Question Answering is most likely one of the toughest tasks in the field of Natural Language Processing. It aims at directly returning accurate and short answers to questions asked by users in human language over a huge collection of documents or database. Recently, the continuously exponential rise of digital information has imposed the need for more direct access to relevant answers. Thus, question answering has been the subject of a widespread attention and has been extensively explored over the last few years. Retrieving passages remains a crucial but also a challenging task in question answering. Although there has been an abundance of work on this task, this latter still implies non-trivial endeavor. In this paper, we propose an ad-hoc passage retrieval approach for Question Answering using n-grams. This approach relies on a new measure of similarity between a passage and a question for the extraction and ranking of the different passages based on n-gram overlapping. More concretely, our measure is based on the dependency degree of n-gram words of the question in the passage. We validate our approach by the development of the “SysPex” system that automatically returns the most relevant passages to a given question.

中文翻译：

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使用 N-gram 检索相关段落以进行开放域问答

问答很可能是自然语言处理领域中最艰巨的任务之一。它旨在直接返回对用户在大量文档或数据库中以人类语言提出的问题的准确和简短的答案。最近，数字信息的持续指数增长要求更直接地访问相关答案。因此，问答一直受到广泛关注，并在过去几年中得到了广泛的探索。检索段落仍然是问答中的一项关键但也是一项具有挑战性的任务。尽管在这项任务上进行了大量工作，但后者仍然意味着不平凡的努力。在本文中，我们提出了一种使用 n-gram 进行问答的临时段落检索方法。这种方法依赖于一种新的段落和问题之间的相似性度量，用于基于 n-gram 重叠的不同段落的提取和排序。更具体地说，我们的测量是基于文章中问题的 n-gram 单词的依赖程度。我们通过开发“SysPex”系统来验证我们的方法，该系统会自动返回与给定问题最相关的段落。