Learning and applying contextual constraints in sentence comprehension
Artificial Intelligence - On connectionist symbol processing
Automatic stochastic tagging of natural language texts
Computational Linguistics
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition
Decoding complexity in word-replacement translation models
Computational Linguistics
Probabilistic and rule-based tagger of an inflective language: a comparison
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Fast and optimal decoding for machine translation
Artificial Intelligence
Corpus-based induction of syntactic structure: models of dependency and constituency
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Computational models of language acquisition
CICLing'10 Proceedings of the 11th international conference on Computational Linguistics and Intelligent Text Processing
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Human syntax acquisition involves a system that can learn constraints on possible word sequences in typologically-different human languages. Evaluation of computational syntax acquisition systems typically involves theory-specific or language-specific assumptions that make it difficult to compare results in multiple languages. To address this problem, a bag-of-words incremental generation (BIG) task with an automatic sentence prediction accuracy (SPA) evaluation measure was developed. The BIG-SPA task was used to test several learners that incorporated n-gram statistics which are commonly found in statistical approaches to syntax acquisition. In addition, a novel Adjacency-Prominence learner, that was based on psycholinguistic work in sentence production and syntax acquisition, was also tested and it was found that this learner yielded the best results in this task on these languages. In general, the BIG-SPA task is argued to be a useful platform for comparing explicit theories of syntax acquisition in multiple languages.