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In this paper, the authors study the problem of extracting company acquisition relation from huge amounts of Web pages, and propose a novel algorithm for company acquisition relation extraction. The authors' algorithm considers the tense feature of Web content and classification technology of semantic strength when extracting company acquisition relation from Web pages. It first determines the tense of each sentence in a Web page, which is then applied in sentences classification so as to evaluate the semantic strength of the candidate sentences in describing company acquisition relation. After that, the authors rank the candidate acquisition relations and return the top-k company acquisition relation. They run experiments on 6144 pages crawled through Google, and measure the performance of their algorithm under different metrics. The experimental results show that the algorithm is effective in determining the tense of sentences as well as the company acquisition relation.<\/p>","DOI":"10.4018\/ijswis.2017100102","type":"journal-article","created":{"date-parts":[[2017,9,15]],"date-time":"2017-09-15T13:36:05Z","timestamp":1505482565000},"page":"27-41","source":"Crossref","is-referenced-by-count":2,"title":["Extracting Top-k Company Acquisition Relations From the Web"],"prefix":"10.4018","volume":"13","author":[{"given":"Jie","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Business, Anhui University, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianfei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Business, Anhui University, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jia","family":"Yang","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peiquan","family":"Jin","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"IJSWIS.2017100102-0","first-page":"85","article-title":"Snowball: extracting relations from large plain-text collections","author":"E.Agichtein","year":"2009","journal-title":"Proc. of ICDL"},{"issue":"10","key":"IJSWIS.2017100102-1","first-page":"1297","article-title":"Competitor mining with the web","volume":"20","author":"S.Bao","year":"2010","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"IJSWIS.2017100102-2","first-page":"423","article-title":"Dependency tree kernels for relation extraction.","author":"A.Culotta","year":"2004","journal-title":"Proc. of ACL"},{"key":"IJSWIS.2017100102-3","first-page":"721","article-title":"Evaluating various linguistic features on semantic relation extraction.","author":"M.Garc\u00eda","year":"2011","journal-title":"Proc. of RANLP"},{"key":"IJSWIS.2017100102-4","unstructured":"Googlecode.com. 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