Foundations of statistical natural language processing
Foundations of statistical natural language processing
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
SaRAD: a Simple and Robust Abbreviation Dictionary
Bioinformatics
A discriminative alignment model for abbreviation recognition
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
A supervised learning approach to acronym identification
AI'05 Proceedings of the 18th Canadian Society conference on Advances in Artificial Intelligence
Learning Abbreviations from Chinese and English Terms by Modeling Non-Local Information
ACM Transactions on Asian Language Information Processing (TALIP)
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This paper proposes a novel method for generating Japanese abbreviations from their full forms with the Log-Linear Model (LLM) in order to take advantage of characteristic patterns of Japanese abbreviation. Our experimental results show that the method is effective for TV program titles that contain colloquial expressions. The proposed method achieved 78.8% recall for the top 30 candidates, whereas a baseline method using Conditional Random Fields (CRFs) achieved 68.3% recall. Moreover, from the results of experiments using six data sets classified according to types of character and semantic categories, we show that each performance of the above two methods depends on the types of the full forms.