Text processing by using projective ART neural networks

2016 ◽  
Author(s):  
Radoslav Forgac ◽  
Roman Krakovsky
Keyword(s):  
Neural Networks ◽  
Text Processing ◽  
Projective Art

Advances in Computational Linguistics and Text Processing Frameworks

2020 ◽  
pp. 217-244
Author(s):  
Ayush Srivastav ◽  
Hera Khan ◽  
Amit Kumar Mishra
Keyword(s):  
Neural Networks ◽  
Natural Language Processing ◽  
Natural Language ◽  
Computational Linguistics ◽  
Language Processing ◽  
Text Processing ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Named Entity ◽  
Part Of Speech

The chapter provides an eloquent account of the major methodologies and advances in the field of Natural Language Processing. The most popular models that have been used over time for the task of Natural Language Processing have been discussed along with their applications in their specific tasks. The chapter begins with the fundamental concepts of regex and tokenization. It provides an insight to text preprocessing and its methodologies such as Stemming and Lemmatization, Stop Word Removal, followed by Part-of-Speech tagging and Named Entity Recognition. Further, this chapter elaborates the concept of Word Embedding, its various types, and some common frameworks such as word2vec, GloVe, and fastText. A brief description of classification algorithms used in Natural Language Processing is provided next, followed by Neural Networks and its advanced forms such as Recursive Neural Networks and Seq2seq models that are used in Computational Linguistics. A brief description of chatbots and Memory Networks concludes the chapter.

scholarly journals Classification of Retail Products: From Probabilistic Ranking to Neural Networks

2021 ◽  
Vol 11 (9) ◽  
pp. 4117
Author(s):  
Manar Mohamed Hafez ◽  
Ana Fernández Vilas ◽  
Rebeca P. Díaz Redondo ◽  
Héctor Olivera Pazó
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Text Processing ◽  
Learning Algorithms ◽  
Digital Transformation ◽  
Machine Learning Algorithms ◽  
Quality Data ◽  
High Dispersion ◽  
Food Retailing ◽  
Quality Of Data

Food retailing is now on an accelerated path to a success penetration into the digital market by new ways of value creation at all stages of the consumer decision process. One of the most important imperatives in this path is the availability of quality data to feed all the process in digital transformation. However, the quality of data are not so obvious if we consider the variety of products and suppliers in the grocery market. Within this context of digital transformation of grocery industry, Midiadia is a Spanish data provider company that works on converting data from the retailers’ products into knowledge with attributes and insights from the product labels that is maintaining quality data in a dynamic market with a high dispersion of products. Currently, they manually categorize products (groceries) according to the information extracted directly (text processing) from the product labelling and packaging. This paper introduces a solution to automatically categorize the constantly changing product catalogue into a 3-level food taxonomy. Our proposal studies three different approaches: a score-based ranking method, traditional machine learning algorithms, and deep neural networks. Thus, we provide four different classifiers that support a more efficient and less error-prone maintenance of groceries catalogues, the main asset of the company. Finally, we have compared the performance of these three alternatives, concluding that traditional machine learning algorithms perform better, but closely followed by the score-based approach.

scholarly journals Outlier Detection in High Dimensional Data

2021 ◽  
Vol 10 (5) ◽  
pp. 128-130
Author(s):  
Anusha L. ◽  
Nagaraja G S
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Decision Making ◽  
Outlier Detection ◽  
Text Processing ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Human Intelligence ◽  
Over Time

Artificial intelligence (AI) is the science that allows computers to replicate human intelligence in areas such as decision-making, text processing, visual perception. Artificial Intelligence is the broader field that contains several subfields such as machine learning, robotics, and computer vision. Machine Learning is a branch of Artificial Intelligence that allows a machine to learn and improve at a task over time. Deep Learning is a subset of machine learning that makes use of deep artificial neural networks for training. The paper proposed on outlier detection for multivariate high dimensional data for Autoencoder unsupervised model.

scholarly journals Text Summarization Technique for Punjabi Language Using Neural Networks

2021 ◽  
Author(s):  
Arti Jain ◽  
Anuja Arora ◽  
Divakar Yadav ◽  
Jorge Morato ◽  
Amanpreet Kaur
Keyword(s):  
Neural Networks ◽  
Text Processing ◽  
Morphological Structure ◽  
Activation Function ◽  
Automated System ◽  
Daily Routine ◽  
Indian Languages ◽  
Extractive Summarization ◽  
Text Document ◽  
Single Text

In the contemporary world, utilization of digital content has risen exponentially. For example, newspaper and web articles, status updates, advertisements etc. have become an integral part of our daily routine. Thus, there is a need to build an automated system to summarize such large documents of text in order to save time and effort. Although, there are summarizers for languages such as English since the work has started in the 1950s and at present has led it up to a matured stage but there are several languages that still need special attention such as Punjabi language. The Punjabi language is highly rich in morphological structure as compared to English and other foreign languages. In this work, we provide three phase extractive summarization methodology using neural networks. It induces compendious summary of Punjabi single text document. The methodology incorporates pre-processing phase that cleans the text; processing phase that extracts statistical and linguistic features; and classification phase. The classification based neural network applies an activation function- sigmoid and weighted error reduction-gradient descent optimization to generate the resultant output summary. The proposed summarization system is applied over monolingual Punjabi text corpus from Indian languages corpora initiative phase-II. The precision, recall and F-measure are achieved as 90.0%, 89.28% an 89.65% respectively which is reasonably good in comparison to the performance of other existing Indian languages’ summarizers.

scholarly journals Innovative Topologies and Algorithms for Neural Networks

2020 ◽  
Vol 12 (7) ◽  
pp. 117
Author(s):  
Salvatore Graziani ◽  
Maria Gabriella Xibilia
Keyword(s):  
Neural Networks ◽  
Network Architecture ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Text Processing ◽  
Successful Outcome ◽  
Training Algorithm ◽  
Special Issue ◽  
And Training ◽  
Selection Of

The introduction of new topologies and training procedures to deep neural networks has solicited a renewed interest in the field of neural computation. The use of deep structures has significantly improved the state of the art in many applications, such as computer vision, speech and text processing, medical applications, and IoT (Internet of Things). The probability of a successful outcome from a neural network is linked to selection of an appropriate network architecture and training algorithm. Accordingly, much of the recent research on neural networks is devoted to the study and proposal of novel architectures, including solutions tailored to specific problems. The papers of this Special Issue make significant contributions to the above-mentioned fields by merging theoretical aspects and relevant applications. Twelve papers are collected in the issue, addressing many relevant aspects of the topic.

scholarly journals Design and Investigation of Capsule Networks for Sentence Classification

2019 ◽  
Vol 9 (11) ◽  
pp. 2200 ◽  
Author(s):  
Haftu Wedajo Fentaw ◽  
Tae-Hyong Kim
Keyword(s):  
Neural Networks ◽  
Image Analysis ◽  
Sentiment Analysis ◽  
Recurrent Neural Networks ◽  
Text Processing ◽  
Classification Performance ◽  
Sequential Order ◽  
Great Performance ◽  
Sentence Classification ◽  
Performance Gains

In recent years, convolutional neural networks (CNNs) have been used as an alternative to recurrent neural networks (RNNs) in text processing with promising results. In this paper, we investigated the newly introduced capsule networks (CapsNets), which are getting a lot of attention due to their great performance gains on image analysis more than CNNs, for sentence classification or sentiment analysis in some cases. The results of our experiment show that the proposed well-tuned CapsNet model can be a good, sometimes better and cheaper, substitute of models based on CNNs and RNNs used in sentence classification. In order to investigate whether CapsNets can learn the sequential order of words or not, we performed a number of experiments by reshuffling the test data. Our CapsNet model shows an overall better classification performance and better resistance to adversarial attacks than CNN and RNN models.

scholarly journals DisKnow: A Social-Driven Disaster Support Knowledge Extraction System

2020 ◽  
Vol 10 (17) ◽  
pp. 6083
Author(s):  
João Boné ◽  
Mariana Dias ◽  
João C. Ferreira ◽  
Ricardo Ribeiro
Keyword(s):  
Neural Networks ◽  
Crisis Management ◽  
Language Processing ◽  
Visual Information ◽  
State Of The Art ◽  
Text Processing ◽  
Data Extraction ◽  
Extraction System ◽  
Management Process ◽  
Knowledge Graphs

This research is aimed at creating and presenting DisKnow, a data extraction system with the capability of filtering and abstracting tweets, to improve community resilience and decision-making in disaster scenarios. Nowadays most people act as human sensors, exposing detailed information regarding occurring disasters, in social media. Through a pipeline of natural language processing (NLP) tools for text processing, convolutional neural networks (CNNs) for classifying and extracting disasters, and knowledge graphs (KG) for presenting connected insights, it is possible to generate real-time visual information about such disasters and affected stakeholders, to better the crisis management process, by disseminating such information to both relevant authorities and population alike. DisKnow has proved to be on par with the state-of-the-art Disaster Extraction systems, and it contributes with a way to easily manage and present such happenings.

Text Processing by Binary Neural Networks

2004 ◽  
pp. 125-136
Author(s):  
T. Beran ◽  
T. Macek
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Correlation Matrix ◽  
Text Processing ◽  
Traditional Technique ◽  
The Neural Network ◽  
Binary Neural Network ◽  
Correlation Matrix Memory ◽  
Text Searching

This chapter describes a rather less traditional technique of text processing. The technique is based on the binary neural network Correlation Matrix Memory. We propose using the neural network for text searching tasks. Two methods of coding input words are described and tested. Further, we discuss the problems of using this approach for text processing.

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