BaseNP Supersense Tagging for Japanese Texts

PACLIC 23 / City University of Hong Kong / 3-5 December 200

Gefitinib, an epidermal growth factor receptor blockade agent, shows additional or synergistic effects on the radiosensitivity of esophageal cancer cells in vitro.

Human esophageal cancers have been shown to express high levels of epidermal growth factor receptor (EGFR) and a relationship between high EGFR expression and local advance, the number of lymph node metastases, life expectancy, and sensitivity to chemo-radiotherapy has been demonstrated. We examined the use of gefitinib, an orally active EGFR-selective tyrosine kinase inhibitor, as a new strategy for treatment of esophageal carcinoma. The effects of gefitinib were evaluated in monotherapy and in combination with radiotherapy in human esophageal carcinoma cell lines. Gefitinib produced a dose-dependent inhibition of cellular proliferation in all of the 8 esophageal carcinoma cell lines examined, with IC50 values ranging from 5.7 microM to 36.9 microM. In combination, gefitinib and radiotherapy showed a synergistic effect in 2 human esophageal carcinoma cell lines and an additive effect in 5 cell lines. Western blotting demonstrated that gefitinib blocked activation of the EGFR-extracellular signal-regulated kinase (Erk) pathway and the EGFR-phosphoinositide-3 kinase (PI3K)-Akt pathway after irradiation. These results suggest that further evaluation of EGFR blockade as a treatment for esophageal cancer should be performed, and that radiotherapy combined with EGFR blockade may enhance the response of esophageal carcinoma to therapy.</p

キカイ ガクシュウ オ モチイタ テキスト ブンルイ

博士 (Doctor)工学 (Engineering)博第231号甲第231号奈良先端科学技術大学院大

Text Categorization

With the rapid spread of the Internet and the increase in on-line information, the technology for automatically classifying huge amounts of diverse text information has come to play a very important role. In the 1990s, the performance of computers improved sharply and it became possible to handle large quantities of text data. This led to the use of the machine learning approach, which is a method of creating classifiers automatically from the text data given in a category label. This approach provides excellent accuracy, reduces labor, and ensures conservative use of resources. This paper discusses the following three points related to text classification using machine learning. 1. How to perform highly precise classification by using a large number of word attributes (Chapter 3). 2. How to utilize the distribution of unlabeled examples for high precision when there are few labeled training examples (Chapter 4). 3. How to achieve a highly precise and efficient classification by assuming the existence of sub-categories and using active labeling (Chapter 5)

A Japanese predicate argument structure analysis using decision lists

Maintaining high annotation consistency in large corpora is crucial for statistical learning; however, such work is hard, especially for tasks containing semantic elements. This paper describes predi-cate argument structure analysis using transformation-based learning. An advan-tage of transformation-based learning is the readability of learned rules. A dis-advantage is that the rule extraction pro-cedure is time-consuming. We present incremental-based, transformation-based learning for semantic processing tasks. As an example, we deal with Japanese pred-icate argument analysis and show some tendencies of annotators for constructing a corpus with our method.