Search Results for author:
Found 8 papers, 3 papers with code
Hierarchical User and Item Representation with Three-Tier Attention for Recommendation
In this paper, we propose a hierarchical user and item representation model with three-tier attention to learn user and item representations from reviews for recommendation.
Neural Chinese Word Segmentation with Lexicon and Unlabeled Data via Posterior Regularization
Luckily, the unlabeled data is usually easy to collect and many high-quality Chinese lexicons are off-the-shelf, both of which can provide useful information for CWS.
Neural Chinese Named Entity Recognition via CNN-LSTM-CRF and Joint Training with Word Segmentation
Besides, the training data for CNER in many domains is usually insufficient, and annotating enough training data for CNER is very expensive and time-consuming.
Chinese Named Entity Recognition
named-entity-recognition
+1
Detecting Tweets Mentioning Drug Name and Adverse Drug Reaction with Hierarchical Tweet Representation and Multi-Head Self-Attention
This paper describes our system for the first and third shared tasks of the third Social Media Mining for Health Applications (SMM4H) workshop, which aims to detect the tweets mentioning drug names and adverse drug reactions.
Neural Chinese Word Segmentation with Dictionary Knowledge
The experimental results on two benchmark datasets validate that our approach can effectively improve the performance of Chinese word segmentation, especially when training data is insufficient.
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 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.
