Pattern Information Processing
The research area of Pattern Information Processing involves the study of fundamental theories and practical applications related to pattern recognition and understanding. It mainly focuses on visual information processing and language information processing. The research targets also include the extraction of distinctive patterns from data and the study of methods for analyzing, recognizing, and utilizing these patterns. In the field of pattern information science, we utilize techniques and algorithms from machine learning, statistics, artificial intelligence, and data mining to design appropriate feature representations and construct identification models for images and text.
As an application of natural language processing, automated essay scoring is conducted. Evaluating student essays can be labor-intensive for human graders with arising issues of consistency in evaluations. To address these issues, we have developed an automated essay scoring model and a supporting tool for the essay grading process. By utilizing machine learning techniques, the target of this research is to establish a more reliable and efficient approach for scoring essays. Since Japanese essay datasets have been limited, we have constructed and provided a dataset of scored Japanese essays to the research community.