{"title":"基于图形的关键短语提取,使用Word和文档Em寝具*","authors":"Xian Zu, Fei Xie, Xiaojian Liu","doi":"10.1109/ICBK50248.2020.00020","DOIUrl":null,"url":null,"abstract":"With the increasing amount of text data in applications, the task of keyphrase extraction receives more attention that aims to extract concise and important information from a document. In this paper, we propose a novel graph-based keyphrase extraction method using word and document embedding vectors. Two graph construction schemes named GKE-w and GKE-p are designed in which candidate words and phrases are represented as nodes respectively. By calculating the similarity between a word/phrase and the document, each node is assigned an initial weight that reflects the preference to be a keyphrase. Then, we calculate the score of each candidate word/phrase using a semantic biased random walk strategy. Finally, the Top N scored candidate phrases are selected as the final keyphrases. Experiments on two widely used datasets show that the proposed keyphrase extraction algorithm outperforms the state-of-the-art keyphrase extraction methods in terms of precision, recall, and F1 measures.","PeriodicalId":432857,"journal":{"name":"2020 IEEE International Conference on Knowledge Graph (ICKG)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Graph-based Keyphrase Extraction Using Word and Document Em beddings*\",\"authors\":\"Xian Zu, Fei Xie, Xiaojian Liu\",\"doi\":\"10.1109/ICBK50248.2020.00020\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With the increasing amount of text data in applications, the task of keyphrase extraction receives more attention that aims to extract concise and important information from a document. In this paper, we propose a novel graph-based keyphrase extraction method using word and document embedding vectors. Two graph construction schemes named GKE-w and GKE-p are designed in which candidate words and phrases are represented as nodes respectively. By calculating the similarity between a word/phrase and the document, each node is assigned an initial weight that reflects the preference to be a keyphrase. Then, we calculate the score of each candidate word/phrase using a semantic biased random walk strategy. Finally, the Top N scored candidate phrases are selected as the final keyphrases. Experiments on two widely used datasets show that the proposed keyphrase extraction algorithm outperforms the state-of-the-art keyphrase extraction methods in terms of precision, recall, and F1 measures.\",\"PeriodicalId\":432857,\"journal\":{\"name\":\"2020 IEEE International Conference on Knowledge Graph (ICKG)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE International Conference on Knowledge Graph (ICKG)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICBK50248.2020.00020\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE International Conference on Knowledge Graph (ICKG)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICBK50248.2020.00020","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Graph-based Keyphrase Extraction Using Word and Document Em beddings*
With the increasing amount of text data in applications, the task of keyphrase extraction receives more attention that aims to extract concise and important information from a document. In this paper, we propose a novel graph-based keyphrase extraction method using word and document embedding vectors. Two graph construction schemes named GKE-w and GKE-p are designed in which candidate words and phrases are represented as nodes respectively. By calculating the similarity between a word/phrase and the document, each node is assigned an initial weight that reflects the preference to be a keyphrase. Then, we calculate the score of each candidate word/phrase using a semantic biased random walk strategy. Finally, the Top N scored candidate phrases are selected as the final keyphrases. Experiments on two widely used datasets show that the proposed keyphrase extraction algorithm outperforms the state-of-the-art keyphrase extraction methods in terms of precision, recall, and F1 measures.