生成广告中问题的精确和分级部分匹配答案
Generating Exact- and Ranked Partially-Matched Answers to Questions in Advertisements
摘要：
利用Web，许多广告（简称广告）网站，希望增加客户的交易，从而增加利润，提供搜索工具，允许用户（i）发布关键字查询，以捕捉他们的信息需求或（ii）调用基于表单的界面，通过选择搜索选项，如价格范围，填写条目，复选框，或下拉菜单创建查询。然而，这些搜索机制是不够的，因为它们不能被用来指定一个自然语言查询具有丰富的语法和语义的内容，这只能由一个问答（QA）系统处理。此外，现有的广告网站不能评估任意布尔查询或检索用户可能感兴趣的部分匹配的答案，只要用户的搜索只产生几个或根本没有结果。在解决这些问题时，我们提出了一个用于广告的QA系统，称为CQAds，其（i）允许用户发布用于检索相关广告的自然语言问题Q，如果它们存在的话，（ii）将广告识别为部分匹配以Q表示的所请求信息的答案，如果可以检索到对Q的不充分或没有答案，则使用相似性排名方法对其进行排序，以及（iii）分析不完整或模糊的问题，以在检索与Q中指定的选择标准“最佳匹配”的答案时执行“最佳猜测”。CQAds还配备有布尔模型来评估在Q中显式或隐式指定的布尔运算符，即，分别使用或不使用用户指定的布尔运算符。CQAds易于使用，可扩展到所有广告领域，并且比现有广告网站提供的搜索工具更强大，因为其查询处理策略检索更高质量和数量的相关广告。我们已经验证了CQAds在八个广告域中检索广告的准确性，并将其与其他广告域进行了比较。[截断]。
英文摘要：
Taking advantage of the Web, many advertisements (ads for short) websites, which aspire to increase client's transactions and thus profits, offer searching tools which allow users to (i) post keyword queries to capture their information needs or (ii) invoke form-based interfaces to create queries by selecting search options, such as a price range, filled-in entries, check boxes, or drop-down menus. These search mechanisms, however, are inadequate, since they cannot be used to specify a natural-language query with rich syntactic and semantic content, which can only be handled by a question answering (QA) system. Furthermore, existing ads websites are incapable of evaluating arbitrary Boolean queries or retrieving partiallymatched answers that might be of interest to the user whenever a user's search yields only a few or no results at all. In solving these problems, we present a QA system for ads, called CQAds, which (i) allows users to post a natural-language question Q for retrieving relevant ads, if they exist, (ii) identifies ads as answers that partially-match the requested information expressed in Q, if insufficient or no answers to Q can be retrieved, which are ordered using a similarity-ranking approach, and (iii) analyzes incomplete or ambiguous questions to perform the "best guess" in retrieving answers that "best match" the selection criteria specified in Q. CQAds is also equipped with a Boolean model to evaluate Boolean operators that are either explicitly or implicitly specified in Q, i.e., with or without Boolean operators specified by the users, respectively. CQAds is easy to use, scalable to all ads domains, and more powerful than search tools provided by existing ads websites, since its query-processing strategy retrieves relevant ads of higher quality and quantity. We have verified the accuracy of CQAds in retrieving ads on eight ads domains and compared it...[truncated].
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
