A corpus-based approach to language learning
A corpus-based approach to language learning
Structural ambiguity and lexical relations
Computational Linguistics - Special issue on using large corpora: I
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
A rule-based approach to prepositional phrase attachment disambiguation
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 2
A maximum entropy model for prepositional phrase attachment
HLT '94 Proceedings of the workshop on Human Language Technology
Probabilistic models for PP-attachment resolution and NP analysis
ConLL '01 Proceedings of the 2001 workshop on Computational Natural Language Learning - Volume 7
The effect of corpus size in combining supervised and unsupervised training for disambiguation
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
A lattice-based framework for enhancing statistical parsers with information from unlabeled corpora
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Decision trees for sense disambiguation of prepositions: case of over
CLS '04 Proceedings of the HLT-NAACL Workshop on Computational Lexical Semantics
Prepositions in applications: A survey and introduction to the special issue
Computational Linguistics
Handling of prepositions in English to Bengali machine translation
Prepositions '06 Proceedings of the Third ACL-SIGSEM Workshop on Prepositions
Corpus creation for new genres: A crowdsourced approach to PP attachment
CSLDAMT '10 Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon's Mechanical Turk
Grapheme-to-phoneme conversion based on a fast TBL algorithm in mandarin TTS systems
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
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Determining the attachments of prepositions and subordinate conjunctions is a key problem in parsing natural language. This paper presents a trainable approach to making these attachments through transformation sequences and error-driven learning. Our approach is broad coverage, and accounts for roughly three times the attachment cases that have previously been handled by corpus-based techniques. In addition, our approach is based on a simplified model of syntax that is more consistent with the practice in current state-of-the-art language processing systems. This paper sketches syntactic and algorithmic details, and presents experimental results on data sets derived from the Penn Treebank. We obtain an attachment accuracy of 75.4% for the general case, the first such corpus-based result to be reported. For the restricted cases previously studied with corpusbased methods, our approach yields an accuracy comparable to current work (83.1%).