Chinese Word Segmentation Research Articles

MRE: A Military Relation Extraction Model Based on BiGRU and Multi-Head Attention

A great deal of operational information exists in the form of text. Therefore, extracting operational information from unstructured military text is of great significance for assisting command decision making and operations. Military relation extraction is one of the main tasks of military information extraction, which aims at identifying the relation between two named entities from unstructured military texts. However, the traditional methods of extracting military relations cannot easily resolve problems such as inadequate manual features and inaccurate Chinese word segmentation in military fields, failing to make full use of symmetrical entity relations in military texts. With our approach, based on the pre-trained language model, we present a Chinese military relation extraction method, which combines the bi-directional gate recurrent unit (BiGRU) and multi-head attention mechanism (MHATT). More specifically, the conceptual foundation of our method lies in constructing an embedding layer and combining word embedding with position embedding, based on the pre-trained language model; the output vectors of BiGRU neural networks are symmetrically spliced to learn the semantic features of context, and they fuse the multi-head attention mechanism to improve the ability of expressing semantic information. On the military text corpus that we have built, we conduct extensive experiments. We demonstrate the superiority of our method over the traditional non-attention model, attention model, and improved attention model, and the comprehensive evaluation value F1-score of the model is improved by about 4%.

A Neural N-Gram-Based Classifier for Chinese Clinical Named Entity Recognition

Clinical Named Entity Recognition (CNER) focuses on locating named entities in electronic medical records (EMRs) and the obtained results play an important role in the development of intelligent biomedical systems. In addition to the research in alphabetic languages, the study of non-alphabetic languages has attracted considerable attention as well. In this paper, a neural model is proposed to address the extraction of entities from EMRs written in Chinese. To avoid erroneous noise being caused by the Chinese word segmentation, we employ the character embeddings as the only feature without extra resources. In our model, concatenated n-gram character embeddings are used to represent the context semantics. The self-attention mechanism is then applied to model long-range dependencies of embeddings. The concatenation of the new representations obtained by the attention module is taken as the input to bidirectional long short-term memory (BiLSTM), followed by a conditional random field (CRF) layer to extract entities. The empirical study is conducted on the CCKS-2017 Shared Task 2 dataset to evaluate our method and the experimental results show that our model outperforms other approaches.

Psycho-Linguistic Changes Associated With Historical Celebrities in Henan Using Classical Chinese Big Data.

Chinese civilization has a long history, and the Central Plains, with Henan as the core, is one of its birthplaces. Understanding the psycho-linguistic changes in Henan is of great significance for understanding the evolution and formation of national cultural psychology. The traditional method is mainly qualitative or speculative, which makes the consistency among the research results low. Moreover, because most of the research are conducted in a specific period or on a specific figure, they are relatively scattered and unsystematic. To systematically and quantitatively study the psychological changes in the Central Plains represented by Henan Province, this article aimed to examine the self-reported discourses of historical celebrities in Henan in official history and their psycho-linguistic changes based on the classical Chinese Linguistic Inquiry and Word Count (classical Chinese LIWC, CC-LIWC) psycholinguistic dictionary and the classical Chinese words segmentation system, using big data. The research found that the frequency of male words in Henan historical celebrities (F = 2.938, p < 0.05), of differ words (F = 4.767, p < 0.01), of motion words (F = 4.042, p < 0.01), and of time words (F = 5.412, p < 0.01) are significantly different among the five dynasties. The conclusion is that during the Spring and Autumn and the Warring States periods, 100 schools of thought contended, and the status of “scholars” in Henan rose. The frequency of male and differ words was at that point significantly higher than during the other dynasties. From “The Contention of a Hundred Schools of Thought” to “The Supremacy of Confucianism,” the scholars in Henan were in decline, and differential cognitive tendencies had diminished since the Han dynasty. During the period of the Three Kingdoms and Jin and Southern and Northern Dynasties, political powers and territories changed, and the historical celebrities in Henan show a remarkable tendency that were related to time and space. The psycho-linguistic changes found in this study are highly consistent with the development of social history, which indicates that the guidance of the social, political, and cultural environment has an important influence on the psycho-linguistic changes of social class. This is the first time that the text analysis system has been applied in classical Chinese to carry out quantitative research on the psycho-linguistic changes of ancient Chinese people, which provides new ideas and new methods for humanistic research.

Research on Recommendation of Online Materials Course Resources Based on Text Similarity

In order to solve the problem that it is difficult for college students to find learning resources related to thecourses they are learning quickly and accurately in blended learning. This paper proposes an online materials course resources recommendation method based on text similarity. Firstly, collecting the data of course resources on the online learning platform through web crawler technology. Secondly, preprocessing the data which contend deleting noise data, the Chinese word segmentation and calculating the course similarity based on cosine similarity then getting the course recommendation results according to the similarity ranking. Thirdly, evaluating the recommendation results and optimizing the similarity calculation method according to the evaluation results. Finally, the learners are recommended curriculum resources according to the similarity ranking results. According to the courses learned on the Superstar platform, the experiment recommends similar course resources on the XueYin Online platform. The results show that the online materials course resources recommendation method based on text similarity can recommend relevant online materials course resources for learners quickly and accurately, which has certain reference significance and application value for online course resources recommendation.

A review of Chinese named entity recognition

Named Entity Recognition (NER) is used to identify entity nouns in the corpus such as Location, Person and Organization, etc. NER is also an important basic of research in various natural language fields. The processing of Chinese NER has some unique difficulties, for example, there is no obvious segmentation boundary between each Chinese character in a Chinese sentence. The Chinese NER task is often combined with Chinese word segmentation, and so on. In response to these problems, we summarize the recognition methods of Chinese NER. In this review, we first introduce the sequence labeling system and evaluation metrics of NER. Then, we divide Chinese NER methods into rule-based methods, statistics-based machine learning methods and deep learning-based methods. Subsequently, we analyze in detail the model framework based on deep learning and the typical Chinese NER methods. Finally, we put forward the current challenges and future research directions of Chinese NER technology.

Chinese Word Segmentation Based on Self‐Learning Model and Geological Knowledge for the Geoscience Domain

AbstractChinese word segmentation (CWS) is the foundational work of geological report text mining and has an important influence on various tasks, such as named entity recognition and relation extraction. In recent years, the accuracy of the domain‐general CWS model has been limited by the domain and large scale of the training corpus, especially data on Chinese geological texts. Training these CWS models also requires much manually annotated data, which takes a large amount of time and effort. When applying these existing models/methods directly to the geoscience domain, the segmentation accuracy and performance will drop dramatically. To address this problem, we pretrain the Bidirectional Encoder Representations from Transformer (BERT), which can leverage unlabeled domain‐specific knowledge, on unlabeled Chinese geological text and then input them into a Bidirectional long short‐term memory and Conditional random field (BiLSTM‐CRF) model for extracting text features. Finally, the predicted tags are decoded by the CRF. The experimental results show that the F1 score of the proposed model reaches 96.2% on the constructed test set of geological texts. Additionally, experiments illustrate that our proposed model achieves comparable performance to that of other state‐of‐the‐art models, and the proposed cyclic self‐learning strategy can be further extended to other domains.

Online Textual Symptomatic Assessment Chatbot Based on Q&amp;A Weighted Scoring for Female Breast Cancer Prescreening

The increasing number of female breast cancer (FBC) incidences in the East predominated by Chinese language speakers has generated concerns over women’s medicare. To minimize the mortality rate associated with FBC in the region, governments and health experts are jointly encouraging women to undergo mammography screening at the earliest suspicion of FBC symptoms. However, studies show that a huge number of women affected by FBC tend to delay medical consultation at its early stage as a result of factors such as complacency due to unawareness of FBC symptoms, procrastination due to lifestyle, and the feeling of embarrassment in discussing private matters especially with medical personnel of the opposite gender. To address these issues, we propose a symptomatic assessment chatbot (SAC) based on artificial intelligence (AI) designed to prescreen women for FBC symptoms via a textual question-and-answer (Q&amp;A) approach. The purpose of our chatbot is to assist women in engaging in communication regarding FBC symptoms, so as to subsequently initiate formal medical consultations for early FBC diagnosis and treatment. We implemented the SAC systematically with some of the latest natural language processing (NLP) techniques suitable for Chinese word segmentation (CWS) and trained the model with real-world FBC Q&amp;A data obtained from a major hospital in Taiwan. The results from our experiments showed that the SAC achieved very high accuracy in FBC assessment scoring in comparison to FBC patients’ screening benchmark scores obtained from doctors.

Domain Adaptive Chinese Word Segmentation Method for power data security classification

Chinese word segmentation (CWS) is an important task for Chinese NLP, and also an essential pre-processing step to establish a word-root database for security classification of power data, covering different domains such as laws & regulations, power. It is impracticable to label a large number of training corpus for each domain, which brings great challenge to the supervised statistical learning method to carry out effective CWS. Therefore, a Chinese word segmentation approach based on dictionary and semi-supervised conditional random field (SS-CRF ) is presented. At first, a CRF model for CWS is trained with self-training and active learning algorithms and used to conduct CWS task. Then the dictionary features are introduced to correct the result of CRF based segmentation by adopting RMM algorithm. Experiments on a cross domain segmentation task show that the proposed method can effectively improve the domain-adaptive performance of CWS.

NE–LP: Normalized entropy- and loss prediction-based sampling for active learning in Chinese word segmentation on EHRs

Electronic health records (EHRs) in hospital information systems contain patients’ diagnoses and treatments, so EHRs are essential to clinical data mining. Of all the tasks in the mining process, Chinese word segmentation (CWS) is a fundamental and important one, and most state-of-the-art methods greatly rely on large scale of manually annotated data. Since annotation is time-consuming and expensive, efforts have been devoted to techniques, such as active learning, to locate the most informative samples for modeling. In this paper, we follow the trend and present an active learning method for CWS in EHRs. Specifically, a new sampling strategy combining normalized entropy with loss prediction (NE–LP) is proposed to select the most valuable data. Meanwhile, to minimize the computational cost of learning, we propose a joint model including a word segmenter and a loss prediction model. Furthermore, to capture interactions between adjacent characters, bigram features are also applied in the joint model. To illustrate the effectiveness of NE–LP, we conducted experiments on EHRs collected from the Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine. The results demonstrate that NE–LP consistently outperforms conventional uncertainty-based sampling strategies for active learning in CWS

Chinese image caption of Inceptionv4 and double-layer GRUs based on attention mechanism

In recent years, there has been a wave of research on English image caption at home and abroad. However, due to the particularity of Chinese image caption task, the research on Chinese image caption has not made good progress. In order to solve this problem, a new Chinese image caption model is implemented. Firstly, the AI challenge dataset is enhanced, and then the Chinese text data of the dataset is preprocessed by Chinese word segmentation tool word2vec. Secondly, based on the encoder-decoder framework, the image visual features are extracted by Inceptionv4 network, the attention mechanism is incorporated in the process of feature extraction and the Chinese sentences are generated by double-layer GRUs network. In the process of training, Adam is used to optimize the algorithm. Finally, A GUI interface is designed to better show the experimental effect. Experiments show that the new Chinese image caption model can automatically generate more fluent and more accurate Chinese caption sentences, and the trained model has excellent performance in many evaluation indexes.

A Disease Interaction Retrieval and Recommendation System based on NLP Technology

There is a great request for a Chatbot system that uses natural language processing for disease retrieval and recommendation in medical treatment with interactive functions. This paper uses cMedQA2 data for training, developing and testing. The Chinese word segmentation tool jieba to reassemble the text data of consecutive words into the word sequences, and uses Bag of Words to count the frequency of keywords appearing in sentences, and uses N-gram method to predict whether the evaluation and prediction of a sentence is reasonable. This paper proposes an improved TextCNN network framework structure, which uses overlap technology to ensure the contextual coherence of information. The final experimental results show that there are 96.2% on the training dataset and 87.5% on the testing dataset.

A Survey of CRF Algorithm Based Knowledge Extraction of Elementary Mathematics in Chinese

Chinese word segmentation is an important research direction in related research on elementary mathematics knowledge extraction. The speed of segmentation directly affects subsequent applications, and the accuracy of segmentation directly affects corresponding research in the next step. In the machine learning methods for extracting basic mathematical knowledge points, the Conditional Random Field (CRF) model implements new word discovery well, and is increasingly used in knowledge extraction of basic mathematics. This article first introduces the traditional CRF process of named entity recognition. Then, an improved algorithm CRF++for conditional field model is proposed. Since the recognition rate of named entities based on traditional machine learning methods is not high, a post-processing method for entity recognition that automatically generates a dictionary is proposed. After identifying mathematical entities, a pruning strategy combining Viterbi algorithm and rules is proposed to achieve a higher recognition rate of elementary mathematical entities. Finally, several methods of disambiguation after entity recognition are introduced.

Hybrid Feature Fusion Learning Towards Chinese Chemical Literature Word Segmentation

The rapid increase in the number of chemical science literature has brought challenges to researchers in search and data analysis. For many chemical scientific literature, extracting information from text and using knowledge is the focus of research. However, the existing Chinese text word segmentation methods have low recognition rate for chemical terms. The reason is that the addition of many new vocabulary and mixed professional vocabulary of Chinese and English brings challenges to word segmentation. In this paper, we propose a word segmentation method of Chinese chemical literature based on hybrid feature fusion learning Model(HFFLM). HFFLM first establishes chemical science corpus (Chem-pku) to train Chinese word segmentation (CWS) tasks. In addition, HFFLM uses BiLSTM and CNN to extract document features and fuse them. Then, HFFLM combines boundary features to construct conditional random field to train the end-to-end CWS model. In the end, HFFLM makes visual analysis of the word segmentation results. The experimental results indicate that HFFLM has high accuracy and recall rate, and is suitable for chemical industry vocabulary extraction with mixed Chinese and English.

Chinese Personal Name Disambiguation Based on Clustering

Personal name disambiguation is a significant issue in natural language processing, which is the basis for many tasks in automatic information processing. This research explores the Chinese personal name disambiguation based on clustering technique. Preprocessing is applied to transform raw corpus into standardized format at the beginning. And then, Chinese word segmentation, part‐of‐speech tagging, and named entity recognition are accomplished by lexical analysis. Furthermore, we make an effort to extract features that can better disambiguate Chinese personal names. Some rules for identifying target personal names are created to improve the experimental effect. Additionally, many calculation methods of feature weights are implemented such as bool weight, absolute frequency weight, tf‐idf weight, and entropy weight. As for clustering algorithm, an agglomerative hierarchical clustering is selected by comparison with other clustering methods. Finally, a labeling approach is employed to bring forward feature words that can represent each cluster. The experiment achieves a good result for five groups of Chinese personal names.

Intelligent Acquisition and Analysis of Commodity Review Data

When shopping on online e-commerce platforms as they can only contact products through photos and other means, it is easy to have cognitive deviations on the products on online platforms. This paper makes intelligent acquisition and analysis of commodity review data by using web crawler tools. Through a series of operations such as comment data capture, data preprocessing, Chinese word segmentation, emotional tendency analysis, LDA model analysis, it analyzes buyers’ emotional tendency of this commodity to help consumers choose the commodities they really like.

Named entity recognition of local adverse drug reactions in Xinjiang based on transfer learning

For deep learning’s insufficient learning ability of a small amount of data in the Chinese named entity recognition based on deep learning, this paper proposes a named entity recognition of local adverse drug reactions based on Adversarial Transfer Learning, and constructs a neural network model ASAIBC consisting of Adversarial Transfer Learning, Self-Attention, independently recurrent neural network (IndRNN), Bi-directional long short-term memory (BiLSTM) and conditional random field (CRF). However, of the task of Chinese named entity recognition (NER), there are only few open labeled data sets. Therefore, this article introduces Adversarial Transfer Learning network to fully utilize the boundary of Chinese word segmentation tasks (CWS) and NER tasks for information sharing. Plus, the specific information in the CWS is also filtered. Combing with Self-Attention mechanism and IndRNN, this feature’s expression ability is enhanced, thus allowing the model to concern the important information of different entities from different levels. Along with better capture of the dependence relations of long sentences, the recognition ability of the model is further strengthened. As all the results gained from WeiBoNER and MSRA data sets by ASAIBC model are better than traditional algorithms, this paper conducts an experiment on the data set of Xinjiang local named entity recognition of adverse drug reactions (XJADRNER) based on manual labeling, with the accuracy, precision, recall and F-Score value being 98.97%, 91.01%, 90.21% and 90.57% respectively. These experimental results have shown that ASAIBC model can significantly improve the NER performance of local adverse drug reactions in Xinjiang.

Word Segmentation for Classical Chinese Buddhist Literature

With the growth of digital humanities, information technologies take on more important roles in humanities research, including the study of religion. To analyze text for further processing, many text analysis tools treat a word as a unit. However, in Chinese, there are no word boundary markers. Word segmentation is required for processing Chinese texts. Although several word segmentation tools are available for modern Chinese, there is still no practical word segmentation tool for Classical Chinese, especially for Classical Chinese Buddhist literature. In this paper, we adopt unsupervised and supervised learning techniques to build Classical Chinese word segmentation approaches for processing Buddhist literature. Normalized variation of branching entropy (nVBE) is adopted for unsupervised word segmentation. Conditional random fields (CRF) are used to generate supervised models for Classical Chinese word segmentation. The performance of our word segmentation approach achieves an F-score of up to 0.9396. The experimental results show that our proposed method is effective for correctly segmenting most Classical Chinese sentences in Buddhist literature. Our word segmentation method can be a fundamental tool for further text analysis and processing research, such as word embedding, syntactic parsing, and semantic labeling.

A Graph-based Model for Joint Chinese Word Segmentation and Dependency Parsing

Chinese word segmentation and dependency parsing are two fundamental tasks for Chinese natural language processing. The dependency parsing is defined at the word-level. Therefore word segmentation is the precondition of dependency parsing, which makes dependency parsing suffer from error propagation and unable to directly make use of character-level pre-trained language models (such as BERT). In this paper, we propose a graph-based model to integrate Chinese word segmentation and dependency parsing. Different from previous transition-based joint models, our proposed model is more concise, which results in fewer efforts of feature engineering. Our graph-based joint model achieves better performance than previous joint models and state-of-the-art results in both Chinese word segmentation and dependency parsing. Additionally, when BERT is combined, our model can substantially reduce the performance gap of dependency parsing between joint models and gold-segmented word-based models. Our code is publicly available at https://github.com/fastnlp/JointCwsParser

An Improved Full-text Retrieval for Elementary Education Resource Database System

With the rapid development of Internet and information technology, ways of teaching and learning in China’s elementary education have begun to change. In the face of massive resources, how to optimize the searching results and improve users’ satisfaction through the search services, has now become a major priority. Based on the in-depth study of full-text retrieval technology, combined with the actual searching needs of the elementary education resources, this paper focuses on Chinese word segmentation technology, and proposes a user-defined professional dictionary based on cell lexicon. Experiments show that using this professional dictionary to segment words improves the professionalism of word segmentation results and the accuracy of search results. Meanwhile, an improved full-text retrieval for elementary education resource database system based on Lucene is designed and implemented, including resource collection, text pre-processing, content indexing and resource retrieval. This system is successfully integrated and applied to the massive knowledge base platform for elementary education, which meets the needs of full-text retrieval of textbooks and supplementary content, and makes a good effect.

Developing an instant semantic analysis and feedback system to facilitate learning performance of online discussion

ABSTRACT By applying two-mode social networks and Chinese word segmentation technologies, a novel visualization tool, the instant semantic analysis and feedback system (ISAFS), is designed in this study to present the semantic networks of co-words and non-co-words used in learners’ discussion processes and assist learners in grasping the discussion direction to enhance online learning effectiveness. By conducting a quasi-experimental research, the ISAFS-assisted discussion board was assigned to the 32 learners of one class as the experimental group, whereas the general online discussion board was assigned to the 32 learners of the other class as the control group to support the online discussion learning activities of socio-scientific issues (SSIs). Analytical results show that the mean scores of overall, complexity, and perspective of socio-scientific reasoning performance have no statistically significant differences between the two groups. However, the in-depth interview results suggest that the learners in the experimental group gave positive feedback on the design concepts of the ISAFS and could explore the complexity and various perspectives of the issue through co-words and non-co-words presented on the ISAFS graph. Several suggestions regarding the functions and user interface of the ISAFS were also collected from the interviews to provide the directions for future studies.