AIMC Topic: Language

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SensiMix: Sensitivity-Aware 8-bit index & 1-bit value mixed precision quantization for BERT compression.

PloS one
Given a pre-trained BERT, how can we compress it to a fast and lightweight one while maintaining its accuracy? Pre-training language model, such as BERT, is effective for improving the performance of natural language processing (NLP) tasks. However, ...

Answering medical questions in Chinese using automatically mined knowledge and deep neural networks: an end-to-end solution.

BMC bioinformatics
BACKGROUND: Medical information has rapidly increased on the internet and has become one of the main targets of search engine use. However, medical information on the internet is subject to the problems of quality and accessibility, so ordinary users...

Intelligent Recognition Model of Business English Translation Based on Improved GLR Algorithm.

Computational intelligence and neuroscience
Aiming at the problem of low accuracy of traditional algorithm model, an intelligent recognition model of business English translation based on an improved GLR algorithm is proposed. Through this algorithm, the automatic sentence recognition technolo...

Visualization of Customized Convolutional Neural Network for Natural Language Recognition.

Sensors (Basel, Switzerland)
For analytical approach-based word recognition techniques, the task of segmenting the word into individual characters is a big challenge, specifically for cursive handwriting. For this, a holistic approach can be a better option, wherein the entire w...

When BERT meets Bilbo: a learning curve analysis of pretrained language model on disease classification.

BMC medical informatics and decision making
BACKGROUND: Natural language processing (NLP) tasks in the health domain often deal with limited amount of labeled data due to high annotation costs and naturally rare observations. To compensate for the lack of training data, health NLP researchers ...

Human-Computer Interaction with Detection of Speaker Emotions Using Convolution Neural Networks.

Computational intelligence and neuroscience
Emotions play an essential role in human relationships, and many real-time applications rely on interpreting the speaker's emotion from their words. Speech emotion recognition (SER) modules aid human-computer interface (HCI) applications, but they ar...

PhenoRerank: A re-ranking model for phenotypic concept recognition pre-trained on human phenotype ontology.

Journal of biomedical informatics
The study aims at developing a neural network model to improve the performance of Human Phenotype Ontology (HPO) concept recognition tools. We used the terms, definitions, and comments about the phenotypic concepts in the HPO database to train our mo...

A Question-and-Answer System to Extract Data From Free-Text Oncological Pathology Reports (CancerBERT Network): Development Study.

Journal of medical Internet research
BACKGROUND: Information in pathology reports is critical for cancer care. Natural language processing (NLP) systems used to extract information from pathology reports are often narrow in scope or require extensive tuning. Consequently, there is growi...

A BERT based dual-channel explainable text emotion recognition system.

Neural networks : the official journal of the International Neural Network Society
In this paper, a novel dual-channel system for multi-class text emotion recognition has been proposed, and a novel technique to explain its training & predictions has been developed. The architecture of the proposed system contains the embedding modu...

Chinese clinical named entity recognition via multi-head self-attention based BiLSTM-CRF.

Artificial intelligence in medicine
Clinical named entity recognition (CNER) is a fundamental step for many clinical Natural Language Processing (NLP) systems, which aims to recognize and classify clinical entities such as diseases, symptoms, exams, body parts and treatments in clinica...