Results 1 to 10 of about 32,474 (200)

Systematic analysis of hepatotoxicity: combining literature mining and AI language models. [PDF]

Front Artif Intell
Bauer C, Duc Dang LT, van den Beucken T, Schuchhardt J, Herwig R.   +4 more
europepmc   +1 more source

Interactive learning system neural network algorithm optimization. [PDF]

Sci Rep
Cao H, Yu X, Han P, Peng J, Li D.
europepmc   +1 more source

Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models: Quantitative Study Using Large Language Models.

J Med Internet Res
Sarwar T, Yepes AJJ, Cavedon L.
europepmc   +1 more source

Unveiling social determinants of health impact on adverse pregnancy outcomes through natural language processing. [PDF]

Sci Rep
Soley N, Bentil M, Shah J, Rouhizadeh M, Taylor CO.   +4 more
europepmc   +1 more source

Automated detection of corruption reports in text via deep reinforcement learning. [PDF]

Sci Rep
Xiao Q.
europepmc   +1 more source

Predicting Drug-Side Effect Relationships From Parametric Knowledge Embedded in Biomedical BERT Models: Methodological Study With a Natural Language Processing Approach.

JMIR Med Inform
Jeon W, Park M, An D, Nam W, Shin JY, Lee S, Lee S.   +6 more
europepmc   +1 more source

Adapting word2vec to Named Entity Recognition

, 2015
openaire   +1 more source

word2vec or JoBimText?

Proceedings of the 7th Forum for Information Retrieval Evaluation, 2015
Exploration of distributional semantics for NLP tasks in Indian languages has been scarce. This work carries out a comparative analysis of two recent and high performing distributional semantics techniques namely word2vec and JoBimText. The task of lexical expansion of words in Hindi is considered for the analysis. A manual similarity assessment of the
Nitin Ramrakhiyani, Sachin Pawar, Girish Palshikar   +2 more
openaire   +1 more source

Critical Dimension of Word2Vec

2019 2nd International Conference on Innovations in Electronics, Signal Processing and Communication (IESC), 2019
Word embeddings are an efficient way of representing text such that they can be used by different Machine Learning Algorithms. Word2Vec is one such word embedding model. Although it is highly efficient, this model can take up a lot of space to store the vector representations.
Shuvayanti Das, Sohan Ghosh, Shubham Bhattacharya, Rajat Varma, Dinabandhu Bhandari   +4 more
openaire   +1 more source