Adaptive Co-attention Network for Named Entity Recognition in Tweets

By Qi Zhang, Jinlan Fu, Xiaoyu Liu, Xuanjing Huang. AAAI Conference on Artificial Intelligence, 2018

425 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/5ca1c595708d22d92b3f913be391575560bdab2c

How researchers see this paper

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In Artificial Intelligence

1. Aixin Sun
2. Chenliang Li
3. Jianfei Yu
4. Jing Jiang
5. Rui Xia
6. Jianglei Han
7. Jing Li
8. Shuohang Wang

Citations in the corpus

• Improving Multimodal Named Entity Recognition via Entity Span Detection with Unified Multimodal Transformer (2020, methodology): “Besides, we also consider several competitive multimodal approaches for MNER: (1) GVATT-HBiLSTM-CRF (Lu et al., 2018), a state-of-the-art approach for MNER, which integrates HBiLSTM-CRF with the visual context by proposing a visual attention mechanism followed by a visual gate to obtain word-aware v”
• A Survey on Deep Learning for Named Entity Recognition (2018, methodology): “[172] used an adaptive co-attention network for NER in tweets.”