Acronyms are omnipresent. They usually express information that is repetitive and well known. But acronyms can also be ambiguous because there can be multiple expansions for the same acronym. In this paper, we propose a general system for acronym disambiguation that can work on any acronym given some context information. We present methods for retrieving all the possible expansions of an acronym from Wikipedia and AcronymsFinder.com. We propose to use these expansions to collect all possible contexts in which these acronyms are used and then score them using a paragraph embedding technique called Doc2Vec. This method collectively led to achieving an accuracy of 90.9 in selecting the correct expansion for given acronym, on a dataset we scraped from Wikipedia with 707 distinct acronyms and 14,876 disambiguations.
The paper presents a study on large-scale automatic extraction of acronyms and associated expansions from Web data and from the user interactions with this data through Web search engines. We investigate three information sources for extracting and ranking acronym-expansion pairs, as provided by a large-scale search engine: the crawled web documents, the search engine logs, and the search results. We evaluate and compare the acronym-expansion pairs generated from these sources on three dimensions: (1) the precision and recall of each source; (2) the overlap and inclusion among the acronym-expansion sets; and (3) the rank-order correlation of the ordered expansion sets. Our results show that all three data sources play an important role in building a comprehensive up-to-date collection of acronym-expansion pairs.