Tetsuji Nakagawa

A Hybrid Approach to Word Segmentation and POS Tagging

In this paper, we present a hybrid method for word segmentation and POS tagging. The target languages are those in which word boundaries are ambiguous, such as Chinese and Japanese. In the method, word-based and character-based processing is combined, and word segmentation and POS tagging are conducted simultaneously. Experimental results on multiple corpora show that the integrated method has high accuracy.

Chinese and Japanese word segmentation using word-level and character-level information

In this paper, we present a hybrid method for Chinese and Japanese word segmentation. Word-level information is useful for analysis of known words, while character-level information is useful for analysis of unknown words, and the method utilizes both these two types of information in order to effectively handle known and unknown words. Experimental results show that this method achieves high overall accuracy in Chinese and Japanese word segmentation

Detecting errors in corpora using support vector machines

While the corpus-based research relies on human annotated corpora, it is often said that a non-negligible amount of errors remain even in frequently used corpora such as Penn Treebank. Detection of errors in annotated corpora is important for corpus-based natural language processing. In this paper, we propose a method to detect errors in corpora using support vector machines (SVMs). This method is based on the idea of extracting exceptional elements that violate consistency. We propose a method of using SVMs to assign a weight to each element and to find errors in a POS tagged corpus. We apply the method to English and Japanese POS-tagged corpora and achieve high precision in detecting errors.

Revision Learning and its Application to Part-of-Speech Tagging

This paper presents a revision learning method that achieves high performance with small computational cost by combining a model with high generalization capacity and a model with small computational cost. This method uses a high capacity model to revise the output of a small cost model. We apply this method to English part-of-speech tagging and Japanese morphological analysis, and show that the method performs well.

Guessing Parts-of-Speech of Unknown Words Using Global Information

In this paper, we present a method for guessing POS tags of unknown words using local and global information. Although many existing methods use only local information (i.e. limited window size or intra-sentential features), global information (extra-sentential features) provides valuable clues for predicting POS tags of unknown words. We propose a probabilistic model for POS guessing of unknown words using global information as well as local information, and estimate its parameters using Gibbs sampling. We also attempt to apply the model to semi-supervised learning, and conduct experiments on multiple corpora.