Journal ArticleDOI
An introduction to Deep Learning in Natural Language Processing: Models, techniques, and tools
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TLDR
In this paper , a survey of the application of deep learning techniques in NLP, with a focus on the various tasks where deep learning is demonstrating stronger impact, is presented, including software, hardware, and popular corpora.About:
This article is published in Neurocomputing.The article was published on 2022-01-01. It has received 74 citations till now. The article focuses on the topics: Computer science & Deep learning.read more
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Evolutionary deep learning: A survey
TL;DR: In this paper , a large number of researches have proposed evolutionary deep learning (EDL) algorithms to optimize deep learning, so called EDL, which have obtained promising results.
Journal ArticleDOI
FinBERT : A Large Language Model for Extracting Information from Financial Text†
Allen Huang,Hui Wang,Yi Yang +2 more
TL;DR: This paper developed FinBERT, a state-of-the-art large language model that adapts to the finance domain, and used it to identify the positive or negative sentiment of sentences that other algorithms mislabel as neutral.
Journal ArticleDOI
An unsupervised method for social network spammer detection based on user information interests
TL;DR: In this article , a pure unsupervised approach for spammer detection based on peer acceptance of a user in a social network to distinguish spammers from genuine users is presented, which does not require labeled training datasets.
Journal ArticleDOI
Aggression Detection in Social Media from Textual Data Using Deep Learning Models
TL;DR: This work extracted eight novel emotional features and used a newly designed deep neural network with only three numbers of layers to identify aggressive statements and achieves an F1 score of 97%, surpassing the state-of-the-art models by a significant margin.
Journal ArticleDOI
Energy Sector Enterprises in Digitalization Program: Its Implication for Open Innovation
TL;DR: In this paper , the authors developed a methodology and to determine the level of digitalization in the energy sector in an intercountry context, based on the methods of comparison and analysis, the work analyses the concept of "digitalization" and defines the indicators applied to the assessment of digitalisation.
References
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Proceedings ArticleDOI
SQuAD: 100,000+ Questions for Machine Comprehension of Text
TL;DR: The Stanford Question Answering Dataset (SQuAD) as mentioned in this paper is a reading comprehension dataset consisting of 100,000+ questions posed by crowdworkers on a set of Wikipedia articles, where the answer to each question is a segment of text from the corresponding reading passage.
Proceedings ArticleDOI
Introduction to the CoNLL-2003 shared task: language-independent named entity recognition
TL;DR: The CoNLL-2003 shared task on NER as mentioned in this paper was the first NER task with language-independent named entity recognition (NER) data sets and evaluation method, and a general overview of the systems that participated in the task and their performance.
Journal ArticleDOI
BioBERT: a pre-trained biomedical language representation model for biomedical text mining.
TL;DR: This article proposed BioBERT (Bidirectional Encoder Representations from Transformers for Biomedical Text Mining), which is a domain-specific language representation model pre-trained on large-scale biomedical corpora.
Journal ArticleDOI
IEMOCAP: interactive emotional dyadic motion capture database
Carlos Busso,Murtaza Bulut,Chi-Chun Lee,Abe Kazemzadeh,Emily Mower,Samuel Kim,Jeannette N. Chang,Sungbok Lee,Shrikanth S. Narayanan +8 more
TL;DR: A new corpus named the “interactive emotional dyadic motion capture database” (IEMOCAP), collected by the Speech Analysis and Interpretation Laboratory at the University of Southern California (USC), which provides detailed information about their facial expressions and hand movements during scripted and spontaneous spoken communication scenarios.
Journal ArticleDOI
Natural Questions: A Benchmark for Question Answering Research
Tom Kwiatkowski,Jennimaria Palomaki,Olivia Redfield,Michael Collins,Ankur P. Parikh,Chris Alberti,Danielle Epstein,Illia Polosukhin,Jacob Devlin,Kenton Lee,Kristina Toutanova,Llion Jones,Matthew Kelcey,Ming-Wei Chang,Andrew M. Dai,Jakob Uszkoreit,Quoc V. Le,Slav Petrov +17 more
TL;DR: The Natural Questions corpus, a question answering data set, is presented, introducing robust metrics for the purposes of evaluating question answering systems; demonstrating high human upper bounds on these metrics; and establishing baseline results using competitive methods drawn from related literature.