Maximum entropy models for natural language ambiguity resolution
Maximum entropy models for natural language ambiguity resolution
Maximum entropy estimation for feature forests
HLT '02 Proceedings of the second international conference on Human Language Technology Research
Climbing the path to grammar: a maximum entropy model of subject/object learning
PMHLA '05 Proceedings of the Workshop on Psychocomputational Models of Human Language Acquisition
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The paper reports on a detailed quantitative analysis of distributional language data of both Italian and Czech, highlighting the relative contribution of a number of distributed grammatical factors to sentence-based identification of subjects and direct objects. The work uses a Maximum Entropy model of stochastic resolution of conflicting grammatical constraints and is demonstrably capable of putting explanatory theoretical accounts to the test of usage-based empirical verification.