A Study of Deep Neural Network for Person Interaction Discovery

• Main Authors: Ting-Yu Lin, 林庭宇
• Other Authors: Wen-Lian Hsu
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/htf8qk

碩士 === 國立臺灣大學 === 資料科學學位學程 === 107 === The research topic of this paper is person interaction discovery. We are trying to identify interactions between different people mentioned in social media. To help readers construct a relationship between people under a certain topic, so that readers can quickly understand the text content of different topics. This study is based on the traditional kernel method proposed by Chang et al. We use the deep learning method to improve and integrate the traditional natural language features and tree structure into the neural network model. It utilizes entity embedding, rich interactive tree embedding, part of speech embedding, sentence categories, and dependency features. In this way, two tasks in the person interaction discovery - relation detection task and relation extraction task are completed. In addition, we also explore the multitasking model and hope to improve each other''s effectiveness through mutual assistance between task models. Our method in the relation detection task, eventually surpassed the original author''s paper by about 7% on the F1 score. At the same time, we have implemented a relation extraction model which the original author didn''t implement. It demonstrates superior performances on the person interaction extraction task. This is useful for building a knowledge base for people''s interactive networks.