Learning to Parse Natural Language with Maximum Entropy Models Export

@article{citeulike:1610578, abstract = {. This paper presents a machine learning system for parsing natural language that learns from manually parsed example sentences, and parses unseen data at state-of-the-art accuracies. Its machine learning technology, based on the maximum entropy framework, is highly reusable and not specific to the parsing problem, while the linguistic hints that it uses to learn can be specified concisely. It therefore requires a minimal amount of human effort and linguistic knowledge for its construction. In...}, author = {Ratnaparkhi, Adwait}, citeulike-article-id = {1610578}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.41.4133}, journal = {Machine Learning}, keywords = {maxent, parsing, shift-reduce}, number = {1-3}, pages = {151--175}, posted-at = {2009-11-13 08:56:10}, priority = {2}, title = {Learning to Parse Natural Language with Maximum Entropy Models}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.41.4133}, volume = {34}, year = {1999} }

TY - JOUR ID - citeulike:1610578 L3 - citeulike-article-id:1610578 N2 - . This paper presents a machine learning system for parsing natural language that learns from manually parsed example sentences, and parses unseen data at state-of-the-art accuracies. Its machine learning technology, based on the maximum entropy framework, is highly reusable and not specific to the parsing problem, while the linguistic hints that it uses to learn can be specified concisely. It therefore requires a minimal amount of human effort and linguistic knowledge for its construction. In... IS - 1-3 JF - Machine Learning EP - 175 TI - Learning to Parse Natural Language with Maximum Entropy Models VL - 34 SP - 151 KW - maxent KW - parsing KW - shift-reduce AU - Ratnaparkhi, Adwait PY - 1999/// UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.41.4133 ER -