Most Read
  • Published in last 1 year
  • In last 2 years
  • In last 3 years
  • All

Please wait a minute...
  • Select all
    |
  • Survey
    WU Youzheng, LI Haoran, YAO Ting, HE Xiaodong
    . 2022, 36(5): 1-20.
    Over the past decade, there has been a steady momentum of innovation and breakthroughs that convincingly push the limits of modeling single modality, e.g., vision, speech and language. Going beyond such research progresses made in single modality, the rise of multimodal social network, short video applications, video conferencing, live video streaming and digital human highly demands the development of multimodal intelligence and offers a fertile ground for multimodal analysis. This paper reviews recent multimodal applications that have attracted intensive attention in the field of natural language processing, and summarizes the mainstream multimodal fusion approaches from the perspectives of single modal representation, multimodal fusion stage, fusion network, fusion of unaligned modalities, and fusion of missing modalities. In addition, this paper elaborate the latest progresses of the vision-language pre-training.
  • Survey
    CUI Lei, XU Yiheng, LYU Tengchao, WEI Furu
    . 2022, 36(6): 1-19.
    Document AI, or Document Intelligence, is a relatively new research topic that refers to the techniques to automatically read, understand and analyze business documents. It is an important interdisciplinary study involving natural language processing and computer vision. In recent years, the popularity of deep learning technology has greatly advanced the development of Document AI tasks, such as document layout analysis, document information extraction, document visual question answering, and document image classification etc. This paper briefly introduces the early-stage heuristic rule-based document analysis, statistical machine learning based algorithms, as well as the deep learning-based approaches especially the pre-training approaches. Finally, we also look into the future direction of Document AI.
  • Survey
    CEN Keting, SHEN Huawei, CAO Qi, CHENG Xueqi
    Journal of Chinese Information Processing. 2023, 37(5): 1-21.
    As a self-supervised deep learning paradigm, contrastive learning has achieved remarkable results in computer vision and natural language processing. Inspired by the success of contrastive learning in these fields, researchers have tried to extend it to graph data and promoted the development of graph contrastive learning. To provide a comprehensive overview of graph contrastive learning, this paper summarizes recent works under a unified framework to highlight the development trends. It also catalogues the popular datasets and evaluation metrics for graph contrastive learning, and concludes with the possible future direction of the field.
  • Survey
    ZHANG Rujia, DAI Lu, WANG Bang, GUO Peng
    . 2022, 36(6): 20-35.
    Chinese named entity recognition (CNER) is one of the basic tasks of natural language processing applications such as question answering systems, machine translation and information extraction. Although traditional CNER system has achieved satisfactory experiment results with the help of manually designed domain-specific features and grammatical rules, it is still defected in aspects such as weak generalization ability, poor robustness and difficult maintenance. In recent years, deep learning techniques have been adopted to deal with the above shortcomings by automatically extracting text features in an end-to-end learning manner. This article surveys the recent advances of deep learning-based CNER. It first introduces the concepts, difficulties and applications of CNER, and introduces the common datasets and evaluation metrics. Recent neural network models for the CNER task are then grouped according to their network architectures, and representative models in each group are detailed. Finally, future research directions are discussed.
  • Survey
    AN Zhenwei, LAI Yuxuan, FENG Yansong
    . 2022, 36(8): 1-11.
    In recent years, legal artificial intelligence has attracted increasing attention for its efficiency and convenience. Among others, legal text is the most common manifestation in legal practice, thus, using natural language understanding method to automatically process legal text is an important direction for both academia and industry. In this paper, we provide a gentle survey to summarize recent advances on natural language understanding for legal texts. We first introduce the popular task setups, including legal information extraction, legal case retrieval, legal question answering, legal text summarization, and legal judgement prediction. We further discuss the main challenges from three perspectives: understanding the difference of languages between legal domain and open domain, understanding the rich argumentative texts in legal documents, and incorporating legal knowledge into existing natural language processing models.
  • Sentiment Analysis and Social Computing
    ZHANG Yawei, WU Liangqing, WANG Jingjing, LI Shoushan
    . 2022, 36(5): 145-152.
    Sentiment analysis is a popular research issue in the field of natural language processing, and multimodal sentiment analysis is the current challenge in this task. Existing studies are defected in capturing context information and combining information streams of different models. This paper proposes a novel multi-LSTMs Fusion Model Network (MLFN), which performs deep fusion between the three modalities of text, voice and image via the internal feature extraction layer for single-modal, and the inter-modal fusion layer for dual-modal and tri-modal. This hierarchical LSTM framework takes into account the information features inside the modal while capturing the interaction between the modals. Experimental results show that the proposed method can better integrate multi-modal information, and significantly improve the accuracy of multi-modal emotion recognition.
  • Survey
    Li Yunhan, Shi Yunmei, Li Ning, Tian Ying'ai
    . 2022, 36(9): 1-18,27.
    Text correction, an important research field in Natural Language Processing (NLP), is of great application value in fields such as news, publication, and text input . This paper provides a systematic overview of automatic error correction technology for Chinese texts. Errors in Chinese texts are divided into spelling errors, grammatic errors and semantic errors, and the methods of error correction for these three types are reviewed. Moreover, datasets and evaluation methods of automatic error correction for Chinese texts are summarized. In the end, prospects for the automatic error correction for Chinese texts are raised.
  • Language Resources Construction
    WANG Chengwen, DONG Qingxiu, SUI Zhifang, ZHAN Weidong,
    CHANG Baobao, WANG Haitao
    Journal of Chinese Information Processing. 2023, 37(2): 26-40.
    Pubic NLP datasets form the bedrock for NLP evaluation tasks, and the quality of such datasets has a fundamental impact on the development of evaluation tasks and the application of evaluation metrics. In this paper, we analyze and summarize eight types of problems relating to publicly available mainstream Natural Language Processing (NLP) datasets. Inspired by the quality assessment of testing in education community, we propose a series of evaluation metrics and evaluation methods combining computational and operational approaches, with the aim of providing a reference for the construction, selection and utilization of natural language processing datasets.
  • Survey
    ZHANG Xuan, LI Baobin
    Journal of Chinese Information Processing. 2022, 36(12): 1-15.
    Social bots in microblog platforms significantly impact information dissemination and public opinion stance. This paper reviews the recent researches on social bot account detection in microblogs, especially Twitter and Weibo. The popular methods for data acquisition and feature extraction are reviewed. Various bot detection algorithms are summarized and evaluated, including approaches based on statistical methods, classical machine learning methods, and deep learning methods. Finally, some suggestions for future research are anticipated.
  • Information Extraction and Text Mining
    YE Junyao, SU Jingyong, WANG Yaowei, XU Yong
    . 2022, 36(12): 133-138,148.
    Spaced repetition is a common mnemonic method in language learning. In order to decide proper review intervals for a desired memory effect, it is necessary to predict the learners’ long-term memory. This paper proposes a long-term memory prediction model for language learning via LSTM. We extract statistical features and sequence features from the memory behavior history of learners. The LSTM is used to learn the memory behavior sequence. The half-life regression model is applied to predict the probability of foreign language learners' recall of words. Upon the 9 billion pieces of real memory behavior data collected for evaluation, the sequence features are revealed more informative than statistical features. Compared with the state-of-the-art models, the error of the proposed LSTM-HLR model is significantly reduced by 50%.
  • Sentiment Analysis and Social Computing
    GE Xiaoyi, ZHANG Mingshu, WEI Bin, LIU Jia
    . 2022, 36(9): 129-138.
    The identification of rumors is of substantial significance research value. Current deep learning-based solution brings excellent results, but fails in capturing the relationship between emotion and semantics or providing emotional explanations. This paper proposes a dual emotion-aware method for interpretable rumor detection, aiming to provide a reasonable explanation from an emotional point of view via co-attention weights. Compared with contrast model, the accuracy is increased by 3.9%,3.3% and 4.4% on the public Twitter15, Twitter16, and Weibo20 datasets.
  • Information Extraction and Text Mining
    LI Wenxin, ZHANG Kunli, GUAN Tongfeng, ZHANG Huan,
    ZHU Tiantian, CHANG Baobao, CHEN Qingcai
    . 2022, 36(4): 66-72.
    The 6th China Conference on Health Information Processing (CHIP2020) organized six evaluation tasks in Chinese medical information processing, among which task 1 was named entity recognition task of Chinese medical text. The main purpose of this task is to automatically identify medical named entities in medical texts. A total of 253 teams signed up for the evaluation, and 37 teams finally submitted 80 sets of results. The micro-average F1 is used as the final evaluation criteria, and the highest value of the submitted results reached 68.35%.
  • Information Extraction and Text Mining
    GAN Zifa, ZAN Hongying, GUAN Tongfeng, LI Wenxin, ZHANG Huan, ZHU Tiantian, SUI Zhifang, CHEN Qingcai
    . 2022, 36(6): 101-108.
    The 6th China conference on Health Information Processing (CHIP 2020) organized six shared tasks in Chinese medical information processing. The second task was entity and relation extraction that automatically extracts the triples consist of entities and relations from Chinese medical texts. A total of 174 teams signed up for the task, and eventually 17 teams submitted 42 system runs. According to micro-average F1 which was the key evaluation criterion in the task, the top performance of the submitted results reaches 0.648 6.
  • Survey
    SHI Yuefeng, WANG Yi, ZHANG Yue
    . 2022, 36(7): 1-12,23.
    The goal of argument mining task is to automatically identify and extract argumentative structure from natural language. Understanding the argumentative structure and its reasoning contributes to obtaining reasons behind claims, and argument mining has gained great attention from researchers. Deep learning based methods have been generally applied for these tasks owing to their encoding capabilities for complex structures and representation capabilities for latent features. This paper systematically reviews the deep learning methods in argument mining areas, ncluding fundamental concepts, frameworks and datasets. It also introduces how deep learning based methods are applied in different argument mining tasks. Finally, this paper concludes weaknesses of current argument mining methods and anticipates the future research trends.
  • Language Resources Construction
    XIE Chenhui, HU Zhengsheng, YANG Lin'er, LIAO Tianxin, YANG Erhong
    Journal of Chinese Information Processing. 2023, 37(2): 15-25.
    Sentence pattern structure treebank is developed according to the theory of sentence-based grammar, which is of great significance to Chinese teaching. To further expand such treebank from Chinese as second language textbooks and Chinese textbooks to other domains, we propose a rule-based method to convert a phrase structure treebank named Penn Chinese Treebank (CTB) into a sentence pattern structure treebank so as to increase the size of the existing treebank. The experimental results show that our proposed method is effective.
  • Language Resources Construction
    MA Yazhong, ZHANG Congcong, XU Dapeng, MEI Yiduo,
    SUN Xinglei, ZHAO Zhibin, WANG Jingyu
    . 2022, 36(4): 48-56.
    We propose a methodology for constructing a city brain knowledge graph (CBKG) based on the resource description framework, IoT protocol and digital twin. Knowledge ontology and sub-ontology models for city brain are designed by coupling city elements and smart IoT standards. This knowledge graph model can integrate multi-source heterogeneous data, and therefore serving in the brain knowledge system for city-level intelligent operating system. We explore the event extraction under the city event ontology and designe a novel joint model to extract the event meta-theory. The results suggest that CBKG can support the intelligent management of the city in decision-makings. Future application of CBKG will couple with artificial intelligence, multi-sensor technologies, geographic information systems, and etc.
  • Information Extraction and Text Mining
    ZHANG Zhaowu, XU Bin, GAO Kening, WANG Tongqing, ZHANG Qiaoqiao
    . 2022, 36(7): 114-122.
    In the field of education, named entity recognition is widely used in Automatic machine questioning and Intelligent question answering. The traditional Chinese named entity recognition model needs to change the network structure to incorporate character and word information, which increases the complexity of the network structure. On the other hand, the data in the education field must be very accurate in the identification of entity boundaries. Traditional methods cannot incorporate location information, and the ability to identify entity boundaries is poor. In response to the above problems, this article uses an improved vector representation layer to integrate words, character, and location information in the vector representation layer, which can better define entity boundaries and improve the accuracy of entity recognition. BiGRU and CRF are used as models respectively. The sequence modeling layer and the annotation layer perform Chinese named entity recognition. This article conducted experiments on the Resume data set and the education data set (Edu), and the F1 values were 95.20% and 95.08%, respectively. The experimental results show that the method proposed in this paper improves the training speed of the model and the accuracy of entity recognition compared with the baseline model.
  • Ethnic Language Processing and Cross Language Processing
    LIU Rui, KANG Shiyin, GAO Guanglai, LI Jingdong, BAO Feilong
    . 2022, 36(7): 86-97.
    Aiming at real-time and high-fidelity Mongolian Text-to-Speech (TTS) generation, a FastSpeech2 based non-autoregressive Mongolian TTS system (short forMonTTS) is proposed. To improve the overall performance in terms of prosody naturalness and fidelity, MonTTS adopts three novel mechanisms: 1) Mongolian phoneme sequence is used to represent the Mongolian pronunciation; 2) phoneme-level variance adaptor is employed to learn the long-term prosody information; and 3) two duration aligners, i.e. Mongolian speech recognition and Mongolian autoregressive TTS based models, are used to provide the duration supervise signal. Besides, we build a large-scale Mongolian TTS corpus, named MonSpeech. The experimental results show that the MonTTS outperforms the state-of-the-art Tacotron-based Mongolian TTS and standard FastSpeech2 baseline systems significantly, with real-time rate (RTF) of 3.63× 10-3 and Mean Opinion Score (MOS) of 4.53(see https: //github.com/ttslr/MonTTS).
  • Sentiment Analysis and Social Computing
    ZHU Qinglin, LIANG Bin, XU Ruifeng, LIU Yuhan, CHEN Yi, MAO Ruibin
    . 2022, 36(8): 109-117.
    To address the entity-level sentiment analysis of financial texts, this paper builds a multi-million level corpus of sentiment analysis of financial domain entities and labels more than five thousand financial domain sentiment words as financial domain sentiment dictionary. We further propose an Attention-based Recurrent Network Combined with Financial Lexicon, called FinLexNet. FinLexNet model uses a LSTM to extract category-level information based on financial domain sentiment dictionary and another LSTM to extract semantic information at the word-level. In addition, in order to get more attention to the financial sentiment words, an attention mechanism based on the financial domain sentiment dictionary is proposed. Finally, experiments on the dataset we constructed shows that our model has achieved better performance than the baseline models.
  • The Key Technologies of Educational Cognition for Humanlike Intelligence
    WEI Si, GONG Jiefu, WANG Shijin, SONG Wei, SONG Ziyao
    . 2022, 36(4): 111-123.
    Automated essay scoring is a significant and challenging research topic, which has attracted the attention of scholars in the fields of artificial intelligence and education. Focuses on Chinese automated essay scoring, this paper proposes to exploit deep language analysis, including the application of spelling error corrector and grammar error corrector to analyze grammar level writing ability, the automatic rhetorical analysis and excellent expression recognition to reflect language expression ability, and the fine-grained quality analysis of essay to evaluate overall quality. We then propose an adaptive hybrid scoring model, combining linguistic features and deep neural networks. The experimental results on Chinese student essay datasets show that 1) incorporating deep language analysis features can effectively improve the performance of automated essay scoring; and 2) the grade and topic adaptive training strategy also improves the transferring and predication abilities.
  • Information Extraction and Text Mining
    XIE Binhong, LI Yu, ZHAO Hongyan
    . 2022, 36(5): 49-58.
    Open relation extraction (OpenRE) aims to extract relations for facts from open domain corpus. Most OpenRE methods are unsupervised methods to cluster semantically equivalent patterns into a relation cluster. To further improve the clustering performance, we proposed an unsupervised ensemble clustering framework(UEC), which combines unsupervised ensemble learning with iterative clustering algorithm based on information measurement to create high-quality labels. Such high-quality label can be used as supervised information to improve the feature learning and the clustering process to obtain better labels. Finally, through multiple iterative clustering, the relational types in the text can be effectively discovered. The experimental results on FewRel and NYT-FB datasets show that UEC is superior to other mainstream OpenRE models, with F1 score reaching 65.2% and 67.1%, respectively.
  • Natural Language Understanding and Generation
    MA Tianyu, QIN Jun, LIU Jing, TIE Jun, HOU Qi
    . 2022, 36(8): 127-134.
    The intention classification and the slot filling are two basic sub-tasks of spoken language understanding. A joint model of intention classification and slot filling based on BERT is proposed. Through an association network, the two tasks establish direct contact and share information. BERT is introduced into the model to enhance the semantic representation of word vectors, which effectively alleviates the issue of small training data. Experiments on ATIS and Snips data sets show that the proposed model can significantly improve the accuracy of intention classification and the F1 value of slot filling.
  • Survey
    CHEN Jinpeng, LI Haiyang, ZHANG Fan, LI Huan, WEI Kaimin
    Journal of Chinese Information Processing. 2023, 37(3): 1-17,26.
    In recent years, session-based recommendation methods have attracted extensive attention from academics. With the continuous development of deep learning techniques, different model structures have been used in session-based recommendation methods, such as Recurrent Neural Networks, Attention Mechanism, and Graph Neural Networks. This paper conducts a detailed analysis, classification, and comparison over these models, and expounds on the target problems and shortcomings of these methods. In particular, this paper first compares the session-based recommendation methods with the traditional recommendation methods, and expounds the main advantages and disadvantages of the session-based recommendation methods through investigation. Subsequently, this paper details how complex data and information are modeled in session-based recommendation models, as well as the problems that these models can solve. Finally, this paper discusses and ideatifies the challenges and potential research directions in session-based recommendations.
  • Survey
    DENG Hancheng, XIONG Deyi
    . 2022, 36(11): 20-37.
    Machine translation quality estimation refers to the estimation of the quality of the outputs by machine translation system without the human reference translations. It is of great value to the research and application of machine translation. In this survey, we firstly introduce the background and significance of machine translation quality estimation. Then we introduce in detail the specific task objectives and evaluation indicators of word-level QE, sentence-level QE, and document-level QE. We further summarize the development of QE methods to three main stage: methods based on feature engineering and machine learning, methods based on deep learning, and methods integrated with pre-training model. Representative research works in each stage are introduced, and the current research status and shortcomings are analyzed. Finally, we outline the outlook for the future research and development of QE.
  • Language Resources Construction
    ZHANG Kunli, REN Xiaohui, ZHUANG Lei, ZAN Hongying, ZHANG Weicong, SUI Zhifang
    . 2022, 36(10): 45-53.
    A medicine knowledge base with complete classification system and comprehensive drug information can provide basis and support for clinical decision-making and rational drug use. Based on multiple domestic medical resources as references and data sources, this paper establishes the knowledge description system and classification system of medicine base, standardizes classification of drugs and forms detailed knowledge description, and constructs a multi-source Chinese Medicine Knowledge Base (CMKB). The classification of CMKB includes 27 first-level categories and 119 secondary categories, and describes 14,141 drugs from multiple levels such as drug indications, dosage and administration. Furthermore, the BiLSTM-CRF and T-BiLSTM-CRF models are used to extract information of disease entities in unstructured descriptions, forming structured information extraction of drug attributes, and establishing the knowledge association between drug entities and automatically extracted disease entities. The constructed CMKB can be connected with the Chinese medical knowledge graph to expand drug information, and can provide the knowledge basis for intelligent diagnosis and medical question and answer.
  • Information Extraction and Text Mining
    ZHANG Hongkuan, SONG Hui, XU Bo, WANG Shuyi
    . 2022, 36(10): 97-106.
    Document-level event extraction aims at discovering the event with its arguments and their roles from texts. This paper proposes an end-to-end model for domain-specific document-level event extraction based on BERT. We introduce the embedding of event type and entity nodes to the subsequent layer for event argument and role identification, which represents the relation between events, arguments and roles to improve the accuracy of classifying multi-event arguments. With the title and the embedding of the quintuple of event, we realize the identification of principal and subordinate events, and element fusion between multiple events. Experimental results show that our model outperforms the baselines.
  • Best Paper: CCL2021
    SHU Lei, GUO Yiluan, WANG Huiping, ZHANG Xuetao , HU Renfen
    . 2022, 36(5): 21-30.
    Due to the dominant monosyllabic words, polysemy is a challenge for modern people to understand the ancient Chinese. Based on the linguistic knowledge in traditional dictionaries, this paper designs the principles of semantic division of polysemous words in ancient Chinese, and categorizes the knowledge of popular monosyllabic words in ancient Chinese. With these guidelines, the annotated corpus has accumulated up to 38 700 sentences with more than1 176 000 Chinese characters. Experiments show that the accuracy of BERT based word sense disambiguation model trained on the corpus achieves about 80%. Furthermore, this paper explores the application of the corpus built and the technique of word sense disambiguation in the study of language ontology and dictionary compilation via diachronic evolution analysis of word meaning and the induction of sense families.
  • Ethnic Language Processing and Cross Language Processing
    AN Bo, LONG Congjun
    . 2022, 36(12): 85-93.
    Tibetan text classification is a fundamental task in Tibetan natural language processing. The current mainstream text classification model is a large-scale pre-training model plus fine-tuning. However, Tibetan lacks open source large-scale text and pre-training language model, and cannot be verified on Tibetan text classification task. This paper crawls a large Tibetan text dataset to solve the above problems and trains a Tibetan pre-training language model (BERT-base-Tibetan) based on this dataset. Experimental results show that the pre-training language model can significantly improve the performance of Tibetan text classification (F1 value increases by 9.3% on average) and verify the value of the pre-training language model in Tibetan text classification tasks.
  • Language Analysis and Calculation
    LI Jiacheng, SHEN Jiayu, GONG Chen, LI Zhenghua, ZHANG Min
    . 2022, 36(4): 29-38.
    For Chinese Grammatical Error Correction (CGEC) task, although substitution errors account for the largest proportion of all the errors in the data set, no researcher has tried to incorporate phonological and visual similarity knowledge into the neural network-based GEC model. To tackle this problem, the article makes two attempts. First, this paper proposes a GEC model which incorporates with the confusion set knowledge based on the pointer network. Specifically, this model is Seq2Edit-based GEC model and use the pointer network to incorporate phonological and visual similarity knowledge. Second, during the training data pre-process stage, i.e., in the process of extracting edit sequences from wrong-correct sentence pairs, this paper proposes a confusion set guided edit distance algorithm to better extract substitution edit of phonological and visual similarity characters. The experimental results show that the two proposed methods can both improve the performance of the model and can provide complementary contributions; and the proposed model achieves the current state-of-the-art results in the NLPCC 2018 evaluation data set. Experimental analysis shows that compared with the baseline Seq2Edit GEC model, the overall performance gain of our proposed model is mostly contributed by correction of substitution errors.
  • Survey
    XUE Siyuan, ZHOU Jianshe, REN Fuji
    Journal of Chinese Information Processing. 2023, 37(2): 1-14.
    This paper summarizes the researches on automated essay scoring, including the development of automated essay scoring system. It also examines the tasks, public datasets and popular metrics in of automated essay scoring. The main techniques and models for automated essay scoring are reviewed, as well as the challenges in terms of both native Chinese speakers and non-native Chinese speakers.; Finally, the prospects for future automated essay scoring is discussed.
  • Information Extraction and Text Mining
    HUANG Youwen, WEI Guoqing, HU Yanfang
    . 2022, 36(4): 81-89.
    To balance the classification accuracy and computation cost of text classification model, this paper proposes a text classification model DistillBIGRU based on knowledge distillation. We construct the MPNetGCN model as the teacher model, select the bidectional gated recurent unit network as the student model, and obtain the final model DistillBIGRU through knowledge distillation. On multiple data sets, the average classification accuracy of the teacher model MPNetGCN is 1.3% higher than that of BERTGCN. And the DistillBIGRU achieves comparable classification effect to the BERT-Base mode with roughly 1/9 parameters of the latter.
  • Survey
    HUANG Zhenya, LIU Qi, CHEN Enhong, LIN Xin, HE Liyang, LIU Jiayu, WANG Shijin
    . 2022, 36(10): 1-16.
    One of the important research directions on the integration of artificial intelligence into pedagogy is analyzing the meanings of educational questions and simulating how humans solve problems. In recent years, a large number of educational question resources have been collected, which provides the data support of the related research. Leveraging the big data analysis and natural language processing related techniques, researchers propose many specific text analysis methods for educational questions, which are of great significance to explore the cognitive abilities of how human master knowledge. In this paper, we summarize several representative topics, including question quality analysis, machine reading comprehension, math problem solving, and automated essay scoring. Moreover, we introduce the relevant public datasets and open-source toolkits. Finally, we conclude by anticipating several future directions.
  • Sentiment Analysis and Social Computing
    LEI Pengbin, QIN Bin, WANG Zhili, WU Yufan, LIANG Siyi, CHEN Yu
    . 2022, 36(8): 101-108.
    This paper proposes a new method for text sentiment classification based on the pre-training model. The BiLSTM network is applied to dynamically adjust the output weight of the Transformer of each layer of the pre-training model, and the layered text representation vectors are filtered using features such as BiLSTM and BiGRU. By using the model, we achieved third place in the Netizen Emotion Recognition Track during the epidemic of CCF 2020 Science and Technology for Epidemic·Big Data Charity Challenge. The F1 value of the final test set is 0.745 37, which is 0.000 1 less than the first-place model with 67% less parameters.
  • Survey
    WANG Shaonan , ZHANG Jiajun, ZONG Chengqing
    . 2022, 36(4): 1-11.
    The language understanding processes in human brain is very complicated, involving multiple brain networks and processing mechanisms. Most previous work used strictly controlled experimental designs to investigate specific language phenomena. As a result, the research conclusions tend to be fragmented, hardly forming a picture about the brain language understanding. Recently, the emergence of deep learning has triggered technological changes in the field of language computation, and computational language models have reached or even surpassed human levels in multiple tasks. This brings the possibility of conducting global and highly ecologically valid language comprehension experiments, which will promote the develoyment of computational language methods in language cognition experiments. This article summarizes the related work of language cognition experiments using computational language methods, and anticipates the future development trends.
  • Sentiment Analysis and Social Computing
    WANG Jinghao, LIU Zhen, LIU Tingting , WANG Yuanyi, CHAI Yanjie
    . 2022, 36(10): 145-154.
    Existing methods for sentiment analysis in social media usually deal with single modal data, without capturing the relationship between multimodal information. This paper propose to treat the hierarchical structure relations between texts and images in social media as complementarity. This paper designs a multi-level feature fusion attention network to capture both the ‘images-text’ and the ‘text-images’ relations to perceive the user’s sentiments in social media. Experimental results on Yelp and MultiZOL datasets show that this method can effectively improve the sentiment classification accuracy for multimodal data.
  • Survey
    FAN Zipeng, ZHANG Peng, GAO Hui
    Journal of Chinese Information Processing. 2023, 37(1): 1-15.
    Quantum natural language processing, as a cross-disciplinary field of quantum mechanics and natural language processing, has gradually attracted the attention of the community, and a large number of quantum natural language processing models and algorithms have been proposed. As a review of these work, this paper briefly summarizes the problems of current classical algorithms and the two research ideas of combinng quantum mechanics with natural language processing. It also explains the role of quantum mechanics in natural language processing from three aspects: semantic space, semantic modeling and semantic interaction. By analyzing the differences in storage resources and computation complexity between the quantum computing platform and the classical computing platform, it reveals the necessity of deploying quantum natural language processing algorithms on the quantum computing platform. Finally, the current quantum natural language processing algorithms are enumerated, and the research direction in this field are outlooked for further research.
  • The Key Technologies of Educational Cognition for Humanlike Intelligence
    TAN Hongye, GUO Shaoru, CHENG Xin, WANG Suge, LI Ru, ZHANG Hu,
    YANG Zhizhuo, CHEN Qian, QIAN Yili, WANG Yuanlong, GUAN Yong, LV Guoying
    . 2022, 36(4): 166-174.
    Machine Reading Comprehension (MRC) is a critical task in many real-world applications, which requires machines to understand a text passage and answer relevant questions. This paper studied the key technologies of textual semantic representation, candidate sentence extraction and language appreciation, and built the system for answering multiple choice questions and free-description questions. We have conducted some experiments on the Gaokao tests, finding that the system can achieve a certain degree of accuracy for both questions. In the future, we will explore to utilize more advanced techniques such as semantic representation, unified knowledge representation and aggregation, and transfer learning to improve the MRC system in complex reasoning, inductive analyzing and language appreciating.
  • Sentiment Analysis and Social Computing
    YANG Chunxia, SONG Jinjian, YAO Sicheng
    . 2022, 36(5): 125-132.
    For aspect-based sentiment analysis, existing rule-based dependency tree pruning methods have the problem of deleting some useful information. In addition, how to use the graph convolutional network to obtain the rich global information in the graph structure is also an important problem at present. For the first problem, we use the multi-head attention mechanism to automatically learn how to selectively focus on the structural information that is useful for the classification task, and transform the original dependency tree into a fully connected edge weighted graph.To solve the second problem, we paper introduces dense connections into the graph convolutional network, so that the graph convolutional network can capture rich local and global information. The experimental results on the three public datasets show that the accuracy and F1 of the proposed model are both improved compared with the baseline model.
  • Sentiment Analysis and Social Computing
    FENG Haojia, LI Yang, WANG Suge, FU Yujie, MU Yongli
    . 2022, 36(5): 153-162.
    Emotion-cause pair extraction is to extract both emotion clause and cause clause at the same time. For this task, the existing method of a single graph attention network does not consider emphasize the semantic representation of emotion words in the encoding layer. This paper proposes a Sen-BiGAT-Inter method using sentiment lexicon, graph network and multi-attention. The proposed method uses the sentiment lexicon to merge this clause with the emotion words in the clause, and uses the pre-training model BERT (Bidirectional Encoder Representation from Transformers) to obtain the clause representation. Then, we build two graph attention networks to learn the representation of emotion clause and cause clause, respectively, and then obtain the representation of candidate emotion-cause pair. On this basis, we get the emotion-cause pair with causality by using multi-head attention to learn the global information of candidate sentence pairs, and combing the relative position information to get the final representation of pairs. The experimental results on Chinese emotion-cause pair extraction dataset show the proposed model improves the F1 value by about 1.95 compared with the current optimal results.
  • Sentiment Analysis and Social Computing
    DING Jian, YANG Liang, LIN Hongfei, WANG Jian
    . 2022, 36(5): 112-124.
    In recent years, sentiment analysis has been extended to multi-modal data, and the dynamic instead of static interaction of the intra modality data is worth exploring. This paper proposes a dynamic fusion method for heterogeneous multi-modal emotional stream data to completely capture the interaction between modalities. And using multi-task learning strategy, the heterogeneous dynamic fusion network is combined with a single modality self-supervised learning network to obtain the consistency and difference characteristics of the modality. Experiments on the CMU-MOSI and CMU-MOSEI indicate the advantage of the proposed method over mainstream models, as well as its interpretability.