Company Acquisition Relations Extraction From Web Pages

In this chapter, we study the problem of extracting company acquisition relation from huge amounts of webpages, and propose a novel algorithm for a company acquisition relation extraction. Our algorithm considers the tense feature of Web content and classification technology of semantic strength when extracting company acquisition relation from webpages. It first determines the tense of each sentence in a webpage, where a CRF model is employed. Then, the tense of sentences is applied to sentences classification so as to evaluate the semantic strength of the candidate sentences in describing company acquisition relation. After that, we rank the candidate acquisition relations and return the top-k company acquisition relation. We run experiments on 6144 pages crawled through Google, and measure the performance of our algorithm under different metrics. The experimental results show that our algorithm is effective in determining the tense of sentences as well as the company acquisition relation.