The nature of statistical learning theory
The nature of statistical learning theory
Acrophile: an automated acronym extractor and server
DL '00 Proceedings of the fifth ACM conference on Digital libraries
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
Information extraction from research papers using conditional random fields
Information Processing and Management: an International Journal
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
Segmentation conditional random fields (SCRFs): a new approach for protein fold recognition
RECOMB'05 Proceedings of the 9th Annual international conference on Research in Computational Molecular Biology
An algorithm for local geoparsing of microtext
Geoinformatica
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There are increasingly amount of acronyms in many kinds of documents and web pages, which is a serious obstacle for the readers. This paper addresses the task of finding expansions in texts for given acronym queries. We formulate the expansion finding problem as a sequence labeling task and use Conditional Random Fields to solve it. Since it is a complex task, our method tries to enhance the performance from two aspects. First, we introduce nonlinear hidden layers to learn better representations of the input data under the framework of Conditional Random Fields. Second, simple and effective features are designed. The experimental results on real data show that our model achieves the best performance against the state-of-the-art baselines including Support Vector Machine and standard Conditional Random Fields.