SGER: Breaking the Keyword Bottleneck: Towards More Effective Access of Government Information

大量的政府信息可以在网上获得。然而，载有这些信息的网站往往极其复杂，难以浏览。在这种类型的环境中，简单的查询对定位相关信息没有多大帮助。然而，当前的信息检索（IR）领域主要是简单的查询，因为这是Web搜索引擎擅长的。这些简单的查询通常可以帮助用户找到一个好的主页。然而，就网上的政府信息而言，主页往往无助于找到正确的答案，反而需要用户付出相当多的额外努力。大多数信息需求将更好地表示为复杂的查询，目前的系统强加了一个瓶颈，用户被迫使用基于关键字的简单查询。某些类型的专业搜索人员（例如情报分析师，律师）确实制定了更长和更复杂的查询，但只有当系统能够为这些查询提供良好的答案时，复杂查询才会变得常见，并且更长的语法问题更容易提出。后一个问题将最终解决语音接口，但提高系统处理复杂查询的能力是IR的主要长期目标。该奖项将支持复杂查询检索模型的初步实验，超越了典型的词袋方法。在开发新的检索模型时，将探讨两个主要问题。首先，为了提高系统的鲁棒性，将开发比我们目前的模型更可靠地捕获主题相关性的模型。.其次，为了提高系统在排名靠前的文档中的准确性，将探索更精确地捕获主题相关性的模型。建议活动的智力价值：复杂查询的查询是一个难题，并且有很长的尝试解决方案的历史。然而，有一些因素表明，现在应该有可能取得重大进展。特别是，最近出现了一种新的方法来检索基于语言模型的兴趣激增。拟议的研究将利用这一最新的工作，从一个新的角度研究复杂的查询。拟议活动产生的更广泛影响：在本提案的一年时间内，该奖项将支持对这些新模式的探索，以获得关于其对政府信息的有效性以及最能代表与政府有关的信息需求的复杂查询的初步结果。这是一个高风险的研究项目，因为过去在这一领域缺乏进展。然而，即使是中等程度的成功，回报也会很高，因为这将决定政府信息系统是否会对查询做出有用的响应，而不是完全失败或对寻求答案的人提供很少的帮助。

A huge amount of government information is available on the Web. The web sites containing that information are, however, often extremely complex and difficult to navigate. In this type of environment, simple queries are not very helpful in locating relevant information. The current information retrieval (IR) landscape, however, is dominated by simple queries because that is what Web search engines are good at doing. These simple queries generally help the user to find a good home page. In the case of government information on the web, however, a home page is often of little help in finding the right answers and, instead, a considerable amount of additional user effort is required. Most information needs would be better expressed as complex queries; current systems impose a bottleneck where users are forced to use keyword-based simple queries. Some types of professional searchers (e.g. intelligence analysts, paralegals) do formulate longer and more complex queries, but complex queries will only become common if systems are capable of providing good answers to those queries, and longer, grammatical questions were easier to ask. The latter issue will be eventually addressed by speech interfaces, but improving the capability of systems to handle complex queries represents the major long-term goal of IR. This award will support initial experiments with retrieval models for complex queries that go beyond the typical bag-of-words approach. There are two major issues that will be explored in the development of new retrieval models. First, in order to improve system robustness, models will be developed that more reliably capture topical relevance than our current models. . Second, in order to improve the system accuracy in the top ranked documents, models will be explored that more precisely capture topical relevance. Intellectual merit of the proposed activity: Answering complex queries is a hard problem, and one that has a long history of attempted solutions. There are a number of factors, however, that indicate that it should now be possible to make significant progress. In particular, there has been a recent surge of interest in a new approach to retrieval based on language models. The proposed research will leverage this recent work and study complex queries from a new perspective. Broader impacts resulting from the proposed activity: In the one-year time frame of this proposal, the award will support exploration of these new models to obtain preliminary results on their effectiveness with government information and on complex queries that are most representative of people with information needs related to government. This is a high-risk research project because of the lack of progress in this area in the past. The payoff of even moderate success will be high, however, as it will make the difference between a government information system returning a useful response to a query instead of either failing completely or providing very little assistance to the people seeking answers.