scholarly journals Outomatiese genreklassifikasie vir Afrikaans

2014 ◽  
Vol 33 (1) ◽  
Author(s):  
Dirk Snyman ◽  
Gerhard Van Huyssteen ◽  
Walter Daelemans
Keyword(s):  
Classification System ◽  
Text Processing ◽  
Data Representation ◽  
Performance Measure ◽  
Classification Systems ◽  
Training Data ◽  
Learning Approaches ◽  
Genre Classification ◽  
Automatic Text Classification ◽  
Automatic Text

When working in the terrain of text processing, metadata about a particular text plays an important role. Metadata is often generated, using automatic text classification systems which classify a text into one or more predefined classes or categories based on its contents. One of the dimensions by which a text can be can be classified, is its genre. In this study the development of an automatic genre classification system in a resource scarce environment is postulated. This study aimed to investigate the techniques and approaches that are generally used for automatic genre classification systems, and identify the best approach for Afrikaans (a resource scarce language). With the development of an automatic genre classification system, there is a set of variables that must be considered as they influence the performance of machine learning approaches (i.e. the algorithm used, the amount of training data, and data representation as features). If these variables are handled correctly, an optimal combination of them can be identified to successfully develop a genre classification system. In this article a genre classification system is being developed by using the following approach: The implementation of a MNB algorithm with a bag of words approach feature set. This system provides a resultant f-score (performance measure) of 0.929.

The VINITI RAS Automatic Text Classification System for Processing the Flow of Scientific Publications

2020 ◽  
Vol 54 (3) ◽  
pp. 113-123
Author(s):  
V. S. Egorov ◽  
E. S. Kozlova ◽  
K. E. Lomotin ◽  
O. V. Fedorets ◽  
A. V. Filimonov ◽  
...  
Keyword(s):  
Text Classification ◽  
Classification System ◽  
Scientific Publications ◽  
Automatic Text Classification ◽  
Viniti Ras ◽  
Automatic Text

scholarly journals Automated News Classification using N-gram Model and Key Features of Nepali Language

SCITECH Nepal ◽  
2018 ◽  
Vol 13 (1) ◽  
pp. 64-69
Author(s):  
Dinesh Dangol ◽  
Rupesh Dahi Shrestha ◽  
Arun Timalsina
Keyword(s):  
Text Classification ◽  
English Language ◽  
Promising Result ◽  
Text Processing ◽  
Automatic Text Classification ◽  
Key Features ◽  
Research Experiment ◽  
N Gram ◽  
Automated Text Processing ◽  
Automatic Text

With an increasing trend of publishing news online on website, automatic text processing becomes more and more important. Automatic text classification has been a focus of many researchers in different languages for decades. There is a huge amount of research repository on features of English language and their uses on automated text processing. This research implements Nepali language key features for automatic text classification of Nepali news. In particular, the study on impact of Nepali language based features, which are extremely different than English language is more challenging because of the higher level of complexity to be resolved. The research experiment using vector space model, n-gram model and key feature based processing specific to Nepali language shows promising result compared to bag-of-words model for the task of automated Nepali news classification.

scholarly journals Design and Implementation of Grievance Filing Application using Automatic Text Classification

2020 ◽  
Vol 9 (7) ◽  
pp. 1332-1336
Keyword(s):  
Deep Learning ◽  
Text Classification ◽  
Learning Approaches ◽  
Common People ◽  
Design And Implementation ◽  
Filing System ◽  
Automatic Text Classification ◽  
Successful Resolution ◽  
Language Input ◽  
Automatic Text

This research aims to design a Grievance Filing System built using automatic text classification without any manual interruption. Various methodologies are followed to achieve it and are implemented. Performance of the different algorithms is discussed. People are less aware about the lengthy methods for lodging complaints. We propose a simplified process of enrolling grievances to ministries. The system accepts grievances in recorded voice form. The system is designed for Marathi language. Input in the form of speech will ease people’s comfort for lodging grievances. We present a model where voice is first preprocessed, followed by text classification using deep learning approaches such as CNN and LSTM, the grievances will be sent to respective ministry. This system can be used by government ministries to get grievances from common people through a simplified process. User will be notified on the progress of their lodged complaint and on its successful resolution by respective ministry.

scholarly journals Support Vector Machines and Kernel Functions for Text Processing

2013 ◽  
Vol 20 (3) ◽  
pp. 130 ◽  
Author(s):  
Celso Antonio Alves Kaestner
Keyword(s):  
Text Classification ◽  
Learning Algorithm ◽  
Text Processing ◽  
Dimensional Space ◽  
Kernel Functions ◽  
Support Vector ◽  
Svm Classifier ◽  
Vector Machines ◽  
Automatic Text Classification ◽  
Automatic Text

This work presents kernel functions that can be used in conjunction with the Support Vector Machine – SVM – learning algorithm to solve the automatic text classification task. Initially the Vector Space Model for text processing is presented. According to this model text is seen as a set of vectors in a high dimensional space; then extensions and alternative models are derived, and some preprocessing procedures are discussed. The SVM learning algorithm, largely employed for text classification, is outlined: its decision procedure is obtained as a solution of an optimization problem. The “kernel trick”, that allows the algorithm to be applied in non-linearly separable cases, is presented, as well as some kernel functions that are currently used in text applications. Finally some text classification experiments employing the SVM classifier are conducted, in order to illustrate some text preprocessing techniques and the presented kernel functions.

scholarly journals Genre annotation for the Web

2021 ◽  
Author(s):  
Serge Sharoff
Keyword(s):  
Classification Model ◽  
Linguistic Features ◽  
General Reference ◽  
Digital Curation ◽  
Communicative Functions ◽  
Genre Classification ◽  
Automatic Text Classification ◽  
Deep Learning Model ◽  
Automatic Text ◽  
The Web

Abstract This paper describes a digital curation study aimed at comparing the composition of large Web corpora, such as enTenTen, ukWac or ruWac, by means of automatic text classification. First, the paper presents a Deep Learning model suitable for classifying texts from large Web corpora using a small number of communicative functions, such as Argumentation or Reporting. Second, it describes the results of applying the automatic classification model to these corpora and compares their composition. Finally, the paper introduces a framework for interpreting the results of automatic genre classification using linguistic features. The framework can help in comparing general reference corpora obtained from the Web and in comparing corpora across languages.

scholarly journals Multilayer Soil Moisture Mapping at a Regional Scale from Multisource Data via a Machine Learning Method

2019 ◽  
Vol 11 (3) ◽  
pp. 284 ◽  
Author(s):  
Linglin Zeng ◽  
Shun Hu ◽  
Daxiang Xiang ◽  
Xiang Zhang ◽  
Deren Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Regional Scale ◽  
Remotely Sensed ◽  
Temporal Variations ◽  
Training Data ◽  
Estimation Accuracy ◽  
Learning Approaches ◽  
Remotely Sensed Data ◽  
Deep Soil

Soil moisture mapping at a regional scale is commonplace since these data are required in many applications, such as hydrological and agricultural analyses. The use of remotely sensed data for the estimation of deep soil moisture at a regional scale has received far less emphasis. The objective of this study was to map the 500-m, 8-day average and daily soil moisture at different soil depths in Oklahoma from remotely sensed and ground-measured data using the random forest (RF) method, which is one of the machine-learning approaches. In order to investigate the estimation accuracy of the RF method at both a spatial and a temporal scale, two independent soil moisture estimation experiments were conducted using data from 2010 to 2014: a year-to-year experiment (with a root mean square error (RMSE) ranging from 0.038 to 0.050 m3/m3) and a station-to-station experiment (with an RMSE ranging from 0.044 to 0.057 m3/m3). Then, the data requirements, importance factors, and spatial and temporal variations in estimation accuracy were discussed based on the results using the training data selected by iterated random sampling. The highly accurate estimations of both the surface and the deep soil moisture for the study area reveal the potential of RF methods when mapping soil moisture at a regional scale, especially when considering the high heterogeneity of land-cover types and topography in the study area.

scholarly journals An Improved Machine Learning-Based Employees Attrition Prediction Framework with Emphasis on Feature Selection

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1226
Author(s):  
Saeed Najafi-Zangeneh ◽  
Naser Shams-Gharneh ◽  
Ali Arjomandi-Nezhad ◽  
Sarfaraz Hashemkhani Zolfani
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Standard Deviation ◽  
Analytical Formula ◽  
Feature Selection Method ◽  
Selection Method ◽  
Performance Measure ◽  
Learning Approaches ◽  
Training Costs ◽  
Professional Employees

Companies always seek ways to make their professional employees stay with them to reduce extra recruiting and training costs. Predicting whether a particular employee may leave or not will help the company to make preventive decisions. Unlike physical systems, human resource problems cannot be described by a scientific-analytical formula. Therefore, machine learning approaches are the best tools for this aim. This paper presents a three-stage (pre-processing, processing, post-processing) framework for attrition prediction. An IBM HR dataset is chosen as the case study. Since there are several features in the dataset, the “max-out” feature selection method is proposed for dimension reduction in the pre-processing stage. This method is implemented for the IBM HR dataset. The coefficient of each feature in the logistic regression model shows the importance of the feature in attrition prediction. The results show improvement in the F1-score performance measure due to the “max-out” feature selection method. Finally, the validity of parameters is checked by training the model for multiple bootstrap datasets. Then, the average and standard deviation of parameters are analyzed to check the confidence value of the model’s parameters and their stability. The small standard deviation of parameters indicates that the model is stable and is more likely to generalize well.

scholarly journals Information-Theoretic Generalization Bounds for Meta-Learning and Applications

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 126
Author(s):  
Sharu Theresa Jose ◽  
Osvaldo Simeone
Keyword(s):  
Learning Algorithm ◽  
Broad Class ◽  
Performance Measure ◽  
Training Data ◽  
Learning To Learn ◽  
Data Set ◽  
Information Theoretic ◽  
Meta Learning ◽  
Task Training ◽  
Test Sets

Meta-learning, or “learning to learn”, refers to techniques that infer an inductive bias from data corresponding to multiple related tasks with the goal of improving the sample efficiency for new, previously unobserved, tasks. A key performance measure for meta-learning is the meta-generalization gap, that is, the difference between the average loss measured on the meta-training data and on a new, randomly selected task. This paper presents novel information-theoretic upper bounds on the meta-generalization gap. Two broad classes of meta-learning algorithms are considered that use either separate within-task training and test sets, like model agnostic meta-learning (MAML), or joint within-task training and test sets, like reptile. Extending the existing work for conventional learning, an upper bound on the meta-generalization gap is derived for the former class that depends on the mutual information (MI) between the output of the meta-learning algorithm and its input meta-training data. For the latter, the derived bound includes an additional MI between the output of the per-task learning procedure and corresponding data set to capture within-task uncertainty. Tighter bounds are then developed for the two classes via novel individual task MI (ITMI) bounds. Applications of the derived bounds are finally discussed, including a broad class of noisy iterative algorithms for meta-learning.

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