A survey on recent advances in named entity recognition from deep learning models
Named Entity Recognition (NER) is a key component in NLP systems for question
answering, information retrieval, relation extraction, etc. NER systems have been studied …
answering, information retrieval, relation extraction, etc. NER systems have been studied …
Enriching contextualized language model from knowledge graph for biomedical information extraction
Biomedical information extraction (BioIE) is an important task. The aim is to analyze
biomedical texts and extract structured information such as named entities and semantic …
biomedical texts and extract structured information such as named entities and semantic …
COVID-19 literature knowledge graph construction and drug repurposing report generation
To combat COVID-19, both clinicians and scientists need to digest vast amounts of relevant
biomedical knowledge in scientific literature to understand the disease mechanism and …
biomedical knowledge in scientific literature to understand the disease mechanism and …
A neural joint model for entity and relation extraction from biomedical text
Background Extracting biomedical entities and their relations from text has important
applications on biomedical research. Previous work primarily utilized feature-based pipeline …
applications on biomedical research. Previous work primarily utilized feature-based pipeline …
An empirical study of multi-task learning on BERT for biomedical text mining
Multi-task learning (MTL) has achieved remarkable success in natural language processing
applications. In this work, we study a multi-task learning model with multiple decoders on …
applications. In this work, we study a multi-task learning model with multiple decoders on …
Broad-coverage biomedical relation extraction with SemRep
Background In the era of information overload, natural language processing (NLP)
techniques are increasingly needed to support advanced biomedical information …
techniques are increasingly needed to support advanced biomedical information …
Distantly supervised named entity recognition using positive-unlabeled learning
In this work, we explore the way to perform named entity recognition (NER) using only
unlabeled data and named entity dictionaries. To this end, we formulate the task as a …
unlabeled data and named entity dictionaries. To this end, we formulate the task as a …
Neural relation extraction within and across sentence boundaries
Past work in relation extraction mostly focuses on binary relation between entity pairs within
single sentence. Recently, the NLP community has gained interest in relation extraction in …
single sentence. Recently, the NLP community has gained interest in relation extraction in …
Towards reliable named entity recognition in the biomedical domain
Motivation Automatic biomedical named entity recognition (BioNER) is a key task in
biomedical information extraction. For some time, state-of-the-art BioNER has been …
biomedical information extraction. For some time, state-of-the-art BioNER has been …
Learning to compute word embeddings on the fly
Words in natural language follow a Zipfian distribution whereby some words are frequent but
most are rare. Learning representations for words in the" long tail" of this distribution …
most are rare. Learning representations for words in the" long tail" of this distribution …