On the limited memory BFGS method for large scale optimization
Mathematical Programming: Series A and B
A new statistical parser based on bigram lexical dependencies
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
A stochastic parser based on a structural word prediction model
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Backward beam search algorithm for dependency analysis of Japanese
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
A more precise model for web retrieval
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
Czech-English dependency-based machine translation
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
Japanese dependency analysis using cascaded chunking
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Online large-margin training of dependency parsers
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Pseudo-projective dependency parsing
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Discriminative classifiers for deterministic dependency parsing
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Labeled pseudo-projective dependency parsing with support vector machines
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
IEEE Transactions on Audio, Speech, and Language Processing
| Hi-index | 0.00 |
A dependency structure interprets modification relationships between words and is often recognized as an important element in semantic information analysis. With conventional approaches for extracting this dependency structure, it is assumed that the complete sentence is known before the analysis starts. For spontaneous speech data, however, this assumption is not necessarily correct since sentence boundaries are not marked in the data and it is not easy to detect them correctly. Although sentence boundaries can be detected before dependency analysis, this cascaded implementation is not suitable for online processing since it delays the responses of the application. In this paper, we propose a sequential dependency analysis method for online spontaneous speech processing. The proposed method enables us to analyze incomplete sentences sequentially and detect sentence boundaries simultaneously. The analyzer can be trained using parsed data based on the maximum entropy principle. Experimental results using spontaneous lecture speech from the Corpus of Spontaneous Japanese show that our proposed method achieves online processing with an accuracy equivalent to that of offline processing in which boundary detection and dependency analysis are cascaded.