The nature of statistical learning theory
The nature of statistical learning theory
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Kernel conditional random fields: representation and clique selection
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Using SVM to Extract Acronyms from Text
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Training Conditional Random Fields Using Transfer Learning for Gesture Recognition
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Discriminative probabilistic models for relational data
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
A supervised learning approach to acronym identification
AI'05 Proceedings of the 18th Canadian Society conference on Advances in Artificial Intelligence
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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.