Existing conversational recommendation (CR) systems usually suffer from insufficient item
information when conducted on short dialogue history and unfamiliar items. Incorporating …

Medical imaging is frequently used in clinical practice and trials for diagnosis and treatment.
Writing imaging reports is time-consuming and can be error-prone for inexperienced …

It is popular that neural graph-based models are applied in existing aspect-based sentiment
analysis (ABSA) studies for utilizing word relations through dependency parses to facilitate …

Syntactic information, especially dependency trees, has been widely used by existing
studies to improve relation extraction with better semantic guidance for analyzing the context …

Existing approaches for named entity recognition suffer from data sparsity problems when
conducted on short and informal texts, especially user-generated social media content …

End-to-end aspect-based sentiment analysis (EASA) consists of two sub-tasks: the first
extracts the aspect terms in a sentence and the second predicts the sentiment polarities for …

Large pre-trained models such as BERT are known to improve different downstream NLP
tasks, even when such a model is trained on a generic domain. Moreover, recent studies …

Named entity recognition (NER) is highly sensitive to sentential syntactic and semantic
properties where entities may be extracted according to how they are used and placed in the …

Summarization is an important natural language processing (NLP) task in identifying key
information from text. For conversations, the summarization systems need to extract salient …

Aspect-level sentiment analysis (ASA) has received much attention in recent years. Most
existing approaches tried to leverage syntactic information, such as the dependency parsing …