Keras Implementation of "End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF" by Ma Hovy et al 2016, on multimodal dataset from "Adaptive Co-attention Network for Named Entity Recognition in Tweets" paper AAAI 2018. Different datasets can be used.
- Python 2.7 or higher
- Keras 1.2 (the vesion including the CRF model), the backend is theano.
- Moreover, you need to download the word embedding trained by tweets from http://pan.baidu.com/s/1boSlljL.
Our datasets include 8,257 tweet and image pairs. We split the dataset into three parts: the training set, development set, and testing set, which contain 4,000, 1,000, and 3,257 tweets, respectively.
IMGID:418340
Rep O
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Howard B-PER
Coble I-PER
Preprocess datasets accordingly(for other datasets)
- Training the model: $ python CNN_BiLSTM_CRF.py
The Evaluation code to calculate F1, Precision, and recall is in ner_evaluate.py.