让学术论文中的伪代码可搜索，提升研究者查找算法实现的效率。
PseudoSeer: a Search Engine for Pseudocode
- 基于Elasticsearch构建，支持标题、摘要、代码片段等多维度检索。
- 采用加权BM25算法，结合不同搜索维度权重优化结果排序。
- 适合需要快速定位论文中算法伪代码的研究人员使用。
本文设计了一种新型伪代码搜索引擎，旨在高效检索包含伪代码的学术论文。系统基于Elasticsearch构建，支持在论文标题、摘要、作者信息及LaTeX代码片段等多个维度进行搜索，并提供组合筛选与精确匹配查询功能，以获得更精准的结果。数据主要来源于arXiv，通过特定方法提取并建立文本索引，优化存储结构以提升检索性能。搜索引擎采用加权BM25算法进行排序，详细说明了单维度与多维度联合搜索中各字段的权重设定方式。展示了多个搜索结果页面实例，并简要介绍了未来工作方向及评估该引擎有效性和性能的方法论。
原文摘要 · Abstract (English)
A novel pseudocode search engine is designed to facilitate efficient retrieval and search of academic papers containing pseudocode. By leveraging Elasticsearch, the system enables users to search across various facets of a paper, such as the title, abstract, author information, and LaTeX code snippets, while supporting advanced features like combined facet searches and exact-match queries for more targeted results. A description of the data acquisition process is provided, with arXiv as the primary data source, along with methods for data extraction and text-based indexing, highlighting how different data elements are stored and optimized for search. A weighted BM25-based ranking algorithm is used by the search engine, and factors considered when prioritizing search results for both single and combined facet searches are described. We explain how each facet is weighted in a combined search. Several search engine results pages are displayed. Finally, there is a brief overview of future work and potential evaluation methodology for assessing the effectiveness and performance of the search engine is described.
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