{"title":"从学术研究论文中探索进化技术趋势","authors":"Teng-Kai Fan, Chia-Hui Chang","doi":"10.1109/DAS.2008.25","DOIUrl":null,"url":null,"abstract":"Automatic Term Recognition (ATR) is concerned with discovering terminology in large volumes of text corpora. Technical terms are vital elements for understanding the techniques used in academic research papers, and in this paper, we use focused technical terms to explore technical trends in the research literature. The major purpose of this work is to understand the relationship between techniques and research topics to better explore technical trends. We define this new text mining issue and apply machine learning algorithms for solving this problem by (1) recognizing focused technical terms from research papers; (2) classifying these terms into predefined technology categories; (3) analyzing the evolution of technical trends. The dataset consists of 656 papers collected from well-known conferences on ACM. The experimental results indicate that our proposed methods can effectively explore interesting evolutionary technical trends in various research topics.","PeriodicalId":423207,"journal":{"name":"2008 The Eighth IAPR International Workshop on Document Analysis Systems","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":"{\"title\":\"Exploring Evolutionary Technical Trends from Academic Research Papers\",\"authors\":\"Teng-Kai Fan, Chia-Hui Chang\",\"doi\":\"10.1109/DAS.2008.25\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Automatic Term Recognition (ATR) is concerned with discovering terminology in large volumes of text corpora. Technical terms are vital elements for understanding the techniques used in academic research papers, and in this paper, we use focused technical terms to explore technical trends in the research literature. The major purpose of this work is to understand the relationship between techniques and research topics to better explore technical trends. We define this new text mining issue and apply machine learning algorithms for solving this problem by (1) recognizing focused technical terms from research papers; (2) classifying these terms into predefined technology categories; (3) analyzing the evolution of technical trends. The dataset consists of 656 papers collected from well-known conferences on ACM. The experimental results indicate that our proposed methods can effectively explore interesting evolutionary technical trends in various research topics.\",\"PeriodicalId\":423207,\"journal\":{\"name\":\"2008 The Eighth IAPR International Workshop on Document Analysis Systems\",\"volume\":\"2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-09-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"14\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 The Eighth IAPR International Workshop on Document Analysis Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DAS.2008.25\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 The Eighth IAPR International Workshop on Document Analysis Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DAS.2008.25","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Exploring Evolutionary Technical Trends from Academic Research Papers
Automatic Term Recognition (ATR) is concerned with discovering terminology in large volumes of text corpora. Technical terms are vital elements for understanding the techniques used in academic research papers, and in this paper, we use focused technical terms to explore technical trends in the research literature. The major purpose of this work is to understand the relationship between techniques and research topics to better explore technical trends. We define this new text mining issue and apply machine learning algorithms for solving this problem by (1) recognizing focused technical terms from research papers; (2) classifying these terms into predefined technology categories; (3) analyzing the evolution of technical trends. The dataset consists of 656 papers collected from well-known conferences on ACM. The experimental results indicate that our proposed methods can effectively explore interesting evolutionary technical trends in various research topics.
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