Extracting entity and relationship interactions from danmaku-video comments using a neural bootstrapping framework

Danmaku-enabled videos have revolutionized the way online users engage with and enjoy video content, and creating a dynamic and interactive atmosphere for users. In this paper, we investigate a new task of extract entity and relation on video-sync comments, which holds the potential to facilitate the creation of semantic-driven video knowledge graphs and video content analysis. However, the task is difficult since video-sync comments often contain intricate, ambiguous relationships, and their syntactic structure may be incomplete or inconsistent. One possible method is to extract relationships by using distant supervision or an external knowledge base system (Kbs). Regrettably, the domain of online videos suffers from a lack of comprehensive external KBs. To tackle the problem, we propose an innovative strategy that combines syntactic rule-based techniques with advanced pre-trained language models. We aim to encapsulate both syntactic cues and distinctive features within latent spaces. Our approach comprises two key facets. Firstly, we employ syntactic rules to extract open relations from the video-sync comments. Simultaneously, we introduce a BERT-based model that integrates relationship category constraints. This model not only fuses extraction features but also autonomously learns intricate relationships, enhancing the accuracy and granularity of the extraction process. For demonstration, we conduct experiments on large-scale danmaku data and results demonstrate that our proposed approach can achieve competitive performance.

Automated summary

By combining syntactic rule-based techniques with advanced pre-trained language models, this paper proposes an innovative strategy for extracting entities and relationships from danmaku comments. Experimental results demonstrate that this approach achieves competitive performance on large-scale data.