scholarly journals Pat-in-the-Loop: Declarative Knowledge for Controlling Neural Networks

2020 ◽  
Vol 12 (12) ◽  
pp. 218
Author(s):  
Dario Onorati ◽  
Pierfrancesco Tommasino ◽  
Leonardo Ranaldi ◽  
Francesca Fallucchi ◽  
Fabio Massimo Zanzotto
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Cost Effective ◽  
Specific Class ◽  
Human Control ◽  
The Neural Network ◽  
Parse Trees

The dazzling success of neural networks over natural language processing systems is imposing an urgent need to control their behavior with simpler, more direct declarative rules. In this paper, we propose Pat-in-the-Loop as a model to control a specific class of syntax-oriented neural networks by adding declarative rules. In Pat-in-the-Loop, distributed tree encoders allow to exploit parse trees in neural networks, heat parse trees visualize activation of parse trees, and parse subtrees are used as declarative rules in the neural network. Hence, Pat-in-the-Loop is a model to include human control in specific natural language processing (NLP)-neural network (NN) systems that exploit syntactic information, which we will generically call Pat. A pilot study on question classification showed that declarative rules representing human knowledge, injected by Pat, can be effectively used in these neural networks to ensure correctness, relevance, and cost-effective.

Prediction of Emergency Department Hospital Admission Based on Natural Language Processing and Neural Networks

2017 ◽  
Vol 56 (05) ◽  
pp. 377-389 ◽  
Author(s):  
Xingyu Zhang ◽  
Joyce Kim ◽  
Rachel E. Patzer ◽  
Stephen R. Pitts ◽  
Aaron Patzer ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Emergency Department ◽  
Logistic Regression ◽  
Natural Language Processing ◽  
Natural Language ◽  
Hospital Admission ◽  
Language Processing ◽  
Predictive Accuracy ◽  
Free Text

SummaryObjective: To describe and compare logistic regression and neural network modeling strategies to predict hospital admission or transfer following initial presentation to Emergency Department (ED) triage with and without the addition of natural language processing elements.Methods: Using data from the National Hospital Ambulatory Medical Care Survey (NHAMCS), a cross-sectional probability sample of United States EDs from 2012 and 2013 survey years, we developed several predictive models with the outcome being admission to the hospital or transfer vs. discharge home. We included patient characteristics immediately available after the patient has presented to the ED and undergone a triage process. We used this information to construct logistic regression (LR) and multilayer neural network models (MLNN) which included natural language processing (NLP) and principal component analysis from the patient’s reason for visit. Ten-fold cross validation was used to test the predictive capacity of each model and receiver operating curves (AUC) were then calculated for each model.Results: Of the 47,200 ED visits from 642 hospitals, 6,335 (13.42%) resulted in hospital admission (or transfer). A total of 48 principal components were extracted by NLP from the reason for visit fields, which explained 75% of the overall variance for hospitalization. In the model including only structured variables, the AUC was 0.824 (95% CI 0.818-0.830) for logistic regression and 0.823 (95% CI 0.817-0.829) for MLNN. Models including only free-text information generated AUC of 0.742 (95% CI 0.7310.753) for logistic regression and 0.753 (95% CI 0.742-0.764) for MLNN. When both structured variables and free text variables were included, the AUC reached 0.846 (95% CI 0.839-0.853) for logistic regression and 0.844 (95% CI 0.836-0.852) for MLNN.Conclusions: The predictive accuracy of hospital admission or transfer for patients who presented to ED triage overall was good, and was improved with the inclusion of free text data from a patient’s reason for visit regardless of modeling approach. Natural language processing and neural networks that incorporate patient-reported outcome free text may increase predictive accuracy for hospital admission.

scholarly journals Machine translation using natural language processing

2019 ◽  
Vol 277 ◽  
pp. 02004
Author(s):  
Middi Venkata Sai Rishita ◽  
Middi Appala Raju ◽  
Tanvir Ahmed Harris
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Deep Neural Network ◽  
English Text ◽  
French Translation ◽  
Rule Based

Machine Translation is the translation of text or speech by a computer with no human involvement. It is a popular topic in research with different methods being created, like rule-based, statistical and examplebased machine translation. Neural networks have made a leap forward to machine translation. This paper discusses the building of a deep neural network that functions as a part of end-to-end translation pipeline. The completed pipeline would accept English text as input and return the French Translation. The project has three main parts which are preprocessing, creation of models and Running the model on English Text.

scholarly journals Analysis of Neural Network Based Language Modeling

2020 ◽  
Vol 2 (1) ◽  
pp. 53-63
Author(s):  
Dr. Karrupusamy P.
Keyword(s):  
Neural Network ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Network Models ◽  
Research Field ◽  
Neural Network Models ◽  
Natural Languages ◽  
The Neural Network ◽  
Language Modelling

The fundamental and core process of the natural language processing is the language modelling usually referred as the statistical language modelling. The language modelling is also considered to be vital in the processing the natural languages as the other chores such as the completion of sentences, recognition of speech automatically, translations of the statistical machines, and generation of text and so on. The success of the viable natural language processing totally relies on the quality of the modelling of the language. In the previous spans the research field such as the linguistics, psychology, speech recognition, data compression, neuroscience, machine translation etc. As the neural network are the very good choices for having a quality language modelling the paper presents the analysis of neural networks in the modelling of the language. Utilizing some of the dataset such as the Penn Tree bank, Billion Word Benchmark and the Wiki Test the neural network models are evaluated on the basis of the word error rate, perplexity and the bilingual evaluation under study scores to identify the optimal model.

scholarly journals Analysis of Neural Network Based Language Modeling

2020 ◽  
Vol 2 (1) ◽  
pp. 53-63
Author(s):  
Dr. Karrupusamy P.
Keyword(s):  
Neural Network ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Network Models ◽  
Research Field ◽  
Neural Network Models ◽  
Natural Languages ◽  
The Neural Network ◽  
Language Modelling

The fundamental and core process of the natural language processing is the language modelling usually referred as the statistical language modelling. The language modelling is also considered to be vital in the processing the natural languages as the other chores such as the completion of sentences, recognition of speech automatically, translations of the statistical machines, and generation of text and so on. The success of the viable natural language processing totally relies on the quality of the modelling of the language. In the previous spans the research field such as the linguistics, psychology, speech recognition, data compression, neuroscience, machine translation etc. As the neural network are the very good choices for having a quality language modelling the paper presents the analysis of neural networks in the modelling of the language. Utilizing some of the dataset such as the Penn Tree bank, Billion Word Benchmark and the Wiki Test the neural network models are evaluated on the basis of the word error rate, perplexity and the bilingual evaluation under study scores to identify the optimal model.

scholarly journals Tackling Graphical Natural Language Processing’s Problems with Recurrent Neural Networks

2019 ◽  
Vol 54 (5) ◽  
Author(s):  
Ali Sami Sosa ◽  
Saja Majeed Mohammed ◽  
Haider Hadi Abbas ◽  
Israa Al Barazanchi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Recurrent Neural Network ◽  
Recurrent Neural Networks ◽  
Question Answering ◽  
Structural Information ◽  
Sequential Data

Recent years have witnessed the success of artificial intelligence–based automated systems that use deep learning, especially recurrent neural network-based models, on many natural language processing problems, including machine translation and question answering. Besides, recurrent neural networks and their variations have been extensively studied with respect to several graph problems and have shown preliminary success. Despite these successes, recurrent neural network -based models continue to suffer from several major drawbacks. First, they can only consume sequential data; thus, linearization is required to serialize input graphs, resulting in the loss of important structural information. In particular, graph nodes that are originally located closely to each other can be very far away after linearization, and this introduces great challenges for recurrent neural networks to model their relation. Second, the serialization results are usually very long, so it takes a long time for recurrent neural networks to encode them. In the methodology of this study, we made the resulting graphs more densely connected so that more useful facts could be inferred, and the problem of graphical natural language processing could be easily decoded with graph recurrent neural network. As a result, the performances with single-typed edges were significantly better than the Local baseline, whereas the combination of all types of edges achieved a much better accuracy than just that of the Local using recurrent neural network. In this paper, we propose a novel graph neural network, named graph recurrent network.

scholarly journals Cyberbullying Detection: Hybrid Models Based on Machine Learning and Natural Language Processing Techniques

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2810
Author(s):  
Chahat Raj ◽  
Ayush Agarwal ◽  
Gnana Bharathy ◽  
Bhuva Narayan ◽  
Mukesh Prasad
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Feature Extraction ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Machine Learning Algorithms ◽  
Classification Methods ◽  
Cyberbullying Detection

The rise in web and social media interactions has resulted in the efortless proliferation of offensive language and hate speech. Such online harassment, insults, and attacks are commonly termed cyberbullying. The sheer volume of user-generated content has made it challenging to identify such illicit content. Machine learning has wide applications in text classification, and researchers are shifting towards using deep neural networks in detecting cyberbullying due to the several advantages they have over traditional machine learning algorithms. This paper proposes a novel neural network framework with parameter optimization and an algorithmic comparative study of eleven classification methods: four traditional machine learning and seven shallow neural networks on two real world cyberbullying datasets. In addition, this paper also examines the effect of feature extraction and word-embedding-techniques-based natural language processing on algorithmic performance. Key observations from this study show that bidirectional neural networks and attention models provide high classification results. Logistic Regression was observed to be the best among the traditional machine learning classifiers used. Term Frequency-Inverse Document Frequency (TF-IDF) demonstrates consistently high accuracies with traditional machine learning techniques. Global Vectors (GloVe) perform better with neural network models. Bi-GRU and Bi-LSTM worked best amongst the neural networks used. The extensive experiments performed on the two datasets establish the importance of this work by comparing eleven classification methods and seven feature extraction techniques. Our proposed shallow neural networks outperform existing state-of-the-art approaches for cyberbullying detection, with accuracy and F1-scores as high as ~95% and ~98%, respectively.

scholarly journals Compression of Convolutional Neural Network for Natural Language Processing

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Krzysztof Wróbel ◽  
Michał Karwatowski ◽  
Maciej Wielgosz ◽  
Marcin Pietroń ◽  
Kazimierz Wiatr
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Original Model ◽  
Floating Point ◽  
Memory Footprint ◽  
Classification Tasks

Convolutional Neural Networks (CNNs) were created for image classification tasks. Quickly, they were applied to other domains, including Natural Language Processing (NLP). Nowadays, the solutions based on artificial intelligence appear on mobile devices and in embedded systems, which places constraints on, among others, the memory and power consumption. Due to CNNs memory and computing requirements, to map them to hardware they need to be compressed.This paper presents the results of compression of the efficient CNNs for sentiment analysis. The main steps involve pruning and quantization. The process of mapping the compressed network to FPGA and the results of this implementation are described. The conducted simulations showed that 5-bit width is enough to ensure no drop in accuracy when compared to the floating point version of the network. Additionally, the memory footprint was significantly reduced (between 85% and 93% comparing to the original model).

scholarly journals Physics Inspired Deep Neural Networks for Top Quark Reconstruction

2020 ◽  
Vol 245 ◽  
pp. 06029
Author(s):  
Kevin Greif ◽  
Kevin Lannon
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Computer Vision ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Top Quark ◽  
Deep Neural Networks ◽  
Great Success ◽  
Fully Connected

Deep neural networks (DNNs) have been applied to the fields of computer vision and natural language processing with great success in recent years. The success of these applications has hinged on the development of specialized DNN architectures that take advantage of specific characteristics of the problem to be solved, namely convolutional neural networks for computer vision and recurrent neural networks for natural language processing. This research explores whether a neural network architecture specific to the task of identifying t → Wb decays in particle collision data yields better performance than a generic, fully-connected DNN. Although applied here to resolved top quark decays, this approach is inspired by an DNN technique for tagging boosted top quarks, which consists of defining custom neural network layers known as the combination and Lorentz layers. These layers encode knowledge of relativistic kinematics applied to combinations of particles, and the output of these specialized layers can then be fed into a fully connected neural network to learn tasks such as classification. This research compares the performance of these physics inspired networks to that of a generic, fully-connected DNN, to see if there is any advantage in terms of classification performance, size of the network, or ease of training.

scholarly journals Convolution Neural Networks for Blind Image Steganalysis: A Comprehensive Study

2019 ◽  
Vol 11 (2) ◽  
pp. 53-64
Author(s):  
Hanaa Mohsin Ahmed ◽  
Halah Hasan Mahmoud
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
High Accuracy ◽  
Image Steganalysis ◽  
Scene Labeling ◽  
Blind Image ◽  
Comprehensive Study

Recently, Convolution Neural Network is widely applied in Image Classification, Object Detection, Scene labeling, Speech, Natural Language Processing and other fields. In this comprehensive study a variety of scenarios and efforts are surveyed since 2014 at yet, in order to provide a guide to further improve future researchers what CNN-based blind image steganalysis are presented its architecture, performance and limitations. Long-standing and important problem in image steganalysis difficulties mainly lie in how to give high accuracy and low payload in stego or cover images for improving performance of the network.  

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