C4.5: programs for machine learning
C4.5: programs for machine learning
Theory of Syntactic Recognition for Natural Languages
Theory of Syntactic Recognition for Natural Languages
Learning Parse and Translation Decisions From Examples With Rich Context
Learning Parse and Translation Decisions From Examples With Rich Context
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Three generative, lexicalised models for statistical parsing
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Learning parse and translation decisions from examples with rich context
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Toward semantics-based answer pinpointing
HLT '01 Proceedings of the first international conference on Human language technology research
Parsing and question classification for question answering
ODQA '01 Proceedings of the workshop on Open-domain question answering - Volume 12
Automated multi-document summarization in NeATS
HLT '02 Proceedings of the second international conference on Human Language Technology Research
Factors affecting the accuracy of Korean parsing
SPMRL '10 Proceedings of the NAACL HLT 2010 First Workshop on Statistical Parsing of Morphologically-Rich Languages
| Hi-index | 0.00 |
This paper demonstrates that machine learning is a suitable approach for rapid parser development. From 1000 newly treebanked Korean sentences we generate a deterministic shift-reduce parser. The quality of the treebank, particularly crucial given its small size, is supported by a consistency checker.