Learning Boolean Functions in an Infinite Attribute Space
Machine Learning
Statistical methods for speech recognition
Statistical methods for speech recognition
Learning to resolve natural language ambiguities: a unified approach
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Similarity-Based Models of Word Cooccurrence Probabilities
Machine Learning - Special issue on natural language learning
A Winnow-Based Approach to Context-Sensitive Spelling Correction
Machine Learning - Special issue on natural language learning
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Discovery of linguistic relations using lexical attraction
Discovery of linguistic relations using lexical attraction
A non-projective dependency parser
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Part of speech tagging using a network of linear separators
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Exploiting syntactic structure for language modeling
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Measures of distributional similarity
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Distributional similarity models: clustering vs. nearest neighbors
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
A maximum entropy model for prepositional phrase attachment
HLT '94 Proceedings of the workshop on Human Language Technology
Speech and Language Processing (2nd Edition)
Speech and Language Processing (2nd Edition)
Learning cost-sensitive active classifiers
Artificial Intelligence
Word prediction using a clustered optimal binary search tree
Information Processing Letters
Semantic knowledge in word completion
Proceedings of the 7th international ACM SIGACCESS conference on Computers and accessibility
Co-occurrence Retrieval: A Flexible Framework for Lexical Distributional Similarity
Computational Linguistics
Pattern-based disambiguation for natural language processing
EMNLP '00 Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13
Discriminative learning of selectional preference from unlabeled text
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Learning strategies for open-domain natural language question answering
ACLstudent '05 Proceedings of the ACL Student Research Workshop
All-word prediction as the ultimate confusable disambiguation
CHSLP '06 Proceedings of the Workshop on Computationally Hard Problems and Joint Inference in Speech and Language Processing
Learning strategies for open-domain natural language question answering
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Language models for contextual error detection and correction
CLAGI '09 Proceedings of the EACL 2009 Workshop on Computational Linguistic Aspects of Grammatical Inference
Word prediction using a clustered optimal binary search tree
Information Processing Letters
The Journal of Machine Learning Research
An empirical evaluation of stop word removal in statistical machine translation
EACL 2012 Proceedings of the Joint Workshop on Exploiting Synergies between Information Retrieval and Machine Translation (ESIRMT) and Hybrid Approaches to Machine Translation (HyTra)
| Hi-index | 0.00 |
The eventual goal of a language model is to curately predict the value of a missing word given its context. We present an approach to word prediction that is based on learning a representation for each word as a function of words and linguistics predicates in its context. This approach raises a few new questions that we address. First, in order to learn good word representations it is necessary to use an expressive representation of the context. We present a way that uses external knowledge to generate expressive context representations, along with a learning method capable of handling the large number of features generated this way that can, potentially, contribute to each prediction. Second, since the number of words "competing" for each prediction is large, there is a need to "focus the attention" on a smaller subset of these. We exhibit the contribution of a "focus of attention" mechanism to the performance of the word predictor. Finally, we describe a large scale experimental study in which the approach presented is shown to yield significant improvements in word prediction tasks.