Chinese Text Classification Research Articles

Using big data database to construct new GFuzzy text mining and decision algorithm for targeting and classifying customers

After an enterprise builds a data warehouse, it can record information related to customer interactions using structured and unstructured data. The intention is to convert these data into useful information for decision-making to ensure business continuity. Hence, this study proposes a new Chinese text classification model for the project management office (PMO) using fuzzy semantics and text mining techniques. First, content analysis is performed on the unstructured data to convert important textual information and compile it into a keyword index. Next, a classification and decision algorithm for grey situations and fuzzy (GFuzzy) is used to categorize textual data by three characteristics: maximum impact, moderate impact, and minimum impact. The purpose is to analyze consumer behaviors for the accurate classification of customers. Lastly, a more effective marketing strategy is formulated to target the various customer combinations, growth models, and the best mode of service. A company database of interactions with customers is used to construct a text mining model and to analyze the decision process of its PMO. The purpose is to test the feasibility and validity of the proposed model so that enterprises are provided with better marketing strategies and PMO processes aimed at their customers.

Semi supervised classification of scientific and technical literature based on semi supervised hierarchical description of improved latent dirichlet allocation (LDA)

Chinese text classification problem was studied based on domain ontology graph (DOG) of semi-supervised conceptual clustering to solve the problem that English word disambiguation method cannot be applied to Chinese text classification. Structure model of domain ontology graph, text classification algorithm in HowNet dictionary and KLSeeker ontology and so on were used to realize accurate classification of Chinese text and display effectiveness of algorithm. Chinese text classification model in domain ontology graph based on conceptual clustering was developed from the angle of decreasing human participation in ontology construction as much as possible in the paper. Aimed at application domain of Chinese web text, the algorithm can generate DOG of knowledge conceptualization automatically. At the same time, document ontology graph (DocOG) was defined to represent contents of individual text document. DocOG extracting target realized text classification based on ontology by matching of single document ontology and domain ontology. Finally, example calculation analysis and actual data test set experiment were given in experimental stage. The result shows that proposed Chinese text classification method has higher classification accuracy and reflects effectiveness of design.

Hybrid Attention Networks for Chinese Short Text Classification

To improve the classification performance for Chinese short text with automatic semantic feature selection, in this paper we propose the Hybrid Attention Networks (HANs) which combines the word- and character-level selective attentions. The model firstly applies RNN and CNN to extract the semantic features of texts. Then it captures class-related attentive representation from word- and character-level features. Finally, all of the features are concatenated and fed into the output layer for classification. Experimental results on 32-class and 5-class datasets show that, our model outperforms multiple baselines by combining not only the word- and character-level features of the texts, but also class-related semantic features by attentive mechanism.

Chinese Short Text Categorization Based on Semi-Supervised Learning

Most of the text on the Internet is unlabelled with the rapid development of the Internet, and it is difficult for us to classify the unlabelled text accurately under the condition of insufficient labelled samples. Sei-supervised learning is a method, which combines the labelled samples with the unlabelled samples, can solve the problem in a better way. AdaBoost is one of the most representative algorithm of boosting algorithms, and this paper used the improved decision tree to be weak classifiers of the AdaBoost. Based on this, this paper devised a boosting algorithm which was based on semi-supervised learning and the improved decision tree. The algorithm is devoted to solving the problem of the Chinese short text categorization under the condition of insufficient labelled samples. Experiments show that the algorithm can effectively improve the performance of the Chinese short text categorization on balanced and imbalanced data sets.

A Text Feature Selection Method Based on the Small World Algorithm

The methods of feature selections have a profound influence on the performance of text categorization. Due to the deficiency of the mathematical models, traditional feature selection methods are less likely to get remarkable improvement. Small world algorithm is a kind of swarm intelligence optimization algorithm to solve the optimization problem. As feature selection is the nature of the optimal combination problem of discrete space, in this paper we attempted to improve the effect of feature selection by making full use of the small world algorithm. We used the English R8 single label corpus of Reuters 21578 classic corpus and the data for English text classification experiment, while in the Chinese text classification experiment we chose 3600 texts from Chinese Fudan corpus and halved them for building training and testing sets. By using KNN and SVM classifier respectively in the candidate dimensions of 300,450,600, the results revealed that the small world algorithm method optimized the keywords selected and its effect was superior to the traditional feature selection methods.

An Improved CHI Feature Selection Method for Chinese Text Classification

An Improved CHI Feature Selection Method for Chinese Text Classification

Hybrid Chinese text classification approach using general knowledge from Baidu Baike

Most of the previous studies focused on enriching text representation to address text classification (TC) task. However, conventional classification approaches with VSM (vector space model) on Chinese text study intensively only the words and their relationship in some specific corpus/dataset but ignore the basic concept of categories and the general knowledge behind the words learned and used to recognize entities by people. This paper focuses on enriching text representation and proposes a novel approach, which complements information from the online Chinese encyclopedia Baidu Baike for Chinese TC. The similarities between every text and each concept of categories and the most related words from Baidu Baike are added to the feature space. The performance of the proposed approach is measured on the Fudan University TC corpus, which is an imbalanced Chinese dataset. In the experiments, the proposed Baidu Baike‐based concept similarity approach obtains promising results when compared with a previous research and the conventional method, with macro‐precision of 90.31%, recall of 75.45%, and F1 score 80.32%, which are about 0.02%, 0.15%, 0.12%, respectively, higher than the conventional method, which obviously improves the recall for some small categories while keeping precision at high level and improving the macro F1 score. Moreover, the proposed approach has good expandability, so that many other knowledge bases could be integrated and many other concepts could be referred to improve the effectiveness. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

A Chinese text classification system based on Naive Bayes algorithm

In this paper, aiming at the characteristics of Chinese text classification, using the ICTCLAS(Chinese lexical analysis system of Chinese academy of sciences) for document segmentation, and for data cleaning and filtering the Stop words, using the information gain and document frequency feature selection algorithm to document feature selection. Based on this, based on the Naive Bayesian algorithm implemented text classifier , and use Chinese corpus of Fudan University has carried on the experiment and analysis on the system.

SAW Classification Algorithm for Chinese Text Classification

Considering the explosive growth of data, the increased amount of text data’s effect on the performance of text categorization forward the need for higher requirements, such that the existing classification method cannot be satisfied. Based on the study of existing text classification technology and semantics, this paper puts forward a kind of Chinese text classification oriented SAW (Structural Auxiliary Word) algorithm. The algorithm uses the special space effect of Chinese text where words have an implied correlation between text information mining and text categorization for high-correlation matching. Experiments show that SAW classification algorithm on the premise of ensuring precision in classification, significantly improve the classification precision and recall, obviously improving the performance of information retrieval, and providing an effective means of data use in the era of big data information extraction.

A method for Chinese text classification based on apparent semantics and latent aspects

The existing methods for text classification fail to achieve high accuracy in processing Chinese texts, for that the basic unit of Chinese texts is not hanzis but Chinese phrases, and there is no natural delimiter in Chinese texts to separate the phrases. Things go even worse in the case of processing large number of Chinese Web texts, for these texts often lack of enough context, because most of these text are often short, irregular and sparse. In this paper, a new classification method is proposed for Chinese texts based on apparent semantics and latent aspects (ASLA). First, the apparent semantics of Chinese text are extracted as features instead of hanzis by BaiduBaike; Second, pLSA is applied for mining the latent aspects of these apparent semantics. Third, the relevant degree of a document to a category is calculated according to the apparent semantics and latent aspects. Finally, the category of a document is determined by the relevant degree. The proposed method is able to process Chinese web short text well with mini train data. Our experiments showed that the proposed method is promising, and it outperforms pLSA,SVM, KNN and CRF in the case of training data is not enough and the text is irregular.

Improvement of automatic Chinese text classification by combining multiple features

In this paper, we present an effective way of combining character‐based (N‐gram) and word‐based approaches for Chinese text classification. Uni‐gram and bi‐gram features are considered as the baseline model, which are then combined with word features of length greater than or equal to 3. A weight coefficient that can be used to give higher weights to word features is also introduced. We further employ a serial approach based on feature transformation and dimension reduction techniques. The results of McNemar's test indicate that the performance is significantly improved by our proposed method. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

A Kind of Self-Constructed Category Dictionary in Chinese Text Classification

By means of word-segmentation technology in TRIP database and each word that appears in a database will be account in detail, a kind of self-constructed category dictionary (SCC-dictionary) in Chinese text classification is proposed. For solving high dimension and sparseness problem exit in vector space model, a four-dimensional feature vector space model (FFVSM) is presented in this paper. With Support Vector Machine (SVM) algorithm, the text classifier is designed. Experimental results show there are two achievements in this paper: first, SCC-dictionary can replace the artificial-written dictionary with the same effect; second, the FFVSM will not only reduce the computing load than high-dimensional feature vector space model, but also keep the precision of classification as 86.87%, recall rate as 95.12%, and F1 value as 90.81%.

A Comparative Study on Feature Selection in Chinese Text Classification Problem

Information explosion brings lots of challenges to text classification. The dimension disaster led to a sharp increase of computational complexity and lower classification accuracy. Therefore, it is critical to use feature selection techniques before actual classification. Automatic classification of English text has been researched for many years, but little on Chinese text. In this paper, several classic feature selection methods, namely TF, IG and CHI, are compared on classifying Chinese text. Meanwhile, we take imbalanced data into consideration in the paper. Experimental results show that CHI performed better than IG and TF when the dataset is imbalanced, but no obvious difference on balanced data.

Review of Chinese Short Text Classification

Because features of Chinese short text are different from long text, current text classification technology of long text classification are not suit of short text classification. This paper summarizes features, various methods and Application for Chinese short text classification, and pointes out their advantages, disadvantages and applicability scope. Establishing Open data set and unified framework standard are two main Problems and further research directions.

A Text Categorization Algorithm Based on Sense Group

Giving further consideration on linguistic feature, this study proposes an algorithm of Chinese text categorization based on sense group. The algorithm extracts sense group by analyzing syntactic and semantic properties of Chinese texts and builds the category sense group library. SVM is used for the experiment of text categorization. The experimental results show that the precision and recall of the new algorithm based on sense group is better than that of traditional algorithms.

An EMM-based Approach for Text Classification

In this paper, a classification method named explicit Markov model is applied for text classification. Currently some machine learning technologies, such as support vector machine (SVM), have been discussed widely in text classification. However, these methods consider that any two features are independent and ignore the language structure information. Hidden Markov model is a powerful tool for sequence tagging problems. This paper presents a new method called explicit Markov model (EMM) which is based on HMM for text classification. EMM make better use of the context information between the observation symbols. Our experiments are conducted on three datasets: Reuter's 21578 R8 dataset, WebKB and Fudan University Chinese text classification corpus. Experimental results show that the performance of EMM is comparable to SVM for text classification.

Domain ontology graph model and its application in Chinese text classification

This paper proposes an ontology learning method which is used to generate a graphical ontology structure called ontology graph. The ontology graph defines the ontology and knowledge conceptualization model, and the ontology learning process defines the method of semiautomatic learning and generates ontology graphs from Chinese texts of different domains, the so-called domain ontology graph (DOG). Meanwhile, we also define two other ontological operations—document ontology graph generation and ontology graph-based text classification, which can be carried out with the generated DOG. This research focuses on Chinese text data, and furthermore, we conduct two experiments: the DOG generation and ontology graph-based text classification, with Chinese texts as the experimental data. The first experiment generates ten DOGs as the ontology graph instances to represent ten different domains of knowledge. The generated DOGs are then further used for the second experiment to provide performance evaluation. The ontology graph-based approach is able to achieve high text classification accuracy (with 92.3 % in f-measure) over other text classification approaches (such as 86.8 % in f-measure for tf–idf approach). The better performance in the comparative experiments reveals that the proposed ontology graph knowledge model, the ontology learning and generation process, and the ontological operations are feasible and effective.

A Method for Chinese Short Text Classification Considering Effective Feature Expansion

This paper presents a Chinese short text classification method which considering extended semantic constraints and statistical constraints. This method uses “HowNet” tools to build the attribute set of concept. when coming to the part of feature expansion, we judge the collocation between the attribute words of original text and the characteristics before and after expansion as the semantic constraints, and calculate the ratio between the mutual information of the original contents and the features before expansion versus the mutual information of the original contents and the features after expansion as statistical constraints, so as to judge whether feature expansion is effective with this two constraints , then rationally use various semantic relation word-pairs in short text classification. Experiments show that this method can use semantic relations in Chinese short text classification effectively, and improve the classification performance.

Multiclass Boosting with Adaptive Group‐Based kNN and Its Application in Text Categorization

AdaBoost is an excellent committee‐based tool for classification. However, its effectiveness and efficiency in multiclass categorization face the challenges from methods based on support vector machine (SVM), neural networks (NN), naïve Bayes, and k‐nearest neighbor (kNN). This paper uses a novel multi‐class AdaBoost algorithm to avoid reducing the multi‐class classification problem to multiple two‐class classification problems. This novel method is more effective. In addition, it keeps the accuracy advantage of existing AdaBoost. An adaptive group‐based kNN method is proposed in this paper to build more accurate weak classifiers and in this way control the number of basis classifiers in an acceptable range. To further enhance the performance, weak classifiers are combined into a strong classifier through a double iterative weighted way and construct an adaptive group‐based kNN boosting algorithm (AGkNN‐AdaBoost). We implement AGkNN‐AdaBoost in a Chinese text categorization system. Experimental results showed that the classification algorithm proposed in this paper has better performance both in precision and recall than many other text categorization methods including traditional AdaBoost. In addition, the processing speed is significantly enhanced than original AdaBoost and many other classic categorization algorithms.

Chinese text classification for small sample set

Text classification is one of the most important topics in the fields of Internet information management and natural language processing. Machine learning based text classification methods are currently most popular ones with better performance than rule based ones. But they always need lots of training samples, which not only brings heavy work for previous manual classification, but also puts forward a higher request for storage and computing resources during the computer post-processing. Naïve Bayes algorithm is one of the most effective methods for text classification with the same problem. Only in the large training sample set can it get a more accurate result. This paper mainly studies Naïve Bayes classification algorithm for Chinese text based on Poisson distribution model and feature selection. The experimental results have shown that this method keeps high classification accuracy even in a small sample set.