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.
In Hippocratic Abbreviation Expansion @cite_0 paper, they have used SVM, N-Gram, and many hand-crafted feature engineering techniques to identify the correct expansion of an acronym.
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Incorrect normalization of text can be particularly damaging for applications like text-to-speech synthesis (TTS) or typing auto-correction, where the resulting normalization is directly presented to the user, versus feeding downstream applications. In this paper, we focus on abbreviation expansion for TTS, which requires a “do no harm”, high precision approach yielding few expansion errors at the cost of leaving relatively many abbreviations unexpanded. In the context of a largescale, real-world TTS scenario, we present methods for training classifiers to establish whether a particular expansion is apt. We achieve a large increase in correct abbreviation expansion when combined with the baseline text normalization component of the TTS system, together with a substantial reduction in incorrect expansions.