Assessing agreement on classification tasks: the kappa statistic
Computational Linguistics
Near-synonymy and lexical choice
Computational Linguistics
Accurate methods for the statistics of surprise and coincidence
Computational Linguistics - Special issue on using large corpora: I
Dynamic nonlocal language modeling via hierarchical topic-based adaptation
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Verb paraphrase based on case frame alignment
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
NAACL-Short '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume of the Proceedings of HLT-NAACL 2003--short papers - Volume 2
Bootstrapping lexical choice via multiple-sequence alignment
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Automatic acquisition of context-specific lexical paraphrases
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Web mining for lexical context-specific paraphrasing
AIRS'06 Proceedings of the Third Asia conference on Information Retrieval Technology
Hi-index | 0.00 |
Our research aims at developing a system that paraphrases written language text to spoken language style. In such a system, it is important to distinguish between appropriate and inappropriate words in an input text for spoken language. We call this task lexical choice for paraphrasing. In this paper, we describe a method of lexical choice that considers the topic. Basically, our method is based on the word probabilities in written and spoken language corpora. The novelty of our method is topic adaptation. In our framework, the corpora are classified into topic categories, and the probability is estimated using such corpora that have the same topic as input text. The result of evaluation showed the effectiveness of topic adaptation.