Mining the web to discover acronym‐definitions based on sequence labeling and iterative query expansion model

Abstract

SummaryFinding definitions associated with an acronym is a constructive task to many applications like web search, information retrieval, natural language processing, ontology mapping, and question answering. A large‐scale manually built acronym‐definition repositories are available online but updating is a difficult task. To address this problem, previous research works used either heuristics or a machine learning approach to automate the detection of acronym‐definition pairs. This article presents a heuristics approach based on a rule‐based sequence‐labeling model, for finding the list of definitions associated with an acronym from the Web. The organic search result of the web page includes website title, URL, Meta description, and site links. In the proposed work, web pages of the search engine are used as corpus and sequence‐labeling task is done through character and word level mapping schemes. In these schemes, the desirable properties of the definition are expressed by using rules. Each identified definition is validated through a collocation measure. Besides, the obtained results are assessed against a manually built acronym/definitions repository: Acronym Finder. The proposed model certainly improves the coverage of manual repositories maintaining high precision and recall.