  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%.
