以深度學習方法探索人物互動關係之研究

Abstract

本文的研究主題為人物互動關係之探索，我們試圖識別社交媒體中提到的不同人物之間的互動關係，藉此幫助讀者建構出在某個主題下，不同人物之間的關係背景，加快讀者理解不同主題的文本內容。此研究基於 Chang et al.提出的傳統內核方法，我們以深度學習方法做改良，並將傳統的自然語言特徵與樹結構融合進神經網路模型中，其中利用了實體嵌入、豐富互動樹嵌入、詞性嵌入、句子類別和依賴特徵，藉此完成人物互動關係探索中的兩個任務－關係偵測任務與關係擷取任務，另外我們還對多任務模型進行探討，希望透過兩任務模型之間的互相輔助來提升彼此的效能，我們的方法在關係偵測任務中，最終在F1分數上超越了原作者論文約7%，達到了中文人物互動關係偵測到目前為止最好的效能表現，同時我們實作了原作者論文中所沒有實作的關係擷取任務，並且在效能方面有不錯的表現，這對於建構人物互動網絡的知識庫很有用。

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.