Unsupervised Abbreviation Disambiguation Contextual disambiguation using word embeddings

TL;DR

This paper introduces an unsupervised method for abbreviation disambiguation using word embeddings, which learns context representations from unstructured text and outperforms existing approaches on large real-world datasets.

Contribution

The paper presents the first transparent, unsupervised abbreviation disambiguation method that does not require labeled data and scales to thousands of abbreviations with multiple meanings.

Findings

UAD achieves high accuracy on diverse real-world datasets.

UAD outperforms baseline and state-of-the-art methods.

The approach scales efficiently to large vocabularies.

Abstract

Abbreviations often have several distinct meanings, often making their use in text ambiguous. Expanding them to their intended meaning in context is important for Machine Reading tasks such as document search, recommendation and question answering. Existing approaches mostly rely on manually labeled examples of abbreviations and their correct long-forms. Such data sets are costly to create and result in trained models with limited applicability and flexibility. Importantly, most current methods must be subjected to a full empirical evaluation in order to understand their limitations, which is cumbersome in practice. In this paper, we present an entirely unsupervised abbreviation disambiguation method (called UAD) that picks up abbreviation definitions from unstructured text. Creating distinct tokens per meaning, we learn context representations as word vectors. We demonstrate how to further boost abbreviation disambiguation performance by obtaining better context representations using additional unstructured text. Our method is the first abbreviation disambiguation approach with a transparent model that allows performance analysis without requiring full-scale evaluation, making it highly relevant for real-world deployments. In our thorough empirical evaluation, UAD achieves high performance on large real-world data sets from different domains and outperforms both baseline and state-of-the-art methods. UAD scales well and supports thousands of abbreviations with multiple different meanings within a single model. In order to spur more research into abbreviation disambiguation, we publish a new data set, that we also use in our experiments.