Supervised Contrastive Learning with Term Weighting for Improving Chinese Text Classification

With the rapid growth of information retrieval technology,Chinese text classification,which is the basis of information content security,has become a widely discussed topic.In view of the huge difference compared with English,Chinese text task is more complex in semantic information representations.However,most existing Chinese text classification approaches typically regard feature representation and feature selection as the key points,but fail to take into account the learning strategy that adapts to the task.Besides,these approaches compress the Chinese word into a representation vector,without considering the distribution of the term among the categories of interest.In order to improve the effect of Chinese text classification,a unified method,called Supervised Contrastive Learning with Term Weighting(SCL-TW),is proposed in this paper.Supervised contrastive learning makes full use of a large amount of unlabeled data to improve model stability.In SCL-TW,we calculate the score of term weighting to optimize the process of data augmentation of Chinese text.Subsequently,the transformed features are fed into a temporal convolution network to conduct feature representation.Experimental verifications are conducted on two Chinese benchmark datasets.The results demonstrate that SCL-TW outperforms other advanced Chinese text classification approaches by an amazing margin.

An Optimized Chinese Filtering Model Using Value Scale Extended Text Vector

With the development of Internet technology,the explosive growth of Internet information presentation has led to difficulty in filtering effective information.Finding a model with high accuracy for text classification has become a critical problem to be solved by text filtering,especially for Chinese texts.This paper selected the manually calibrated Douban movie website comment data for research.First,a text filtering model based on the BP neural network has been built;Second,based on the Term Frequency-Inverse Document Frequency(TF-IDF)vector space model and the doc2vec method,the text word frequency vector and the text semantic vector were obtained respectively,and the text word frequency vector was linearly reduced by the Principal Component Analysis(PCA)method.Third,the text word frequency vector after dimensionality reduction and the text semantic vector were combined,add the text value degree,and the text synthesis vector was constructed.Experiments show that the model combined with text word frequency vector degree after dimensionality reduction,text semantic vector,and text value has reached the highest accuracy of 84.67%.

Sentiment Analysis for Chinese Text Based on Emotion Degree Lexicon and Cognitive Theories

The mass data of social media and social networks generated by users play an important role in tracking users’sentiments and opinions online.A good polarity lexicon which can effectively improve the classification results of sentiment analysis is indispensable to analyze the user’s sentiments.Inspired by social cognitive theories,we combine basic emotion value lexicon and social evidence lexicon to improve traditional polarity lexicon.The proposed method obtains significant improvement in Chinese text sentiment analysis by using the proposed lexicon and new syntactic analysis method.

Non-Independent Term Selection for Chinese Text Categorization

Chinese text categorization differs from English text categorization due to its much larger term set （of words or character n-grams）, which results in very slow training and working of modern high-performance classifiers. This study assumes that this high-dimensionality problem is related to the redundancy in the term set, which cannot be solved by traditional term selection methods. A greedy algorithm framework named ＂non-independent term selection＂ is presented, which reduces the redundancy according to string-level correlations. Several preliminary implementations of this idea are demonstrated. Experiment results show that a good tradeoff can be reached between the performance and the size of the term set.

Intercultural Communication and Chinese Tourism Texts Translation

Intercultural communication language plays a crucial role in our global tourism.When we are doing translation we are doing intercultural communication in a sense,so it is necessary for translators to have intercultural communication awareness and be sensitive to the cultural elements in translation.Taking the perspective of intercultural communication,this paper analyses the cultural elements in Chinese tourism material translation in terms of culturally-loaded words and terms,and presents certain translation techniques a translator can use to deal with culturally-loaded words in their translation.

Chinese News Text Classification Based on Convolutional Neural Network

With the explosive growth of Internet text information,the task of text classification is more important.As a part of text classification,Chinese news text classification also plays an important role.In public security work,public opinion news classification is an important topic.Effective and accurate classification of public opinion news is a necessary prerequisite for relevant departments to grasp the situation of public opinion and control the trend of public opinion in time.This paper introduces a combinedconvolutional neural network text classification model based on word2vec and improved TF-IDF:firstly,the word vector is trained through word2vec model,then the weight of each word is calculated by using the improved TFIDF algorithm based on class frequency variance,and the word vector and weight are combined to construct the text vector representation.Finally,the combined-convolutional neural network is used to train and test the Thucnews data set.The results show that the classification effect of this model is better than the traditional Text-RNN model,the traditional Text-CNN model and word2vec-CNN model.The test accuracy is 97.56%,the accuracy rate is 97%,the recall rate is 97%,and the F1-score is 97%.

Digital Rights Management for a Chinese XML Text Centre

The Electronic Text Centre of the OpenUniversity of Hong Kong(OUHK)has been in full operationsince early 2001.It currently houses 7,300+electronictexts,including free electronic titles,electronic titlespurchased directly from the market,and about,1,000 locallyproduced electronic titles.The locally produced titles are notavailable in the market but require local digitization andnegotiation with publishers with regard to the right to use(RTU)them so as to meet the learning needs of the OUHKcommunity.Nearl...

English Translation Study of Chinese Tourism Texts Based on the Skopos Theory--A Case Study of Hubei Scenic-spot Translation

Nowadays, China has witnessed vigorous development in tourism industry, and it has made a great contribution to Chinese economic growth. In order to draw more foreign tourists and demonstrate the unique charm and cultural deposits of Chinese landscapes, the translators should capitalize on appropriate translation methods so as to guarantee the translation quality.The thesis analyzes the guiding role of Skopos Theory in tourism texts with a lot of examples, taking the Hubei scenic-spot translation as a carrier, which has important guiding significanse to translators.

Hierarchical Classification of Chinese Documents Based on N grams

We explore the techniques of utilizing N gram information to categorize Chinese text documents hierarchically so that the classifier can shake off the burden of large dictionaries and complex segmentation processing, and subsequently be domain and time independent. A hierarchical Chinese text classifier is implemented. Experimental results show that hierarchically classifying Chinese text documents based N grams can achieve satisfactory performance and outperforms the other traditional Chinese text classifiers.

Multi-Label Chinese Comments Categorization: Comparison of Multi-Label Learning Algorithms

Multi-label text categorization refers to the problem of categorizing text througha multi-label learning algorithm. Text classification for Asian languages such as Chinese isdifferent from work for other languages such as English which use spaces to separate words.Before classifying text, it is necessary to perform a word segmentation operation to converta continuous language into a list of separate words and then convert it into a vector of acertain dimension. Generally, multi-label learning algorithms can be divided into twocategories, problem transformation methods and adapted algorithms. This work will usecustomer's comments about some hotels as a training data set, which contains labels for allaspects of the hotel evaluation, aiming to analyze and compare the performance of variousmulti-label learning algorithms on Chinese text classification. The experiment involves threebasic methods of problem transformation methods: Support Vector Machine, Random Forest,k-Nearest-Neighbor;and one adapted algorithm of Convolutional Neural Network. Theexperimental results show that the Support Vector Machine has better performance.

A Short Text Classification Model Based on Chinese Part-of-Speech Information and Mutual Learning

Short text classification is one of the common tasks in natural language processing.Short text contains less information,and there is still much room for improvement in the performance of short text classification models.This paper proposes a new short text classification model ML-BERT based on the idea of mutual learning.ML-BERT includes a BERT that only uses word vector informa-tion and a BERT that fuses word information and part-of-speech information and introduces transmissionflag to control the information transfer between the two BERTs to simulate the mutual learning process between the two models.Experi-mental results show that the ML-BERT model obtains a MAF1 score of 93.79%on the THUCNews dataset.Compared with the representative models Text-CNN,Text-RNN and BERT,the MAF1 score improves by 8.11%,6.69%and 1.69%,respectively.

A Text Zero-Watermarking Algorithm Based on Chinese Phonetic Alphabets

The text watermarking is a feasible method to protect the copyright from being copied and tampered. In this paper, a text zero-watermarking algorithm is proposed based on the connection between the Chinese characters and the Chinese phonetic alphabets. According to the predefined interval threshold, the proposed algorithm extracts the characteristics of the text content by valuing on the basis of the custom of Chinese phonetic alphabets. After being chaotic transformed, the algorithm combines the text characteristics with the embedded watermarking information in the Chinese text. The experimental results show that the watermarking＇s capability of preventing tampering is up to 0.1%, which demonstrates the strong robustness and resistance to aggressive behavior of the algorithm.

The text design for continuous speech database of standard Chinese

Well developed continuous speech recognition and synthesis systems demand a high quality continuous speech database which is compact and valid, and whose scientific design would benefit from incorporating linguistic and phonetic knowledge. It is argued that at the present stage the database should be limited to read speech. To describe those very complex variabilities in continuous speech, the following speech units are proposed: (1) 401syllables without tone; (2) 415 inter-syllabic diphones, (3) 3035 inter-syllabic triphones, (4) 781 inter-syllabic final-initial structures. The 17 basic sefltence patterns in standard Chinese are summarized to cover the most important prosodic phenomena. By using the automatic method,2393 sentences and 388 phrases are selected by above phonetic rules from a large corpus, which includes People's Daily in recent years, TV play scripts and dictionary entries, as the reading text of continuous speech recognition database in standard Chinese. This set of sentences and pbrases covers 99.8% syllables without counting tones, 100% inter-syllable diphones, 99.6% inter-syllable triphones and 100% sentence patterns.