计算机科学
关系抽取
模态(人机交互)
情报检索
信息抽取
人工智能
关系(数据库)
互联网
自然语言处理
多样性(控制论)
模式
特征提取
数据挖掘
万维网
社会科学
社会学
标识
DOI:10.1145/3539618.3591790
摘要
Multimodal Named Entity Recognition (MNER) and Multimodal Relation Extraction (MRE) are tasks in information retrieval that aim to recognize entities and extract relations among them using information from multiple modalities, such as text and images. Although current methods have attempted a variety of modality fusion approaches to enhance the information in text, a large amount of readily available internet retrieval data has not been considered. Therefore, we attempt to retrieve real-world text related to images, objects, and entire sentences from the internet and use this retrieved text as input for cross-modal fusion to improve the performance of entity and relation extraction tasks in the text.
科研通智能强力驱动
Strongly Powered by AbleSci AI