Novel Machine Learning–Based Approach for Arabic Text Classification Using Stylistic and Semantic Features

Text classification is an essential task for many applications related to the Natural Language Processing domain.It can be applied in many fields,such as Information Retrieval,Knowledge Extraction,and Knowledge modeling.Even though the importance of this task,Arabic Text Classification tools still suffer from many problems and remain incapable of responding to the increasing volume of Arabic content that circulates on the web or resides in large databases.This paper introduces a novel machine learning-based approach that exclusively uses hybrid(stylistic and semantic)features.First,we clean the Arabic documents and translate them to English using translation tools.Consequently,the semantic features are automatically extracted from the translated documents using an existing database of English topics.Besides,the model automatically extracts from the textual content a set of stylistic features such as word and character frequencies and punctuation.Therefore,we obtain 3 types of features:semantic,stylistic and hybrid.Using each time,a different type of feature,we performed an in-depth comparison study of nine well-known Machine Learning models to evaluate our approach and used a standard Arabic corpus.The obtained results show that Neural Network outperforms other models and provides good performances using hybrid features(F1-score=0.88%).

Image Retrieval with Text Manipulation by Local Feature Modification

The demand for image retrieval with text manipulation exists in many fields, such as e-commerce and Internet search. Deep metric learning methods are used by most researchers to calculate the similarity between the query and the candidate image by fusing the global feature of the query image and the text feature. However, the text usually corresponds to the local feature of the query image rather than the global feature. Therefore, in this paper, we propose a framework of image retrieval with text manipulation by local feature modification(LFM-IR) which can focus on the related image regions and attributes and perform modification. A spatial attention module and a channel attention module are designed to realize the semantic mapping between image and text. We achieve excellent performance on three benchmark datasets, namely Color-Shape-Size(CSS), Massachusetts Institute of Technology(MIT) States and Fashion200K(+8.3%, +0.7% and +4.6% in R@1).

A New Approach of Feature Selection for Text Categorization

This paper proposes a new approach of feature selection based on the independent measure between features for text categorization. A fundamental hypothesis that occurrence of the terms in documents is independent of each other, widely used in the probabilistic models for text categorization （TC）, is discussed. However, the basic hypothesis is incom plete for independence of feature set. From the view of feature selection, a new independent measure between features is designed, by which a feature selection algorithm is given to ob rain a feature subset. The selected subset is high in relevance with category and strong in independence between features, satisfies the basic hypothesis at maximum degree. Compared with other traditional feature selection method in TC （which is only taken into the relevance account）, the performance of feature subset selected by our method is prior to others with experiments on the benchmark dataset of 20 Newsgroups.

Agricultural Ontology Based Feature Optimization for Agricultural Text Clustering

Feature optimization is important to agricultural text mining. Usually, the vector space model is used to represent text documents. However, this basic approach still suffers from two drawbacks： thecurse of dimension and the lack of semantic information. In this paper, a novel ontology-based feature optimization method for agricultural text was proposed. First, terms of vector space model were mapped into concepts of agricultural ontology, which concept frequency weights are computed statistically by term frequency weights; second, weights of concept similarity were assigned to the concept features according to the structure of the agricultural ontology. By combining feature frequency weights and feature similarity weights based on the agricultural ontology, the dimensionality of feature space can be reduced drastically. Moreover, the semantic information can be incorporated into this method. The results showed that this method yields a significant improvement on agricultural text clustering by the feature optimization.

A New Feature Selection Method for Text Clustering

Feature selection methods have been successfully applied to text categorization but seldom applied to text clustering due to the unavailability of class label information. In this paper, a new feature selection method for text clustering based on expectation maximization and cluster validity is proposed. It uses supervised feature selection method on the intermediate clustering result which is generated during iterative clustering to do feature selection for text clustering; meanwhile, the Davies-Bouldin＇s index is used to evaluate the intermediate feature subsets indirectly. Then feature subsets are selected according to the curve of the Davies-Bouldin＇s index. Experiment is carried out on several popular datasets and the results show the advantages of the proposed method.

基于DAN与FastText的藏文短文本分类研究

随着藏文信息不断融入社会生活,越来越多的藏文短文本数据存在网络平台上。针对传统分类方法在藏文短文本上分类性能低的问题,文中提出了一种基于DAN-FastText的藏文短文本分类模型。该模型使用FastText网络在较大规模的藏文语料上进行无监督训练获得预训练的藏文音节向量集,使用预训练的音节向量集将藏文短文本信息转化为音节向量,把音节向量送入DAN(Deep Averaging Networks)网络并在输出阶段融合经过FastText网络训练的句向量特征,最后通过全连接层和softmax层完成分类。在公开的TNCC(Tibetan News Classification Corpus)新闻标题数据集上所提模型的Macro-F1是64.53%,比目前最好评测结果TiBERT模型的Macro-F1得分高出2.81%,比GCN模型的Macro-F1得分高出6.14%,融合模型具有较好的藏文短文本分类效果。

Lazy learner text categorization algorithm based on embedded feature selection

To avoid the curse of dimensionality, text categorization （TC） algorithms based on machine learning （ML） have to use an feature selection （FS） method to reduce the dimensionality of feature space. Although having been widely used, FS process will generally cause information losing and then have much side-effect on the whole performance of TC algorithms. On the basis of the sparsity characteristic of text vectors, a new TC algorithm based on lazy feature selection （LFS） is presented. As a new type of embedded feature selection approach, the LFS method can greatly reduce the dimension of features without any information losing, which can improve both efficiency and performance of algorithms greatly. The experiments show the new algorithm can simultaneously achieve much higher both performance and efficiency than some of other classical TC algorithms.

Integrating Audio-Visual Features and Text Information for Story Segmentation of News Video

Video data are composed of multimodal information streams including visual, auditory and textual streams, so an approach of story segmentation for news video using multimodal analysis is described in this paper. The proposed approach detects the topic-caption frames, and integrates them with silence clips detection results, as well as shot segmentation results to locate the news story boundaries. The integration of audio-visual features and text information overcomes the weakness of the approach using only image analysis techniques. On test data with 135 400 frames, when the boundaries between news stories are detected, the accuracy rate 85.8% and the recall rate 97.5% are obtained. The experimental results show the approach is valid and robust.

Feature selection algorithm for text classification based on improved mutual information

In order to solve the poor performance in text classification when using traditional formula of mutual information (MI),a feature selection algorithm were proposed based on improved mutual information.The improved mutual information algorithm,which is on the basis of traditional improved mutual information methods that enhance the MI value of negative characteristics and feature's frequency,supports the concept of concentration degree and dispersion degree.In accordance with the concept of concentration degree and dispersion degree,formulas which embody concentration degree and dispersion degree were constructed and the improved mutual information was implemented based on these.In this paper,the feature selection algorithm was applied based on improved mutual information to a text classifier based on Biomimetic Pattern Recognition and it was compared with several other feature selection methods.The experimental results showed that the improved mutual information feature selection method greatly enhances the performance compared with traditional mutual information feature selection methods and the performance is better than that of information gain.Through the introduction of the concept of concentration degree and dispersion degree,the improved mutual information feature selection method greatly improves the performance of text classification system.

Automatic Persian Text Summarization Using Linguistic Features from Text Structure Analysis

With the remarkable growth of textual data sources in recent years,easy,fast,and accurate text processing has become a challenge with significant payoffs.Automatic text summarization is the process of compressing text documents into shorter summaries for easier review of its core contents,which must be done without losing important features and information.This paper introduces a new hybrid method for extractive text summarization with feature selection based on text structure.The major advantage of the proposed summarization method over previous systems is the modeling of text structure and relationship between entities in the input text,which improves the sentence feature selection process and leads to the generation of unambiguous,concise,consistent,and coherent summaries.The paper also presents the results of the evaluation of the proposed method based on precision and recall criteria.It is shown that the method produces summaries consisting of chains of sentences with the aforementioned characteristics from the original text.

基于SWPF2vec和DJ-TextRCNN的古籍文本主题分类研究

以编目分类和规则匹配为主的古籍文本主题分类方法存在工作效能低、专家知识依赖性强、分类依据单一化、古籍文本主题自动分类难等问题。对此,本文结合古籍文本内容和文字特征,尝试从古籍内容分类得到符合研究者需求的主题,推动数字人文研究范式的转型。首先,参照东汉古籍《说文解字》对文字的分析方式,以前期标注的古籍语料数据集为基础,构建全新的“字音(说)-原文(文)-结构(解)-字形(字)”四维特征数据集。其次,设计四维特征向量提取模型(speaking,word,pattern,and font to vector,SWPF2vec),并结合预训练模型实现对古籍文本细粒度的特征表示。再其次,构建融合卷积神经网络、循环神经网络和多头注意力机制的古籍文本主题分类模型(dianji-recurrent convolutional neural networks for text classification,DJ-TextRCNN)。最后,融入四维语义特征,实现对古籍文本多维度、深层次、细粒度的语义挖掘。在古籍文本主题分类任务上,DJ-TextRCNN模型在不同维度特征下的主题分类准确率均为最优,在“说文解字”四维特征下达到76.23%的准确率,初步实现了对古籍文本的精准主题分类。

A Comparative Study on Two Techniques of Reducing the Dimension of Text Feature Space

With the development of large scale text processing, the dimension of text feature space has become larger and larger, which has added a lot of difficulties to natural language processing. How to reduce the dimension has become a practical problem in the field. Here we present two clustering methods, i.e. concept association and concept abstract, to achieve the goal. The first refers to the keyword clustering based on the co occurrence of

Smart Approaches to Efficient Text Mining for Categorizing Sexual Reproductive Health Short Messages into Key Themes

To promote behavioral change among adolescents in Zambia, the National HIV/AIDS/STI/TB Council, in collaboration with UNICEF, developed the Zambia U-Report platform. This platform provides young people with improved access to information on various Sexual Reproductive Health topics through Short Messaging Service (SMS) messages. Over the years, the platform has accumulated millions of incoming and outgoing messages, which need to be categorized into key thematic areas for better tracking of sexual reproductive health knowledge gaps among young people. The current manual categorization process of these text messages is inefficient and time-consuming and this study aims to automate the process for improved analysis using text-mining techniques. Firstly, the study investigates the current text message categorization process and identifies a list of categories adopted by counselors over time which are then used to build and train a categorization model. Secondly, the study presents a proof of concept tool that automates the categorization of U-report messages into key thematic areas using the developed categorization model. Finally, it compares the performance and effectiveness of the developed proof of concept tool against the manual system. The study used a dataset comprising 206,625 text messages. The current process would take roughly 2.82 years to categorise this dataset whereas the trained SVM model would require only 6.4 minutes while achieving an accuracy of 70.4% demonstrating that the automated method is significantly faster, more scalable, and consistent when compared to the current manual categorization. These advantages make the SVM model a more efficient and effective tool for categorizing large unstructured text datasets. These results and the proof-of-concept tool developed demonstrate the potential for enhancing the efficiency and accuracy of message categorization on the Zambia U-report platform and other similar text messages-based platforms.

A feature representation method for biomedical scientific data based on composite text description

Feature representation is one of the key issues in data clustering. The existing feature representation of scientific data is not sufficient, which to some extent affects the result of scientific data clustering. Therefore, the paper proposes a concept of composite text description(CTD) and a CTD-based feature representation method for biomedical scientific data. The method mainly uses different feature weight algorisms to represent candidate features based on two types of data sources respectively, combines and finally strengthens the two feature sets. Experiments show that comparing with traditional methods, the feature representation method is more effective than traditional methods and can significantly improve the performance of biomedcial data clustering.

Text Feature Extraction and Classification Based on Convolutional Neural Network(CNN)

With the high-speed development of the Internet,a growing number of Internet users like giving their subjective comments in the BBS,blog and shopping website.These comments contains critics’attitudes,emotions,views and other information.Using these information reasonablely can help understand the social public opinion and make a timely response and help dealer to improve quality and service of products and make consumers know merchandise.This paper mainly discusses using convolutional neural network(CNN)for the operation of the text feature extraction.The concrete realization are discussed.Then combining with other text classifier make class operation.The experiment result shows the effectiveness of the method which is proposed in this paper.

Gender Prediction on Twitter Using Stream Algorithms with N-Gram Character Features

The rapid growth of social networks has produced an unprecedented amount of user-generated data, which provides an excellent opportunity for text mining. Authorship analysis, an important part of text mining, attempts to learn about the author of the text through subtle variations in the writing styles that occur between gender, age and social groups. Such information has a variety of applications including advertising and law enforcement. One of the most accessible sources of user-generated data is Twitter, which makes the majority of its user data freely available through its data access API. In this study we seek to identify the gender of users on Twitter using Perceptron and Nai ve Bayes with selected 1 through 5-gram features from tweet text. Stream applications of these algorithms were employed for gender prediction to handle the speed and volume of tweet traffic. Because informal text, such as tweets, cannot be easily evaluated using traditional dictionary methods, n-gram features were implemented in this study to represent streaming tweets. The large number of 1 through 5-grams requires that only a subset of them be used in gender classification, for this reason informative n-gram features were chosen using multiple selection algorithms. In the best case the Naive Bayes and Perceptron algorithms produced accuracy, balanced accuracy, and F-measure above 99%.

Research on Privacy Disclosure Detection Method in Social Networks Based on Multi-Dimensional Deep Learning

In order to effectively detect the privacy that may be leaked through social networks and avoid unnecessary harm to users,this paper takes microblog as the research object to study the detection of privacy disclosure in social networks.First,we perform fast privacy leak detection on the currently published text based on the fastText model.In the case that the text to be published contains certain private information,we fully consider the aggregation effect of the private information leaked by different channels,and establish a convolution neural network model based on multi-dimensional features(MF-CNN)to detect privacy disclosure comprehensively and accurately.The experimental results show that the proposed method has a higher accuracy of privacy disclosure detection and can meet the real-time requirements of detection.

Feature Selection Method by Applying Parallel Collaborative Evolutionary Genetic Algorithm

Feature selection is one of the important topics in text classification. However, most of existing feature selection methods are serial and inefficient to be applied to massive text data sets. In this case, a feature selection method based on parallel collaborative evolutionary genetic algorithm is presented. The presented method uses genetic algorithm to select feature subsets and takes advantage of parallel collaborative evolution to enhance time efficiency, so it can quickly acquire the feature subsets which are more representative. The experimental results show that, for accuracy ratio and recall ratio, the presented method is better than information gain, x2 statistics, and mutual information methods; the consumed time of the presented method with only one CPU is inferior to that of these three methods, but the presented method is supe rior after using the parallel strategy.

Event-based Two-stage Non-intrusive Load Monitoring Method Involving Multi-dimensional Features

This paper proposes an event-based two-stage Nonintrusive load monitoring(NILM)method involving multidimensional features,which is an essential technology for energy savings and management.First,capture appliance events using a goodness of fit test and then pair the on-off events.Then the multi-dimensional features are extracted to establish a feature library.In the first stage identification,several groups of events for the appliance have been divided,according to three features,including phase,steady active power and power peak.In the second stage identification,a“one against the rest”support vector machine(SVM)model for each group is established to precisely identify the appliances.The proposed method is verified by using a public available dataset;the results show that the proposed method contains high generalization ability,less computation,and less training samples.