  This paper addresses automatic extraction of abbreviations (encompassing
acronyms and initialisms) and corresponding long-form expansions from plain
unstructured text. We create and are going to release a multilingual resource
for abbreviations and their corresponding expansions, built automatically by
exploiting Wikipedia redirect and disambiguation pages, that can be used as a
benchmark for evaluation. We address a shortcoming of previous work where only
the redirect pages were used, and so every abbreviation had only a single
expansion, even though multiple different expansions are possible for many of
the abbreviations. We also develop a principled machine learning based approach
to scoring expansion candidates using different techniques such as indicators
of near synonymy, topical relatedness, and surface similarity. We show improved
performance over seven languages, including two with a non-Latin alphabet,
relative to strong baselines.
