We describe a rule-based approach for the automatic acquisition of salient
scientific entities from Computational Linguistics (CL) scholarly article
titles. Two observations motivated the approach: (i) noting salient aspects of
an article's contribution in its title; and (ii) pattern regularities capturing
the salient terms that could be expressed in a set of rules. Only those
lexico-syntactic patterns were selected that were easily recognizable, occurred
frequently, and positionally indicated a scientific entity type. The rules were
developed on a collection of 50,237 CL titles covering all articles in the ACL
Anthology. In total, 19,799 research problems, 18,111 solutions, 20,033
resources, 1,059 languages, 6,878 tools, and 21,687 methods were extracted at
an average precision of 75%.

使用基于规则的方法，从计算语言学（CL）学术文章标题中自动获取突出的科学实体，从而提高实体检索的精确性和召回率。在所有 ACL Anthology 文章标题的基础上，总计提取了 19,799 个研究问题，18,111 个解决方案，20,033 个资源，1,059 种语言，6,878 个工具和 21,687 种方法，并达到了 75％ 的平均精确度。