scholarly journals Applications of Deep Learning in News Text Classification

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Menghan Zhang
Keyword(s):  
Deep Learning ◽  
Text Classification ◽  
Recall Rate ◽  
Technological Advancement ◽  
Text Data ◽  
Healthcare Data ◽  
Feature Information ◽  
And Performance ◽  
The Relationship

The advancement in technology is taking place with an accelerating pace across the globe. With the increasing expansion and technological advancement, a vast volume of text data are generated everyday, in the form of social media platform, websites, company data, healthcare data, and news. Indeed, it is a difficult task to extract intriguing patterns from the text data, such as opinions, summaries, and facts, having varying length. Because of the problems of the length of text data and the difficulty of feature value extraction in news, this paper proposes a news text classification method based on the combination of deep learning (DL) algorithms. In order to classify the text data, the earlier approaches use a single word vector to express text information and only the information of the relationship between words were considered, but the relationship between words and categories was ignored which indeed is an important factor for the classification of news text. This paper follows the idea of a customized algorithm which is the combination of DL algorithms such as CNN, LSTM, and MLP and proposes a customized DCLSTM-MLP model for the classification of news text data. The proposed model is expressed in parallel with word vector and word dispersion. The relationship among words is represented by the word vector as an input of the CNN module, and the relationship between words and categories is represented by a discrete vector as an input of the MLP module in order to realize comprehensive learning of spatial feature information, time-series feature information, and relationship between words and categories of news text. To check the stability and performance of the proposed method, multiple experiments were performed. The experimental results showed that the proposed method solves the problems of text length, difficulty of feature extraction in the news text, and classification of news text in an effective way and attained better accuracy, recall rate, and comprehensive value as compared to the other models.

Deep Learning for text in limted data settings

2020 ◽  
Author(s):  
Pathikkumar Patel ◽  
Bhargav Lad ◽  
Jinan Fiaidhi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Text Classification ◽  
State Of The Art ◽  
Time Series Forecasting ◽  
Text Data ◽  
Performance Levels

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

Speculations on performance models

1968 ◽  
Vol 4 (1) ◽  
pp. 47-68 ◽  
Author(s):  
Victoria Fromkin
Keyword(s):  
Speech Act ◽  
Linguistic Competence ◽  
Performance Models ◽  
Syntactic Structures ◽  
Mechanistic Approach ◽  
Speech Performance ◽  
And Performance ◽  
Recurrent Patterns ◽  
The Relationship

The publication of Syntactic Structures in 1957 stimulated a much-needed re-evaluation among linguists as to the goals of linguistic theory and the nature of language. Part of the discussion which has ensued has centred around the question of linguistic competence versus performance. Competence has been related to performance as ‘langue’ is to ‘parole’. ‘Competence’ thus refers to the ‘underlying system of rules that has been mastered by the speaker-hearer’ (Chomsky, 1965) and ‘performance’ to the way the speaker-hearer utilizes this ‘internalized grammar’ when he actually produces and understands utterances. Despite the continued controversy about this distinction, little can be added to the justifications for it put forth over many decades (cf. Chomsky, 1957, 1964, 1965; Katz, 1964, 1966; Postal, 1966; Sapir, 1933; Levin, 1965; de Saussure, 1916; etc.). Yet there remains much vagueness as to the limits of each and the relationship between the two. For many years the confusion was due to the influence of Bloomfield who centred his attention on the speech act; his aim was the classification of the OUTPUT of performance, i.e. the utterances, and led to no theory about the dynamic process of performance itself (Bloomfield, 1924, 1926, 1927, 1933). While giving lip service to a concern for ‘langue’, his own mechanistic approach negated any possibility for the rules of ‘langue’ to be anything more than lists of recurrent patterns found in ‘parole’. And since he was of the opinion that ‘the physiologic and acoustic description of acts of speech belongs to other sciences than ours’ (Bloomfield, 1926: 153) he did not direct himself to those aspects of ‘parole’ which could explain speech performance.

Relation between capacity and performance in paediatric upper limb prosthesis users

2017 ◽  
Vol 42 (1) ◽  
pp. 14-20 ◽  
Author(s):  
Helen Lindner ◽  
Ayako Hiyoshi ◽  
Liselotte Hermansson
Keyword(s):  
Real Life ◽  
Myoelectric Control ◽  
International Classification Of Functioning ◽  
Upper Limb Prosthesis ◽  
Unit Increase ◽  
And Performance ◽  
The Individual ◽  
Adjusted Model ◽  
The Relationship

Background: The International Classification of functioning, disability and health refers capacity to what an individual can do in a standardised environment and describes performance as what an individual really does and whether the individual encounters any difficulty in the real-life environment. Measures of capacity and performance can help to determine if there is any gap between them that may restrict participation. The aim of this study was to explore the relationship between capacity scores obtained in a standardised clinical setting and proportional ease of performance obtained from a real-life environment. Methods: The Assessment of Capacity for Myoelectric Control and the Prosthetic Upper Extremity Functional Index were used to assess capacity and performance in 62 prosthetic users (age 3–17). Spearman coefficient and generalised linear model were used to examine the association between these measures. Results: A strong correlation (Spearman = 0.75) was found between the capacity scores and the ease of performance. In both unadjusted and adjusted models, capacity was significantly associated with proportional ease of performance. The adjusted model showed that, by 1 unit increase in the Assessment of Capacity for Myoelectric Control score, the ratio of proportional ease of performance increases by 45%. Conclusion: This implies that Assessment of Capacity for Myoelectric Control can be a predictor for ease of performance in real-life environment. Clinical relevance The ACMC scores may serve as an indicator to predict the difficulties that the children may encounter in their home environment. This prediction can help the clinician to make decisions, such that if the child requires more control training or is ready to move on to learn more complex tasks.

scholarly journals A Study into Math Document Classification using Deep Learning

2020 ◽  
Author(s):  
Fatimah Alshamari ◽  
Abdou Youssef
Keyword(s):  
Deep Learning ◽  
Classification Performance ◽  
Document Classification ◽  
Design Parameters ◽  
Technological Advancement ◽  
Scientific Publications ◽  
Specific Domain ◽  
Decision Choices ◽  
And Mathematics

Document classification is a fundamental task for many applications, including document annotation, document understanding, and knowledge discovery. This is especially true in STEM fields where the growth rate of scientific publications is exponential, and where the need for document processing and understanding is essential to technological advancement. Classifying a new publication into a specific domain based on the content of the document is an expensive process in terms of cost and time. Therefore, there is a high demand for a reliable document classification system. In this paper, we focus on classification of mathematics documents, which consist of English text and mathematics formulas and symbols. The paper addresses two key questions. The first question is whether math-document classification performance is impacted by math expressions and symbols, either alone or in conjunction with the text contents of documents. Our investigations show that Text-Only embedding produces better classification results. The second question we address is the optimization of a deep learning (DL) model, the LSTM combined with one dimension CNN, for math document classification. We examine the model with several input representations, key design parameters and decision choices, and choices of the best input representation for math documents classification.

scholarly journals Vacant Parking Slot Detection in the Around View Image Based on Deep Learning

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2138 ◽  
Author(s):  
Wei Li ◽  
Libo Cao ◽  
Lingbo Yan ◽  
Chaohui Li ◽  
Xiexing Feng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Detection Method ◽  
Recall Rate ◽  
Visual Environment ◽  
Convolution Kernel ◽  
Number Of Layers ◽  
Detection Stage ◽  
Geometric Cues ◽  
Precision Rate

Due to the complex visual environment, such as lighting variations, shadows, and limitations of vision, the accuracy of vacant parking slot detection for the park assist system (PAS) with a standalone around view monitor (AVM) needs to be improved. To address this problem, we propose a vacant parking slot detection method based on deep learning, namely VPS-Net. VPS-Net converts the vacant parking slot detection into a two-step problem, including parking slot detection and occupancy classification. In the parking slot detection stage, we propose a parking slot detection method based on YOLOv3, which combines the classification of the parking slot with the localization of marking points so that various parking slots can be directly inferred using geometric cues. In the occupancy classification stage, we design a customized network whose size of convolution kernel and number of layers are adjusted according to the characteristics of the parking slot. Experiments show that VPS-Net can detect various vacant parking slots with a precision rate of 99.63% and a recall rate of 99.31% in the ps2.0 dataset, and has a satisfying generalizability in the PSV dataset. By introducing a multi-object detection network and a classification network, VPS-Net can detect various vacant parking slots robustly.

scholarly journals A systematic review of text classification research based on deep learning models in Arabic language

2020 ◽  
Vol 10 (6) ◽  
pp. 6629
Author(s):  
Ahlam Wahdan ◽  
Sendeyah AL Hantoobi ◽  
Said A. Salloum ◽  
Khaled Shaalan
Keyword(s):  
Neural Network ◽  
Systematic Review ◽  
Neural Networks ◽  
Deep Learning ◽  
Text Classification ◽  
Arabic Language ◽  
Machine Learning Techniques ◽  
Learning Models ◽  
Learning Techniques

Classifying or categorizing texts is the process by which documents are classified into groups by subject, title, author, etc. This paper undertakes a systematic review of the latest research in the field of the classification of Arabic texts. Several machine learning techniques can be used for text classification, but we have focused only on the recent trend of neural network algorithms. In this paper, the concept of classifying texts and classification processes are reviewed. Deep learning techniques in classification and its type are discussed in this paper as well. Neural networks of various types, namely, RNN, CNN, FFNN, and LSTM, are identified as the subject of study. Through systematic study, 12 research papers related to the field of the classification of Arabic texts using neural networks are obtained: for each paper the methodology for each type of neural network and the accuracy ration for each type is determined. The evaluation criteria used in the algorithms of different neural network types and how they play a large role in the highly accurate classification of Arabic texts are discussed. Our results provide some findings regarding how deep learning models can be used to improve text classification research in Arabic language.

scholarly journals Technical Domain Classification of Bangla Text using BERT

2021 ◽  
Author(s):  
Koyel Ghosh ◽  
Apurbalal Senapati
Keyword(s):  
Deep Learning ◽  
Computer Science ◽  
Communication Technology ◽  
Text Classification ◽  
Language Model ◽  
Learning Model ◽  
Coarse Grained ◽  
Deep Learning Model

Coarse-grained tasks are primarily based on Text classification, one of the earliest problems in NLP, and these tasks are done on document and sentence levels. Here, our goal is to identify the technical domain of a given Bangla text. In Coarse-grained technical domain classification, such a piece of the Bangla text provides information about specific Coarse-grained technical domains like Biochemistry (bioche), Communication Technology (com-tech), Computer Science (cse), Management (mgmt), Physics (phy) Etc. This paper uses a recent deep learning model called the Bangla Bidirectional Encoder Representations Transformers (Bangla BERT) mechanism to identify the domain of a given text. Bangla BERT (Bangla-Bert-Base) is a pretrained language model of the Bangla language. Later, we discuss the Bangla BERT accuracy and compare it with other models that solve the same problem.

scholarly journals Musical Shaping Gestures: Considerations about Terminology and Methodology

2013 ◽  
Vol 8 (1) ◽  
pp. 68 ◽  
Author(s):  
Elaine King
Keyword(s):  
Observational Research ◽  
Hearing Impairments ◽  
Gesture Studies ◽  
Research In Education ◽  
Physical Gestures ◽  
And Performance ◽  
The Relationship ◽  
Opening Up ◽  
Consistent Application

Fulford and Ginsborg’s investigation into non-verbal communication during music rehearsal-talk between performers with and without hearing impairments extends existing research in the field of gesture studies by contributing significantly to our understanding of musicians’ physical gestures as well as opening up discussion about the relationship between speech, sign and gesture in discourse about music. Importantly, the authors weigh up the possibility of an emerging sign language about music. This commentary focuses on three key considerations in response to their paper: first, use of terminology in the study of gesture, specifically about ‘musical shaping gestures’ (MSGs); second, methodological issues about capturing physical gestures; and third, evaluation of the application of gesture research beyond the rehearsal context. While the difficulties of categorising gestures in observational research are acknowledged, I indicate that the consistent application of terminology from outside and within the study is paramount. I also suggest that the classification of MSGs might be based upon a set of observed physical characteristics within a single gesture, including size, duration, speed, plane and handedness, leading towards an alternative taxonomy for interpreting these data. Finally, evaluation of the application of gesture research in education and performance arenas is provided.

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