Do Kook Choe, David McClosky. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2015.

Byron C. Wallace, Do Kook Choe, Eugene Charniak. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2015.

Model combination techniques have consistently shown state-of-the-art performance across multiple tasks, including syntactic parsing.However, they dramatically increase runtime and can be difficult to employ in practice.We demonstrate that applying constituency model combination techniques to n-best lists instead of n different parsers results in significant parsing accuracy improvements.Parses are weighted by their probabilities and combined using an adapted version of Sagae and Lavie (2006).These accuracy gains come with marginal computational costs and are obtained on top of existing parsing techniques such as discriminative reranking and self-training, resulting in state-of-the-art accuracy: 92.6% on WSJ section 23.On out-of-domain corpora, accuracy is improved by 0.4% on average.We empirically confirm that six well-known n-best parsers benefit from the proposed methods across six domains.

We introduce an extended naive Bayes model for word sense induction (WSI) and apply it to a WSI task.The extended model incorporates the idea the words closer to the target word are more relevant in predicting its sense.The proposed model is very simple yet effective when evaluated on SemEval-2010 WSI data.