• Publications
  • Influence
Part-of-Speech Tagging for Twitter with Adversarial Neural Networks
TLDR
A novel neural network to make use of out-of-domain labeled data, unlabeled in- domain data, and labeled in-domain data is proposed to learn common features through adversarial discriminator for Tweets tagging. Expand
A Lexicon-Based Graph Neural Network for Chinese NER
TLDR
A lexicon-based graph neural network with global semantics is introduced, in which lexicon knowledge is used to connect characters to capture the local composition, while a global relay node can capture global sentence semantics and long-range dependency. Expand
CNN-Based Chinese NER with Lexicon Rethinking
TLDR
This work proposes a faster alternative to Chinese NER: a convolutional neural network (CNN)-based method that incorporates lexicons using a rethinking mechanism that can model all the characters and potential words that match the sentence in parallel. Expand
A Lexicon-Based Supervised Attention Model for Neural Sentiment Analysis
TLDR
A novel lexicon-based supervised attention model (LBSA) is proposed, which allows a recurrent neural network to focus on the sentiment content, thus generating sentiment-informative representations and has better interpretability and less noise. Expand
Switch-LSTMs for Multi-Criteria Chinese Word Segmentation
TLDR
This paper presents Switch-LSTMs to segment words, which consist of several long short-term memory neural networks (LSTM), and a switcher to automatically switch the routing among these LSTMs, which provides a more flexible solution for multi-criteria CWS, which is also easy to transfer the learned knowledge to new criteria. Expand
Cooperative Multimodal Approach to Depression Detection in Twitter
TLDR
Experimental results demonstrate that the proposed method outperforms state-of-the-art methods by a large margin, and the model can obtained a robust performance in realistic scenarios. Expand
Trainable Undersampling for Class-Imbalance Learning
TLDR
A meta-learning method built on the undersampling to parametrize the data sampler and train it to optimize the classification performance over the evaluation metric through reinforcement learning, which solves the non-differentiable optimization problem for training the data Sampler via reinforcement learning. Expand
Leveraging Document-Level Label Consistency for Named Entity Recognition
TLDR
This work introduces a novel two-stage label refinement approach to handle documentlevel label consistency, where a key-value memory network is first used to record draft labels predicted by the base model, and then a multi-channel Transformer makes refinements on these draft predictions based on the explicit co-occurrence relationship derived from the memory network. Expand
Uncertainty-Aware Label Refinement for Sequence Labeling
TLDR
This work introduces a novel two-stage label decoding framework to model long-term label dependencies, while being much more computationally efficient and mitigating the side effects of incorrect draft labels. Expand
A Unified Generative Framework for Various NER Subtasks
TLDR
This work proposes to formulate the NER subtasks as an entity span sequence generation task, which can be solved by a unified sequence-to-sequence (Seq2Seq) framework, and exploits three types of entity representations to linearize entities into a sequence. Expand
...
1
2
3
...