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Found 7 papers, 1 papers with code
Named Entity Recognition in Multi-level Contexts
In this paper, we propose a unified framework to incorporate multi-level contexts for named entity recognition.
Neural Metaphor Detecting with CNN-LSTM Model
In addition, we compare the performance of the softmax classifier and conditional random field (CRF) for sequential labeling in this task.
THU\_NGN at SemEval-2018 Task 10: Capturing Discriminative Attributes with MLP-CNN model
Thus, the aim of SemEval-2018 Task 10 is to predict whether a word is a discriminative attribute between two concepts.
THU\_NGN at SemEval-2018 Task 2: Residual CNN-LSTM Network with Attention for English Emoji Prediction
Thus, in SemEval-2018 Task 2 an interesting and challenging task is proposed, i. e., predicting which emojis are evoked by text-based tweets.
THU\_NGN at SemEval-2018 Task 1: Fine-grained Tweet Sentiment Intensity Analysis with Attention CNN-LSTM
In order to address this task, we propose a system based on an attention CNN-LSTM model.
THU\_NGN at SemEval-2018 Task 3: Tweet Irony Detection with Densely connected LSTM and Multi-task Learning
Detecting irony is an important task to mine fine-grained information from social web messages.
THU\_NGN at IJCNLP-2017 Task 2: Dimensional Sentiment Analysis for Chinese Phrases with Deep LSTM
Since the existing valence-arousal resources of Chinese are mainly in word-level and there is a lack of phrase-level ones, the Dimensional Sentiment Analysis for Chinese Phrases (DSAP) task aims to predict the valence-arousal ratings for Chinese affective words and phrases automatically.
