Language Independent Acquisition of Abbreviations
This work addresses the need for a multilingual resource to benchmark abbreviation extraction, which is incremental by extending previous methods to handle multiple expansions per abbreviation.
The paper tackles the problem of automatically extracting abbreviations and their expansions from unstructured text across multiple languages, resulting in improved performance over strong baselines in seven languages, including two with non-Latin alphabets.
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.