Generating radiology reports via memory-driven transformer
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 …
Writing imaging reports is time-consuming and can be error-prone for inexperienced …
Aspect-based sentiment analysis with type-aware graph convolutional networks and layer ensemble
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 …
analysis (ABSA) studies for utilizing word relations through dependency parses to facilitate …
Dependency-driven relation extraction with attentive graph convolutional networks
Syntactic information, especially dependency trees, has been widely used by existing
studies to improve relation extraction with better semantic guidance for analyzing the context …
studies to improve relation extraction with better semantic guidance for analyzing the context …
ZEN: Pre-training Chinese text encoder enhanced by n-gram representations
The pre-training of text encoders normally processes text as a sequence of tokens
corresponding to small text units, such as word pieces in English and characters in Chinese …
corresponding to small text units, such as word pieces in English and characters in Chinese …
Joint aspect extraction and sentiment analysis with directional graph convolutional networks
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 …
extracts the aspect terms in a sentence and the second predicts the sentiment polarities for …
Named entity recognition for social media texts with semantic augmentation
Existing approaches for named entity recognition suffer from data sparsity problems when
conducted on short and informal texts, especially user-generated social media content …
conducted on short and informal texts, especially user-generated social media content …
Improving named entity recognition with attentive ensemble of syntactic information
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 …
properties where entities may be extracted according to how they are used and placed in the …
Taming pre-trained language models with n-gram representations for low-resource domain adaptation
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 …
tasks, even when such a model is trained on a generic domain. Moreover, recent studies …
Benchclamp: A benchmark for evaluating language models on syntactic and semantic parsing
Recent work has shown that generation from a prompted or fine-tuned language model can
perform well at semantic parsing when the output is constrained to be a valid semantic …
perform well at semantic parsing when the output is constrained to be a valid semantic …
Bottom-up constituency parsing and nested named entity recognition with pointer networks
Constituency parsing and nested named entity recognition (NER) are similar tasks since
they both aim to predict a collection of nested and non-crossing spans. In this work, we cast …
they both aim to predict a collection of nested and non-crossing spans. In this work, we cast …