Exploiting textual descriptions and dependency graph for searching mathematical expressions in scientific papers
Exploiting textual descriptions and dependency graph for searching mathematical expressions in scientific papers
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DOI：
10.1109/icdim.2014.6991403
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发表时间：
2014-12
期刊：
影响因子：
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通讯作者：
Giovanni Yoko Kristianto;Goran Topic;Akiko Aizawa
中科院分区：
文献类型：
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作者：
Giovanni Yoko Kristianto;Goran Topic;Akiko Aizawa
数学表达式对于科学信息的交流非常重要，例如，解释或定义用自然语言编写的概念。尽管它们很重要，但当前的传统搜索系统无法建立对科学论文中包含的数学表达式的访问。当前数学搜索系统开发的主要焦点是数学树结构索引，但利用这些系统中表达式周围的文本信息也很重要。我们研究文本信息如何对数学搜索系统做出贡献，主要是在排名过程中。我们研究了两种类型的文本信息对数学搜索系统排名性能的影响：上下文窗口（基线）中的单词（很容易从句子标记化结果中提取）和描述（使用机器学习方法提取）。我们还研究了通过利用数学表达式的依赖图获得的排名改进。实验结果表明，与使用上下文或不使用文本信息时相比，同时使用描述和依赖图可以提供更好的排名性能。结果还表明，依赖图对于增加分配描述的数学表达式的数量至关重要，因此与描述一起使用比仅使用描述具有更高的排名性能。这项研究表明，描述比上下文窗口更好（更精确）地表示数学表达式，甚至来自子（间接）表达式的描述仍然比目标表达式本身的上下文更好地表示目标表达式。
Mathematical expressions are important for communication of scientific information, for instance, to explain or define concepts written in natural language. Despite their importance, current conventional search systems can not establish access to the mathematical expressions contained in a scientific paper. The major focus of current development of mathematical search systems is mathematical tree structure indexing, but utilizing textual information surrounding the expressions in these systems is also important. We examine how textual information contributes to a mathematical search system, primarily in the ranking process. We investigate the impact of two types of textual information in the ranking performances of a mathematical search system: words in context windows (baseline), which is easily extracted from sentence tokenization result, and descriptions, which are extracted using a machine learning method. We also examine the improvement in ranking obtained by utilizing the dependency graph of mathematical expressions. The experiment results show that the use of description and dependency graph together deliver better ranking performance than the use of context or when no textual information is used. The results also show that the dependency graph is crucial for increasing the number of mathematical expressions being assigned descriptions, and thus its use with descriptions together presented higher ranking performance than the use of descriptions only. This study suggests that descriptions represent mathematical expressions better (more precisely) than context windows, and even descriptions from child (indirect) expressions still represent the target expression better than the context from the target expression itself.