The domain of Aspect-based Sentiment Analysis, in which aspects are extracted, their
sentiments are analysed and sentiments are evolved over time, is getting much attention …

Deep learning approaches have achieved great success in the field of Natural Language
Processing (NLP). However, directly training deep neural models often suffer from overfitting …

Aspect-based sentiment analysis produces a list of aspect terms and their corresponding
sentiments for a natural language sentence. This task is usually done in a pipeline manner …

In this paper, we propose the TBCNN-pair model to recognize entailment and contradiction
between two sentences. In our model, a tree-based convolutional neural network (TBCNN) …

Transfer learning is aimed to make use of valuable knowledge in a source domain to help
model performance in a target domain. It is particularly important to neural networks, which …

Attention-based long short-term memory (LSTM) networks have proven to be useful in
aspect-level sentiment classification. However, due to the difficulties in annotating aspect …

State-of-the-art studies have demonstrated the superiority of joint modeling over pipeline
implementation for medical named entity recognition and normalization due to the mutual …

Implicit discourse relation recognition (IDRR) is a challenging but crucial task in discourse
analysis. Most existing methods train multiple models to predict multi-level labels …

Many domain adaptation approaches rely on learning cross domain shared representations
to transfer the knowledge learned in one domain to other domains. Traditional domain …

Aspect-level sentiment classification aims to obtain fine-grained sentiment polarities of
different aspects in one sentence. Most existing approaches handle the classification by …