{'fraction_non_alphanumeric': 0.0247805885389778, 'fraction_numerical': 0.002839442436757873, 'mean_word_length': 4.7835820895522385, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Finding Answers in Web Search

There are many informational queries that could be answered with a text passage, thereby not requiring the searcher to access the full web document. When building manual annotations of answer passages for TREC queries, Keikha et al. [6] confirmed that many such queries can be answered with just passages. By presenting the answers directly in the search result page, user information needs will be addressed more rapidly so that reduces user interaction (click) with the search result page [3] and gives a significant positive effect on user satisfaction [2, 7]. In the context of general web search, the problem of finding answer passages has not been explored extensively. Retrieving relevant passages has been studied in TREC HARD track [1] and in INEX [5], but relevant passages are not required to contain answers. One of the tasks in the TREC genomics track [4] was to find answer passages on biomedical literature. Previous work has shown that current passage retrieval methods that focus on topical relevance are not effective at finding answers [6]. Therefore, more knowledge is required to identify answers in a document. Bernstein et al. [2] has studied an approach to extract inline direct answers for search result using paid crowdsourcing service. Such an approach, however, is expensive and not practical to be applied for all possible information needs. A fully automatic process in finding answers remains a research challenge. The aim of this thesis is to find passages in the documents that contain answers to a user's query. In this research, we proposed to use a summarization technique through taking advantage of Community Question Answering (CQA) content. In our previous work, we have shown the benefit of using social media to generate more accurate summaries of web documents [8], but this was not designed to present answer in the summary. With the high volume of questions and answers posted in CQA, we believe that there are many questions that have been previously asked in CQA that are the same as or related to actual web queries, for which their best answers can guide us to extract answers in the document. As an initial work, we proposed using term distributions extracted from best answers for top matching questions in one of leading CQA sites, Yahoo! Answers (Y!A), for answer summaries generation. An experiment was done by comparing our summaries with reference answers built in previous work [6], finding some level of success. A manuscript is prepared for this result. Next, as an extension of our work above, we were interested to see whether the documents that have better quality answer summaries should be ranked higher in the result list. A set of features are derived from answer summaries to re-rank documents in the result list. Our experiment shows that answer summaries can be used to improve state-of-the-art document ranking. The method is also shown to outperform a current re-ranking approach using comprehensive document quality features. A manuscript was submitted for this result. For future work, we plan to conduct deeper analysis on top matching questions and their corresponding best answers from Y!A to better understand their benefit to the generated summaries and re-ranking results. For example, how do the results differ on different relevance level of top best answers from Y!A that were used to generate summaries. There are also opportunities to improve the use of Y!A in generating answer summaries, such as by predicting the quality of best answers from Y!A corresponding to the query. We also aim to combine the related Y!A pages into our initial result list when there is a question from Y!A, which is well matched with the query. Next, it is important to think about an approach to generate answer summaries for the queries that do not have related result from CQA.