| 研究課題/領域番号 |
19K00850
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| 研究機関 | 会津大学 |
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研究代表者 |
BLAKE John 会津大学, コンピュータ理工学部, 准教授 (80635954)
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| 研究分担者 |
Mozgovoy Maxim 会津大学, コンピュータ理工学部, 准教授 (60571776)
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| 研究期間 (年度) |
2019-04-01 – 2022-03-31
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| キーワード | lexical patterns / grammatical patterns / genre / feature visualization |
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| 研究実績の概要 |
In the first year we have achieved all our target objectives. We annotated a small corpus of short research articles that will form the dataset of the feature visualizer. We have also created a number of explanatory videos to be displayed in the online feature detector. We created some low-fidelity and high-fidelity prototypes in order to select a user-friendly interface with the required functionalities. The base for the feature visualizer was created using Django and Vue.js. This is now deployed online.We have also made progress on the second-year goals. We created software programs that can automatically identify grammatical tenses and voice in Python. We have created an initial prototype for the feature detector, which will allow users to input their own texts for analysis.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
1: 当初の計画以上に進展している
理由
We have been able to address some of the goals set for the second year. In addition to creating programs that match pre-annotated segments of texts, we have created programs that run on raw text. Initially, we expected to have to rely on using annotations to visualize complex features such as tense and aspect. However, we were able to create a program that works on raw text. This alleviates the need for additional annotations. These functionalities will be incorporated into both the feature visualizer and the feature detector. A prototype for the feature detector is currently deployed online via Heroku. The deployed feature detector currently incorporates readability statistics and lexical profiles (using academic word and academic vocabulary lists).
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| 今後の研究の推進方策 |
In the second year, we aim to improve the feature visualizer by integrating more functionalities, such as tense-aspect identification and various types of information structure (e.g. information flow, information focus and end weight). Our focus will be on developing programs that work on natural language without the need for pre-annotation. This will enable the same functionalities to be deployed in the feature visualizer for the pre-annotated corpus and for the feature detector that is designed for users to input their own texts. The key challenge will be to increase the accuracy and precision of the pattern-matching functions.
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| 次年度使用額が生じた理由 |
The balance of approximately 15000 will be added to the second-year budget.
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