Learning conditional random fields with latent sparse features for acronym expansion finding

The ever increasing usage of acronyms in many kinds of documents, including web pages, is becoming an obstacle for average readers. This paper studies the task of finding expansions in documents for a given set of acronyms. We cast the expansion finding problem as a sequence labeling task and adapt Conditional Random Fields (CRF) to solve it. While adapting CRFs, we enhance the performance from two aspects. First, we introduce nonlinear hidden layers to learn better representations of the input data. Second, we design simple and effective features. We create a hand labeled evaluation data based on Wikipedia.org and web crawling. We evaluate the effectiveness of several algorithms in solving the expansion finding problem. The experimental results demonstrate that the new method achieves performs better than Support Vector Machine and standard Conditional Random Fields.