Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Class-based n-gram models of natural language
Computational Linguistics
Statistical language understanding using frame semantics
Statistical language understanding using frame semantics
Statistical morphological disambiguation for agglutinative languages
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Dependency parsing with an extended finite state approach
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Factored language models and generalized parallel backoff
NAACL-Short '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume of the Proceedings of HLT-NAACL 2003--short papers - Volume 2
Multi-speaker language modeling
HLT-NAACL-Short '04 Proceedings of HLT-NAACL 2004: Short Papers
A genetic algorithm for learning significant phrase patterns in radiology reports
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Factored neural language models
NAACL-Short '06 Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers
Improved language modeling for statistical machine translation
ParaText '05 Proceedings of the ACL Workshop on Building and Using Parallel Texts
Morpheme-based and factored language modeling for amharic speech recognition
LTC'09 Proceedings of the 4th conference on Human language technology: challenges for computer science and linguistics
| Hi-index | 0.00 |
Statistical language modeling remains a challenging task, in particular for morphologically rich languages. Recently, new approaches based on factored language models have been developed to address this problem. These models provide principled ways of including additional conditioning variables other than the preceding words, such as morphological or syntactic features. However, the number of possible choices for model parameters creates a large space of models that cannot be searched exhaustively. This paper presents an entirely data-driven model selection procedure based on genetic search, which is shown to outperform both knowledge-based and random selection procedures on two different language modeling tasks (Arabic and Turkish).