{"title":"从自然语言文档中挖掘共性和可变性","authors":"Alessio Ferrari, G. Spagnolo, F. Dell’Orletta","doi":"10.1145/2491627.2491634","DOIUrl":null,"url":null,"abstract":"A company who wishes to enter an established marked with a new, competitive product is required to analyse the product solutions of the competitors. Identifying and comparing the features provided by the other vendors might greatly help during the market analysis. However, mining common and variant features of from the publicly available documents of the competitors is a time consuming and error-prone task. In this paper, we suggest to employ a natural language processing approach based on contrastive analysis to identify commonalities and variabilities from the brochures of a group of vendors. We present a first step towards a practical application of the approach, in the the context of the market of Communications-Based Train Control (CBTC) systems.","PeriodicalId":339444,"journal":{"name":"Software Product Lines Conference","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"74","resultStr":"{\"title\":\"Mining commonalities and variabilities from natural language documents\",\"authors\":\"Alessio Ferrari, G. Spagnolo, F. Dell’Orletta\",\"doi\":\"10.1145/2491627.2491634\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A company who wishes to enter an established marked with a new, competitive product is required to analyse the product solutions of the competitors. Identifying and comparing the features provided by the other vendors might greatly help during the market analysis. However, mining common and variant features of from the publicly available documents of the competitors is a time consuming and error-prone task. In this paper, we suggest to employ a natural language processing approach based on contrastive analysis to identify commonalities and variabilities from the brochures of a group of vendors. We present a first step towards a practical application of the approach, in the the context of the market of Communications-Based Train Control (CBTC) systems.\",\"PeriodicalId\":339444,\"journal\":{\"name\":\"Software Product Lines Conference\",\"volume\":\"22 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-08-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"74\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Software Product Lines Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2491627.2491634\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Software Product Lines Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2491627.2491634","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Mining commonalities and variabilities from natural language documents
A company who wishes to enter an established marked with a new, competitive product is required to analyse the product solutions of the competitors. Identifying and comparing the features provided by the other vendors might greatly help during the market analysis. However, mining common and variant features of from the publicly available documents of the competitors is a time consuming and error-prone task. In this paper, we suggest to employ a natural language processing approach based on contrastive analysis to identify commonalities and variabilities from the brochures of a group of vendors. We present a first step towards a practical application of the approach, in the the context of the market of Communications-Based Train Control (CBTC) systems.