III: Small: Interactive Construction of Complex Query Models

本研究计划将研究和实现SearchIE，一种基于搜索的信息提取方法。“SearchIE将允许快速，个性化，情景识别文本中的对象或动作类型，这些类型可能对复杂的搜索任务有用。现代搜索引擎通常提供某种机制来指示查询关键字仅在出现在人名或位置中时才与文档匹配。为了实现这一点，注释者在文本中发现并标记了大量的人名（例如），应用机器学习算法来学习哪些低级特征指示名称类型，然后在文档集合中运行该类型的分类器。这样就可以编写一个查询，表示“巴黎用作人名而不是位置。“不幸的是，现有的方法不适合对新奇的、意想不到的类型感兴趣的搜索者--例如，捕鲸船的名字、维多利亚女王海军的军官、当地的酒吧。这些例子目前无法处理，因为分类器需要提前训练和运行，这是一个昂贵的数据标记过程，对于许多搜索任务来说太艰巨了。由于在线信息收集几乎总是从搜索开始，并且经常涉及在找到的文本中识别感兴趣的项目，因此将这两者结合在一起有可能大大改变两者。SearchIE的方法使人们有可能根据他们的主题兴趣建立个性化的提取器。其结果是，该技术可以从根本上改善在线搜索的外行人以及专业人士，显着减少所需的时间集中查询相关信息。信息提取任务似乎从来没有被直接当作搜索任务来处理。SearchIE在将信息检索（搜索）思维引入提取问题方面是独一无二的，它提供了在传统的“注释然后检测”问题模型中不可能或非常困难的新功能。这个项目将调查由SearchIE方法提出的基本问题。什么样的模型可以在新的环境中最好地集成提取和搜索，使它们真正同时发生？开发人员如何为感兴趣的类型描述和编辑模型？一个交互式开发的模型可以成为机器学习模型的跳板吗？什么时候有足够的信息来做到这一点？使用主题上下文来限制提取范围是否提供了使用SearchIE方法的预期准确性增益？什么样的数据结构需要修改，以充分实施SearchIE，使它是高效的，以及有效的？这种方法在额外的标准测试集合上表现如何？解决系统和算法问题是有可能极大地影响搜索和提取的基本问题。欲了解更多信息，请访问该项目的网站http://ciir.cs.umass.edu/research/searchie。

This research program will investigate and implement SearchIE, a search-based approach to information "extraction." SearchIE will allow rapid, personalized, situational identification of types of objects or actions in text, where those types are likely to be useful for a complex search task. Modern search engines often provide some mechanism to indicate that a query keyword matches a document only if it occurs in the name of a person or in a location. To make that possible, annotators found and marked a large number of people names (for example) in text, a machine learning algorithm was applied to learn which low-level features are indicative of the name type, and then a resulting classifier for that type is run across the collection of documents. It is then possible to write a query that means "paris used as a person's name rather than a location." Unfortunately, the existing approaches do not serve searchers interested in novel, unanticipated types - for example, names of whaling ships, officers in Queen Victoria's navy, local watering holes. Such examples cannot be handled currently because the classifiers need to be trained and run ahead of time, an expensive data labeling process that is too daunting for many search tasks. Since on-line information gathering almost always starts with search and frequently involves identifying items of interest in the found text, bringing these two together has the potential to change both substantially. The SearchIE approach makes it possible for someone to build personalized extractors contextualized by their topical interests. The result is that the technology can radically improve online searching for lay persons as well as professionals by significantly reducing the time needed to focus queries into relevant information. It does not appear that the information extraction task has ever been approached directly as a search task. SearchIE is unique in bringing an information retrieval (search) mindset to the extraction problem, providing new capabilities that are either impossible or extremely difficult in the traditional "annotate then detect" model of the problem. This project will investigate the fundamental issues raised by the SearchIE approach. What models can best integrate extraction and search in new settings where they can truly happen simultaneously? How can a searcher describe and edit a model for the types of interest? Can an interactively developed model be a springboard into a machine learned model and when is there enough information to do that? Does using topical context to limit the scope of extraction provide the expected accuracy gains using SearchIE's approach? What data structure modifications are needed to fully implement SearchIE so that it is efficient as well as effective? How well does this approach fare on additional standard test collections? Addressing the systems and algorithmic issues are fundamental problems that have the potential to greatly impact both search and extraction. For further information, see the project's web site at http://ciir.cs.umass.edu/research/searchie.