ICTNET at TREC2017 Complex Answer Retrieval Track

Information retrieval (IR) is finding material (usually documents) of an unstructured nature (usually text) that satisfies an information need from within large collections (usually stored on computers) [1]. The most common and popular information retrieval application is web search engine such as Google[2], Baidu[3], Bing[4] and Sogou[5]. These application will return top-N best retrieval result to users. Information Retrieval systems for the Web, i.e., web search engines, are mainly devoted to finding relevant web documents in response to a user’s query[6]. Current retrieval systems performance well in phrase-level retrieval tasks which provide simple fact and entitycentric needs. Complex Answer Retrieval Track is a new track in 2017, which requests a more complex and longer retrieval result to answer a query. It focuses on developing systems that are capable of answering complex information needs by collating relevant information from an entire corpus. Given an article stub Q, retrieval for each of its sections Hi, a ranking of relevant entity-passage tuples (E, P). Tow tasks are offered: passage ranking and entity ranking. This paper introduces an algorithm and a system for passage ranking. The retrieval queries are outlines which consist of titles and section titles of articles. The retrieval collection consists of paragraphs which are come from Wikipedia articles. We use the BM25 algorithm and develop a system to retrieval the top-100 most relevant paragraphs.