vietnamese Keyword Extraction Using Hybrid Deep Learning Methods\",\"authors\":\"Bui Thanh Hung\",\"doi\":\"10.1109/NICS.2018.8606906\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Keywords provide a short way of reflecting a main idea of the document, making it easier for the readers in reading. Extracting keyword is the main task in natural language processing. Since it is not only time consuming but also requires lots of efforts to extract the keywords manually, it arises the need for the automated approaches. This paper has proposed a solution for the automatic keyword extraction in Vietnamese language using hybrid deep learning approaches. Every existing deep learning approach has its own advantages; and the hybrid deep learning model we are introducing is the combination of the superior features of CNN and LSTM models. The proposed model shows enhanced accuracy and f1-score over another approach.\",\"PeriodicalId\":137666,\"journal\":{\"name\":\"2018 5th NAFOSTED Conference on Information and Computer Science (NICS)\",\"volume\":\"515 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 5th NAFOSTED Conference on Information and Computer Science (NICS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NICS.2018.8606906\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 5th NAFOSTED Conference on Information and Computer Science (NICS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NICS.2018.8606906","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Vietnamese Keyword Extraction Using Hybrid Deep Learning Methods
Keywords provide a short way of reflecting a main idea of the document, making it easier for the readers in reading. Extracting keyword is the main task in natural language processing. Since it is not only time consuming but also requires lots of efforts to extract the keywords manually, it arises the need for the automated approaches. This paper has proposed a solution for the automatic keyword extraction in Vietnamese language using hybrid deep learning approaches. Every existing deep learning approach has its own advantages; and the hybrid deep learning model we are introducing is the combination of the superior features of CNN and LSTM models. The proposed model shows enhanced accuracy and f1-score over another approach.