检索具有数学内容的文档

Shahab Kamali, Frank Wm. Tompa
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引用次数: 37

摘要

许多包含数学内容的文档都发布在Web上,但是仅依赖关键字搜索的传统搜索引擎无法充分利用其中的数学信息。特别是,当文档中的表达式没有使用自然关键字注释或用户无法使用关键字描述其查询时,关键字搜索是不够的。通过直接查询其数学内容来检索文档在教育、数字图书馆、工程、专利文档、医学科学等各个领域都非常有吸引力。捕获数学表达式的相关性也极大地增强了这些领域中的文档分类。与文本检索不同,关键字携带足够的语义来区分文本文档并对其进行排序,数学符号本身不包含太多的语义信息。事实上,数学表达式通常由几个字母符号组成,它们以相当复杂的结构组织起来。因此,还应该考虑描述这些符号组合方式的表达式的结构。不幸的是,没有标准的测试平台来评估数学检索算法的有效性。在本文中,我们研究了数学检索中最基本和最具挑战性的问题,即如何捕获数学表达式的相关性,如何查询它们,以及如何评估结果。我们描述了各种搜索范例,并提出了相应的检索系统。我们讨论了每种方法的优点和缺点,并通过广泛的实证研究进一步比较它们。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Retrieving documents with mathematical content
Many documents with mathematical content are published on the Web, but conventional search engines that rely on keyword search only cannot fully exploit their mathematical information. In particular, keyword search is insufficient when expressions in a document are not annotated with natural keywords or the user cannot describe her query with keywords. Retrieving documents by querying their mathematical content directly is very appealing in various domains such as education, digital libraries, engineering, patent documents, medical sciences, etc. Capturing the relevance of mathematical expressions also greatly enhances document classification in such domains. Unlike text retrieval, where keywords carry enough semantics to distinguish text documents and rank them, math symbols do not contain much semantic information on their own. In fact, mathematical expressions typically consist of few alphabetical symbols organized in rather complex structures. Hence, the structure of an expression, which describes the way such symbols are combined, should also be considered. Unfortunately, there is no standard testbed with which to evaluate the effectiveness of a mathematics retrieval algorithm. In this paper we study the fundamental and challenging problems in mathematics retrieval, that is how to capture the relevance of mathematical expressions, how to query them, and how to evaluate the results. We describe various search paradigms and propose retrieval systems accordingly. We discuss the benefits and drawbacks of each approach, and further compare them through an extensive empirical study.
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