Approximately one third of patients with titanium alloy spinal implants exhibited abnormal serum or hair metal concentrations at a mean time of mean 5.1 years after surgery. Titanium or aluminum may travel to distant organs after dissolution of metals from the spinal implants.

This paper describes our ongoing research project on text simplification for congenitally deaf people. Text simplification we are aiming at is the task of offering a deaf reader a syntactic and lexical paraphrase of a given text for assisting her/him to understand what it means. In this paper, we discuss the issues we should address to realize text simplification and report on the present results in three different aspects of this task: readability assessment, paraphrase representation and post-transfer error detection.

In this paper, we discuss how to annotate coreference and predicate-argument relations in Japanese written text. There have been research activities for building Japanese text corpora annotated with coreference and predicate-argument relations as are done in the Kyoto Text Corpus version 4.0 (Kawahara et al., 2002) and the GDATagged Corpus (Hasida, 2005). However, there is still much room for refining their specifications. For this reason, we discuss issues in annotating these two types of relations, and propose a new specification for each. In accordance with the specification, we built a large-scaled annotated corpus, and examined its reliability. As a result of our current work, we have released an annotated corpus named the NAIST Text Corpus 1 , which is used as the evaluation data set in the coreference and zero-anaphora resolution tasks in Iida et al. (2005) and Iida et al. (2006).

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This paper proposes a method for intrasentential subject zero anaphora resolution in Japanese. Our proposed method utilizes a Multi-column Convolutional Neural Network (MCNN) for predicting zero anaphoric relations. Motivated by Centering Theory and other previous works, we exploit as clues both the surface word sequence and the dependency tree of a target sentence in our MCNN. Even though the F-score of our method was lower than that of the state-of-the-art method, which achieved relatively high recall and low precision, our method achieved much higher precision (>0.8) in a wide range of recall levels. We believe such high precision is crucial for real-world NLP applications and thus our method is preferable to the state-of-the-art method.