کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
10360634 | 869872 | 2005 | 13 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Natural language grammar induction with a generative constituent-context model
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
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چکیده انگلیسی
We present a generative probabilistic model for the unsupervised learning of hierarchical natural language syntactic structure. Unlike most previous work, we do not learn a context-free grammar, but rather induce a distributional model of constituents which explicitly relates constituent yields and their linear contexts. Parameter search with EM produces higher quality analyses for human language data than those previously exhibited by unsupervised systems, giving the best published unsupervised parsing results on the ATIS corpus. Experiments on Penn treebank sentences of comparable length show an even higher constituent F1 of 71% on non-trivial brackets. We compare distributionally induced and actual part-of-speech tags as input data, and examine extensions to the basic model. We discuss errors made by the system, compare the system to previous models, and discuss upper bounds, lower bounds, and stability for this task.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 38, Issue 9, September 2005, Pages 1407-1419
Journal: Pattern Recognition - Volume 38, Issue 9, September 2005, Pages 1407-1419
نویسندگان
Dan Klein, Christopher D. Manning,