An introduction to deep learning in natural language processing: Models, techniques, and tools
Abstract Natural Language Processing (NLP) is a branch of artificial intelligence that
involves the design and implementation of systems and algorithms able to interact through …
involves the design and implementation of systems and algorithms able to interact through …
[HTML][HTML] A review of research on neuromarketing using content analysis: key approaches and new avenues
L Robaina-Calderín, JD Martín-Santana - Cognitive Neurodynamics, 2021 - Springer
There is currently a growing interest in a deeper understanding of consumer behaviour. In
this context, the union of different disciplines such as neuroscience and marketing has given …
this context, the union of different disciplines such as neuroscience and marketing has given …
Survey on the Biomedical Text Summarization Techniques with an Emphasis on Databases, Techniques, Semantic Approaches, Classification Techniques, and …
Biomedical text summarization (BTS) is proving to be an emerging area of work and
research with the need for sustainable healthcare applications such as evidence-based …
research with the need for sustainable healthcare applications such as evidence-based …
OGER++: hybrid multi-type entity recognition
Background We present a text-mining tool for recognizing biomedical entities in scientific
literature. OGER++ is a hybrid system for named entity recognition and concept recognition …
literature. OGER++ is a hybrid system for named entity recognition and concept recognition …
An attention based bi-LSTM DenseNet model for named entity recognition in english texts
B VeeraSekharReddy, KS Rao, N Koppula - Wireless Personal …, 2023 - Springer
Abstract Named Entity Recognition (NER), a popular method that is used for recognizing
entities that are present in a text document. It is a method for processing natural language …
entities that are present in a text document. It is a method for processing natural language …
A Gated Recurrent Unit based architecture for recognizing ontology concepts from biological literature
P Devkota, SD Mohanty, P Manda - BioData Mining, 2022 - Springer
Background Annotating scientific literature with ontology concepts is a critical task in biology
and several other domains for knowledge discovery. Ontology based annotations can power …
and several other domains for knowledge discovery. Ontology based annotations can power …
CoCoScore: context-aware co-occurrence scoring for text mining applications using distant supervision
Motivation Information extraction by mining the scientific literature is key to uncovering
relations between biomedical entities. Most existing approaches based on natural language …
relations between biomedical entities. Most existing approaches based on natural language …
[HTML][HTML] MultiGBS: A multi-layer graph approach to biomedical summarization
Automatic text summarization methods generate a shorter version of the input text to assist
the reader in gaining a quick yet informative gist. Existing text summarization methods …
the reader in gaining a quick yet informative gist. Existing text summarization methods …
Improving named entity recognition for biomedical and patent data using bi-LSTM deep neural network models
The daily exponential increase of biomedical information in scientific literature and patents is
a main obstacle to foster advances in biomedical research. A fundamental step hereby is to …
a main obstacle to foster advances in biomedical research. A fundamental step hereby is to …
Concept recognition as a machine translation problem
Background Automated assignment of specific ontology concepts to mentions in text is a
critical task in biomedical natural language processing, and the subject of many open …
critical task in biomedical natural language processing, and the subject of many open …