Process (computing)

Operating system

Economics

Computer science

Natural language processing

Named-entity recognition

Task (project management)

Conditional random field

Pattern recognition (psychology)

Management

Knowledge base

Entity linking

Artificial intelligence

Information retrieval

In this study, we investigate the problem of named entity recognition for tweets. Named entity recognition is an important task in natural language processing and has been carefully studied in recent decades. Previous named entity recognition methods usually only used the textual content when processing tweets. However, many tweets contain not only textual content, but also images. Such visual information is also valuable in the name entity recognition task. To make full use of textual and visual information, this paper proposes a novel method to process tweets that contain multimodal information. We extend a bi-directional long short term memory network with conditional random fields and an adaptive co-attention network to achieve this task. To evaluate the proposed methods, we constructed a large scale labeled dataset that contained multimodal tweets. Experimental results demonstrated that the proposed method could achieve a better performance than the previous methods in most cases.

Adaptive Co-attention Network for Named Entity Recognition in Tweets