Named Entity Recognition (NER) in social media posts is challenging since texts are usually short and contexts are lacking. Most recent works show that visual information can boost the NER performance since images can provide complementary contextual information for texts. However, the image-level features ignore the mapping relations between fine-grained visual objects and textual entities, which results in error detection in entities with different types. To better exploit visual and textual information in NER, we propose an adversarial gated bilinear attention neural network (AGBAN). The model jointly extracts entity-related features from both visual objects and texts, and leverages an adversarial training to map two different representations into a shared representation. As a result, domain information contained in an image can be transferred and applied for extracting named entities in the text associated with the image. Experimental results on Tweets dataset demonstrate that our model outperforms the state-of-the-art methods. Moreover, we systematically evaluate the effectiveness of the proposed gated bilinear attention network in capturing the interactions of mutimodal features visual objects and textual words. Our results indicate that the adversarial training can effectively exploit commonalities across heterogeneous data sources, which leads to improved performance in NER when compared to models purely exploiting text data or combining the image-level visual features.

Changmeng Zheng

Zhiwei Wu

Tao Wang

Yi Cai

Qing Li

IEEE Transactions on Multimedia

期刊的范围是多媒体技术和多媒体应用研究的各个方面，包括但不限于电路、网络、信号处理、系统、软件和系统集成，以“感兴趣的领域”为代表。赞助商。

The IEEE Transactions on Multimedia journal covers various aspects of multimedia technology and application research, including but not limited to circuits, networks, signal processing, systems, software, and system integration. The journal is sponsored by stakeholders in the field.

《IEEE Transactions on Multimedia》期刊涵盖多媒体技术及其应用研究的各个方面，包括但不限于电路、网络、信号处理、系统、软件和系统集成等领域。该期刊由相关领域的赞助商支持。

IEEE Trans. Multimedia

Object-Aware Multimodal Named Entity Recognition in Social Media Posts With Adversarial Learning

An adversarial gated bilinear attention neural network that jointly extracts entity-related features from both visual objects and texts, and leverages an adversarial training to map two different representations into a shared representation leads to improved performance in NER when compared to models purely exploiting text data or combining the image-level visual features.

Category	Quartile
计算机科学	1
计算机科学, 计算机信息系统	1
计算机科学, 计算机软件工程	1
计算机科学, 电信学	2

Object-Aware Multimodal Named Entity Recognition in Social Media Posts With Adversarial Learning

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