pybool_ir: A Toolkit for Domain-Specific Search Experiments

Harrisen Scells, Martin Potthast
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引用次数: 1

Abstract

Undertaking research in domain-specific scenarios such as systematic review literature search, legal search, and patent search can often have a high barrier of entry due to complicated indexing procedures and complex Boolean query syntax. Indexing and searching document collections like PubMed in off-the-shelf tools such as Elasticsearch and Lucene often yields less accurate (and less effective) results than the PubMed search engine, i.e., retrieval results do not match what would be retrieved if one issued the same query to PubMed. Furthermore, off-the-shelf tools have their own nuanced query languages and do not allow directly using the often large and complicated Boolean queries seen in domain-specific search scenarios. The pybool_ir toolkit aims to address these problems and to lower the barrier to entry for developing new methods for domain-specific search. The toolkit is an open source package available at https://github.com/hscells/pybool_ir.
pybool_ir:用于特定领域搜索实验的工具包
由于复杂的索引过程和复杂的布尔查询语法,在系统综述文献检索、法律检索和专利检索等领域特定场景中进行研究通常具有很高的进入门槛。在Elasticsearch和Lucene等现成的工具中索引和搜索像PubMed这样的文档集合,通常会产生比PubMed搜索引擎更不准确(和更低效)的结果,也就是说,如果向PubMed发出相同的查询,检索结果与检索结果不匹配。此外,现成的工具有自己细微的查询语言,不允许直接使用特定领域搜索场景中常见的大型和复杂的布尔查询。pybool_ir工具包旨在解决这些问题,并降低开发特定领域搜索新方法的门槛。该工具包是一个开放源码包,可从https://github.com/hscells/pybool_ir获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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