Identifying Abbreviation Definitions Machine Learning with Naturally Labeled Data

The rapid growth of biomedical literature requires accurate text analysis and text processing tools. Detecting abbreviations and identifying their definitions is an important component of such tools. In this work, we develop a machine learning algorithm for abbreviation definition identification in text. Most existing approaches for abbreviation definition identification employ rule-based methods. While achieving high precision, rule-based methods are limited to the rules defined and fail to capture many uncommon definition patterns. Supervised learning techniques, which offer more flexibility in detecting abbreviation definitions, have also been applied to the problem. However, they require manually labeled training data. In this study, we make use of what we term naturally labeled data. Positive training examples are extracted from text, which provides naturally occurring potential abbreviation-definition pairs. Negative training examples are generated randomly by mixing potential abbreviations with unrelated potential definitions. The machine learner is trained to distinguish between these two sets of examples. Then, the learned feature weights are used to identify the abbreviation full form. This approach does not require manually labeled training data. We evaluate the performance of our algorithm on the Ab3P, BIOADI and Meds tract corpora. We achieve an F-score that is comparable to the earlier existing systems yet with a higher recall.