scholarly journals PAAD: POLITICAL ARABIC ARTICLES DATASET FOR AUTOMATIC TEXT CATEGORIZATION

2020 ◽  
Vol 46 (1) ◽  
pp. 1-10
Author(s):  
Dhafar Hamed Abd ◽  
Ahmed T. Sadiq ◽  
Ayad R. Abbas
Keyword(s):  
Computational Linguistics ◽  
Language Processing ◽  
Text Classification ◽  
Text Categorization ◽  
Political Orientation ◽  
Huge Amount ◽  
Textual Data ◽  
Automatic Text ◽  
Excel File ◽  
Modern Standard

Now day’s text Classification and Sentiment analysis is considered as one of the popular Natural Language Processing (NLP) tasks. This kind of technique plays significant role in human activities and has impact on the daily behaviours. Each article in different fields such as politics and business represent different opinions according to the writer tendency. A huge amount of data will be acquired through that differentiation. The capability to manage the political orientation of an online article automatically. Therefore, there is no corpus for political categorization was directed towards this task in Arabic, due to the lack of rich representative resources for training an Arabic text classifier. However, we introduce political Arabic articles dataset (PAAD) of textual data collected from newspapers, social network, general forum and ideology website. The dataset is 206 articles distributed into three categories as (Reform, Conservative and Revolutionary) that we offer to the research community on Arabic computational linguistics. We anticipate that this dataset would make a great aid for a variety of NLP tasks on Modern Standard Arabic, political text classification purposes. We present the data in raw form and excel file. Excel file will be in four types such as V1 raw data, V2 preprocessing, V3 root stemming and V4 light stemming.

scholarly journals Machine Learning for Dissimulating Reality

Proceedings ◽  
2021 ◽  
Vol 77 (1) ◽  
pp. 17
Author(s):  
Andrea Giussani
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Huge Amount ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Technological Advances ◽  
Textual Data ◽  
Musical Scores ◽  
Mathematical Formulas

In the last decade, advances in statistical modeling and computer science have boosted the production of machine-produced contents in different fields: from language to image generation, the quality of the generated outputs is remarkably high, sometimes better than those produced by a human being. Modern technological advances such as OpenAI’s GPT-2 (and recently GPT-3) permit automated systems to dramatically alter reality with synthetic outputs so that humans are not able to distinguish the real copy from its counteracts. An example is given by an article entirely written by GPT-2, but many other examples exist. In the field of computer vision, Nvidia’s Generative Adversarial Network, commonly known as StyleGAN (Karras et al. 2018), has become the de facto reference point for the production of a huge amount of fake human face portraits; additionally, recent algorithms were developed to create both musical scores and mathematical formulas. This presentation aims to stimulate participants on the state-of-the-art results in this field: we will cover both GANs and language modeling with recent applications. The novelty here is that we apply a transformer-based machine learning technique, namely RoBerta (Liu et al. 2019), to the detection of human-produced versus machine-produced text concerning fake news detection. RoBerta is a recent algorithm that is based on the well-known Bidirectional Encoder Representations from Transformers algorithm, known as BERT (Devlin et al. 2018); this is a bi-directional transformer used for natural language processing developed by Google and pre-trained over a huge amount of unlabeled textual data to learn embeddings. We will then use these representations as an input of our classifier to detect real vs. machine-produced text. The application is demonstrated in the presentation.

scholarly journals recommendation to the SSH community: Take a linguist on board

2021 ◽  
Vol 45 (1) ◽  
Author(s):  
Jeannine Beeken
Keyword(s):  
Computational Linguistics ◽  
Language Processing ◽  
Process Data ◽  
Data Services ◽  
Named Entity ◽  
Language Technology ◽  
Textual Data ◽  
Automated Speech Recognition ◽  
Linguistic Approaches ◽  
Free Open Source

In this paper we address how Natural Language Processing (NLP) approaches and language technology can contribute to data services in different ways; from providing social science users with new approaches and tools to explore oral and textual data, to enhancing the search, findability and retrieval of data sources. By using linguistic approaches we are able to process data, for example using Automated Speech Recognition (ASR) and named entity recognizers (NER), extract key concepts and terms, and improve search strategies.  We provide examples of how computational linguistics contribute to and facilitate the mining and analysis of oral or textual material, for example (transcribed) interviews or oral histories, and show how free open source (OS) tools can be used very easily to gain a quick overview of the key features of text, which can be further exploited as useful metadata.

scholarly journals Automatic Text Categorization in Terms of Genre and Author

2000 ◽  
Vol 26 (4) ◽  
pp. 471-495 ◽  
Author(s):  
Efstathios Stamatatos ◽  
Nikos Fakotakis ◽  
George Kokkinakis
Keyword(s):  
Language Processing ◽  
Text Categorization ◽  
Text Processing ◽  
Small Scale ◽  
Set Size ◽  
Vocabulary Richness ◽  
Author Identification ◽  
Text Genre ◽  
Automatic Text ◽  
Better Than

The two main factors that characterize a text are its content and its style, and both can be used as a means of categorization. In this paper we present an approach to text categorization in terms of genre and author for Modern Greek. In contrast to previous stylometric approaches, we attempt to take full advantage of existing natural language processing (NLP) tools. To this end, we propose a set of style markers including analysis-level measures that represent the way in which the input text has been analyzed and capture useful stylistic information without additional cost. We present a set of small-scale but reasonable experiments in text genre detection, author identification, and author verification tasks and show that the proposed method performs better than the most popular distributional lexical measures, i.e., functions of vocabulary richness and frequencies of occurrence of the most frequent words. All the presented experiments are based on unrestricted text downloaded from the World Wide Web without any manual text preprocessing or text sampling. Various performance issues regarding the training set size and the significance of the proposed style markers are discussed. Our system can be used in any application that requires fast and easily adaptable text categorization in terms of stylistically homogeneous categories. Moreover, the procedure of defining analysis-level markers can be followed in order to extract useful stylistic information using existing text processing tools.

scholarly journals Self-Information Loss Compensation Learning for Machine-Generated Text Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Weikuan Wang ◽  
Ao Feng
Keyword(s):  
Language Processing ◽  
Text Classification ◽  
Information Loss ◽  
Text Detection ◽  
Written Text ◽  
Modeling Process ◽  
Loss Compensation ◽  
Logical Information ◽  
Automatic Text ◽  
General Method

The technology of automatic text generation by machine has always been an important task in natural language processing, but the low-quality text generated by the machine seriously affects the user experience due to poor readability and fuzzy effective information. The machine-generated text detection method based on traditional machine learning relies on a large number of artificial features with detection rules. The general method of text classification based on deep learning tends to the orientation of text topics, but logical information between texts sequences is not well utilized. For this problem, we propose an end-to-end model which uses the text sequences self-information to compensate for the information loss in the modeling process, to learn the logical information between the text sequences for machine-generated text detection. This is a text classification task. We experiment on a Chinese question and answer the dataset collected from a biomedical social media, which includes human-written text and machine-generated text. The result shows that our method is effective and exceeds most baseline models.

Improving Arabic Text Classification Using P-Stemmer

2020 ◽  
Vol 13 ◽  
Author(s):  
Tarek Kanan ◽  
Bilal Hawashin ◽  
Shadi Alzubi ◽  
Eyad Almaita ◽  
Ahmad Alkhatib ◽  
...  
Keyword(s):  
Language Processing ◽  
Text Classification ◽  
Text Categorization ◽  
English Language ◽  
Arabic Language ◽  
Online News ◽  
Support Vector ◽  
Arabic Text ◽  
Fast Learning ◽  
Arabic Text Classification

Introduction: Stemming is an important preprocessing step in text classification, and could contribute in increasing text classification accuracy. Although many works proposed stemmers for English language, few stemmers were proposed for Arabic text. Arabic language has gained increasing attention in the previous decades and the need is vital to further improve Arabic text classification. Method: This work combined the use of the recently proposed P-Stemmer with various classifiers to find the optimal classifier for the P-stemmer in term of Arabic text classification. As part of this work, a synthesized dataset was collected. Result: The previous experiments show that the use of P-Stemmer has a positive effect on classification. The degree of improvement was classifier-dependent, which is reasonable as classifiers vary in their methodologies. Moreover, the experiments show that the best classifier with the P-Stemmer was NB. This is an interesting result as this classifier is wellknown for its fast learning and classification time. Discussion: First, the continuous improvement of the P-Stemmer by more optimization steps is necessary to further improve the Arabic text categorization. This can be made by combining more classifiers with the stemmer, by optimizing the other natural language processing steps, and by improving the set of stemming rules. Second, the lack of sufficient Arabic datasets, especially large ones, is still an issue. Conclusion: In this work, an improved P-Stemmer was proposed by combining its use with various classifiers. In order to evaluate its performance, and due to the lack of Arabic datasets, a novel Arabic dataset was synthesized from various online news pages. Next, the P-Stemmer was combined with Naïve Bayes, Random Forest, Support Vector Machines, KNearest Neighbor, and K-Star.

scholarly journals A Recapitulation of Different Text Classification Algorithms

2020 ◽  
Vol 8 (6) ◽  
pp. 3411-3414
Keyword(s):  
Machine Learning ◽  
Text Mining ◽  
Natural Language ◽  
Data Processing ◽  
Text Classification ◽  
Text Categorization ◽  
Cluster Formation ◽  
Classification Algorithms ◽  
Electronic Documents ◽  
Textual Data

Text classification process has gained a lot of importance in recent years and is still one of the most popular topics of discussion because of the presence of a huge outsized range of electronic documents from diverse resources. The text categorization process assigns predefined classes to documents. It finds noteworthy similarities in large textual data were interesting, hidden, previously unknown and extremely useful patterns and information can be discovered. Text classification helps in analysis of large textual data. Text mining intends facilitating customers extract informations from resources, and deals with operations such as retrieving, classifying ,cluster formation, mining of data, processing of natural language and techniques of machine learning together to classificate unalike patterns. Inside the process of content arrangement, terms gauging strategies configuration fitting loads to the offered terms to improvise content grouping execution. This paper overviews content order, the procedure of content grouping, distinctive term gauging techniques and correlations between various characterization calculations.

Text Classification of Gujarati Newspaper Headlines

2021 ◽  
pp. 2050020
Author(s):  
Stuti Mehta ◽  
Suman K. Mitra
Keyword(s):  
Feature Extraction ◽  
Language Processing ◽  
Text Classification ◽  
Low Resource ◽  
Textual Data ◽  
Gujarati Language ◽  
News Headlines ◽  
Embedding Methods ◽  
Insight Into

Text classification is an extremely important area of Natural Language Processing (NLP). This paper studies various methods for embedding and classification in the Gujarati language. The dataset comprises of Gujarati News Headlines classified into various categories. Different embedding methods for Gujarati language and various classifiers are used to classify the headlines into given categories. Gujarati is a low resource language. This language is not commonly worked upon. This paper deals with one of the most important NLP tasks - classification and along with it, an idea about various embedding techniques for Gujarati language can be obtained since they help in feature extraction for the process of classification. This paper first performs embedding to get a valid representation of the textual data and then uses already existing robust classifiers to perform classification over the embedded data. Additionally, the paper provides an insight into how various NLP tasks can be performed over a low resource language like Gujarati. Finally, the research paper carries out a comparative analysis between the performances of various existing methods of embedding and classification to get an idea of which combination gives a better outcome.

Text Preprocessing

2018 ◽  
pp. 86-104
Author(s):  
Piotr Malak
Keyword(s):  
Computational Linguistics ◽  
Language Processing ◽  
Text Processing ◽  
Data Gathering ◽  
Original Data ◽  
Practical Experience ◽  
Digital Data ◽  
Quality Of Data ◽  
Textual Data ◽  
Text Preprocessing

Digital humanities and information visualization rely on huge sets of digital data. Those data are mostly delivered in the text form. Although computational linguistics provides a lot of valuable tools for text processing, the initial phase (text preprocessing) is very involved and time-consuming. The problems arise due to a human factor – they are not always errors; there is also inconsistency in forms, affecting data quality. In this chapter, the author describes and discusses the main issues that arise during the preprocessing phase of textual data gathering for InfoVis. Chosen examples of InfoVis applications are presented. Except for problems with raw, original data, solutions are also referred. Canonical approaches used in text preprocessing and common issues affecting the process and ways to prevent them are also presented. The quality of data from different sources is also discussed. The content of this chapter is a result of a few years of practical experience in natural language processing gained during realization of different projects and evaluation campaigns.

scholarly journals Text Categorization Techniques and Current Trends

2020 ◽  
Vol 9 (5) ◽  
pp. 335-345
Keyword(s):  
Language Processing ◽  
Text Categorization ◽  
Classification Systems ◽  
Online Data ◽  
Text Data ◽  
Current Trends ◽  
Practical Evaluation ◽  
Textual Data ◽  
Meaningful Text ◽  
The Given

With the development of online data, text categorization has become one of the key procedures for taking care of and sorting out content information. Text categorization strategies are utilized to order reports, to discover fascinating data on the world wide web. Text Categorization is a task for categorizing information based on text and it has been important for effective analysis of textual data frameworks. There are systems which are designed to analyse and make distinctions between meaningful classes of information and text, such system is known as text classification systems. The above-mentioned system is widely accepted and has been used for the purpose of retrieval of information and natural language processing. The archives can be ordered in three different ways unsupervised, supervised and semi supervised techniques. Text categorization alludes to the procedure of dole out a classification or a few classes among predefined ones to each archive, naturally. For the given text data, these words that can be expressed in the correct meaning of a word in different documents are usually considered as good features. In the paper, we have used certain measures to ensure meaningful text categorization. One such method is through feature selection which is the solution proposed in this paper which does not change the physicality of the original features. We have taken into account all meaningful features to distinguish between different text categorization approaches and highlighted the evaluation metrics, advantages and limitations of each approach. We conclusively studied the working of several approaches and drew conclusion of best suited algorithm by performing practical evaluation. We are going to review different papers on the basis of different text categorization sections and a comparative and conclusive analysis is presented in this paper. This paper will present classification on various kinds of ways to deal and compare with text categorization.

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