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GTE-Rank: A time-aware search engine to answer time-sensitive queries

机译：GTE-Rank：可识别时间的查询的时间感知搜索引擎

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摘要

In the web environment, most of the queries issued by users are implicit by nature. Inferring the different temporal intents of this type of query enhances the overall temporal part of the web search results. Previous works tackling this problem usually focused on news queries, where the retrieval of the most recent results related to the query are usually sufficient to meet the user's information needs. However, few works have studied the importance of time in queries such as "Philip Seymour Hoffman" where the results may require no recency at all. In this work, we focus on this type of queries named "time-sensitive queries" where the results are preferably from a diversified time span, not necessarily the most recent one. Unlike related work, we follow a content-based approach to identify the most important time periods of the query and integrate time into a re-ranking model to boost the retrieval of documents whose contents match the query time period. For that purpose, we define a linear combination of topical and temporal scores, which reflects the relevance of any web document both in the topical and temporal dimensions, thus contributing to improve the effectiveness of the ranked results across different types of queries. Our approach relies on a novel temporal similarity measure that is capable of determining the most important dates for a query, while filtering out the non-relevant ones. Through extensive experimental evaluation over web corpora, we show that our model offers promising results compared to baseline approaches. As a result of our investigation, we publicly provide a set of web services and a web search interface so that the system can be graphically explored by the research community.
机译：在Web环境中，用户发出的大多数查询本质上都是隐式的。推断这种查询的不同时间意图可以增强Web搜索结果的整体时间部分。解决该问题的先前工作通常集中于新闻查询，其中与查询有关的最新结果的检索通常足以满足用户的信息需求。但是，很少有研究在“ Philip Seymour Hoffman”等查询中研究时间的重要性，因为这些查询的结果可能根本不需要新近度。在这项工作中，我们将重点放在这种类型的名为“时间敏感查询”的查询上，其中的结果最好来自多样化的时间跨度，而不一定是最新的。与相关工作不同，我们采用基于内容的方法来识别查询的最重要时间段，并将时间整合到重新排序模型中，以促进内容与查询时间段匹配的文档的检索。为此，我们定义了主题分数和时间分数的线性组合，它反映了主题和时间维度中任何Web文档的相关性，从而有助于提高不同类型查询的排名结果的有效性。我们的方法依靠一种新颖的时间相似性度量，该度量能够确定查询的最重要日期，同时过滤掉不相关的日期。通过对Web语料库的广泛实验评估，我们表明与基线方法相比，我们的模型提供了可喜的结果。作为我们调查的结果，我们公开提供了一组Web服务和Web搜索界面，以便研究社区可以图形方式浏览该系统。

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