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  • 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
    CHEN Yulong, FU Qiankun, ZHANG Yue
    . 2021, 35(3): 1-23.
    In recent years, neural networks have gradually overtaken classical machine learning models and become the de facto paradigm for natural language processing tasks. Most typical neural networks are capable of dealing with data in Euclidean space. Due to the linguistic nature, however, the language information such as discourse and syntactic information is of graph structures. Therefore, there has been an increasing number of researches that use graph neural networks to explore structures in natural languages. This paper systematically introduces applications of graph neural networks in natural language processing areas. It first discusses the fundamental concepts and introduces three main categories of graph neural networks, namely graph recurrent neural network, graph convolutional network, and graph attention network. Then this paper introduces methods to construct proper graph structures according to different tasks, and to apply graph neural networks to embed those structures. This paper suggests that compared with focusing on novel structures, exploring how to use the key information in specific tasks to create corresponding graphs is more universal and is of more academic value, which can be a promising future research direction.
  • Survey
    CAO Qi, SHEN Huawei, GAO Jinhua, CHENG Xueqi
    . 2021, 35(2): 1-18,32.
    Popularity prediction over online social networks plays an important role in various applications, e.g., recommendation, advertising, and information retrieval. Recently, the rapid development of deep learning and the availability of information diffusion data provide a solid foundation for deep learning based popularity prediction research. Existing surveys of popularity prediction mainly focus on traditional popularity prediction methods. To systematically summarize the deep learning based popularity prediction methods, this paper reviews existing popularity prediction methods based on deep learning, categorizes the recent deep learning based popularity prediction research into deep representation based and deep fusion based methods, and discusses the future researches.
  • Survey
    DU Xiaohu, WU Hongming, YI Zibo, LI Shasha, MA Jun, YU Jie
    . 2021, 35(8): 1-15.
    Adversarial attack and defense is a popular research issue in recent years. Attackers use small modifications to generate adversarial examples to cause prediction errors from the deep neural network. The generated adversarial examples can reveal the vulnerability of the neural network, which can be repaired to improve the security and robustness of the model. This paper gives a more detailed and comprehensive introduction to the current mainstream adversarial text example attack and defense methods, the data set together with the target neural network of the mainstream attack. We also compare the differences between different attack methods in this paper. Finally, the challenges of the adversarial text examples and the prospect of future research are summarized.
  • Information Extraction and Text Mining
    SUN Yuejun, LIU Zhiqiang, YANG Zhihao, LIN Hongfei
    . 2021, 35(4): 75-82.
    The diversity of clinical terms in electronic medical records hinder the analysis and utilization of medical data. To address this issue, this paper proposes a method of clinical term normalization based on BERT. The method uses Jaccard similarity to select the candidate words from the standard term set, and matches the original words and candidate words based on BERT model to obtain standardized results. Evaluated on the dataset of CHIP2019 clinical term normalization evaluation task, the method obtains 90.04% accuracy.
  • Information Extraction and Text Mining
    WANG Bingqian, SU Shaoxun, LIANG Tianxin
    . 2021, 35(7): 81-88.
    Event extraction (EE) refers to the technology of extracting events from natural language texts and identifying event types and event elements. This paper proposes an end-to-end multi-label pointer network for event extraction, in which the event detection task is integrated into the event element recognition task to extract event elements and event types at the same time. This method avoids the problem of wrong cascade and task separation in traditional pipeline methods, and alleviates the problem of role overlapping and element overlapping in event extraction. The proposed method achieves 85.9% F1 score on the test set in 2020 Language and Intelligence Challenge Event Extraction task.
  • 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
    DENG Yiyi, WU Changxing, WEI Yongfeng, WAN Zhongbao, HUANG Zhaohua
    . 2021, 35(9): 30-45.
    Named entity recognition (NER), as one of the basic tasks in natural language processing, aims to identify the required entities and their types in unstructured text. In recent years, various named entity recognition methods based on deep learning have achieved much better performance than that of traditional methods based on manual features. This paper summarizes recent named entity recognition methods from the following three aspects: 1) A general framework is introduced, which consists of an input layer, an encoding layer and a decoding layer. 2) After analyzing the characteristics of Chinese named entity recognition, this paper introduces Chinese NER models which incorporate both character-level and word-level information. 3) The methods for low-resource named entity recognition are described, including cross-lingual transfer methods, cross-domain transfer methods, cross-task transfer methods, and methods incorporating automatically labeled data. Finally, the conclusions and possible research directions are given.
  • 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
    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.
  • Information Extraction and Text Mining
    WU Hao, PAN Shanliang
    . 2022, 36(1): 92-103.
    The current detection method of illegal comments mainly relies on sensitive words screening, incapable of effectively identifying malicious comments without vulgar language. In this paper, a data set of Chinese illegal comments is established by crawler and manual annotation. On the basis of BERT, RCNN combined with attention mechanism is used to further extract the context features of comments, and multi-task joint training is adopted to improve the classification accuracy and generalization ability of the model. The model is independent to sensitive thesaurus. Experimental results show that the proposed model can better understand the semantic information than the traditional model, achieving aprecision of 94.24%, which is 8.42% higher than traditional TextRNN and 6.92% higher than TextRNN combined with attention mechanism.
  • Survey
    YUE Zengying, YE Xia, LIU Ruiheng
    . 2021, 35(9): 15-29.
    Pre-training technology has stepped into the center stage of natural language processing, especially with the emergence of ELMo, GTP, BERT, XLNet, T5, and GTP-3 in the last two years. In this paper, we analyze and classify the existing pre-training technologies from four aspects: language model, feature extractor, contextual representation, and word representation. We discuss the main issues and development trends of pre-training technologies in current natural language processing.
  • Sentiment Analysis and Social Computing
    DU Peng, LU Yiqing, Han Changfeng
    . 2021, 35(2): 125-132.
    This paper investigates the positive and negative emotion recognition of commodity reviews, and proposes a neural network model based on the Transformer model, which combines the multi-head self-attention layer and the convolution layer. The multi-head self-attention layer enriches the correlation between words, and the convolution operation is used for further feature extraction and fusion. Compared with Bidirectional Long Short-Term Memory Networks (Bi-LSTM), Bi-LSTM with attention and Text Convolutional Neural Networks (TEXTCNN), experiments show that the proposed model improves the top accuracy of emotion classification tasks by 4.12%, 1.47% and 1.36%, respectively.
  • Survey
    YANG Fan, RAO Yuan, DING Yi, HE Wangbo, DING Zifang
    . 2021, 35(10): 1-20.
    Recently, the artificial intelligence-based dialogue system has been widely applied in, human-computer interaction, intelligent assistant, smart customer service, Q&A consulting, and so on. This paper proposes a definition about the task-oriented dialogue system, which is to satisfy the user’s requirements and certain tasks with the least response turns in dialogue between human and machine. Furthermore, three critical technical problems and challenges are summarized: the user’s intent detection in the complex context, the limitation annotated data, and the personalized response under the multi-modal situation. The research progress in these three challenges are discussed in the paper. Finally, we outline the research directions in the future and the key issues in the next generation of task-oriented dialogue system.
  • Survey
    SUN Yi, QIU Hangping, ZHENG Yu, ZHANG Chaoran, HAO Chao
    . 2021, 35(7): 10-29.
    Introducing knowledge into data-driven artificial intelligence models is an important way to realize human-machine hybrid intelligence. The current pre-trained language models represented by BERT have achieved remarkable success in the field of natural language processing. However, the pre-trained language models are trained on large scale unstructured corpus data, and it is necessary to introduce external knowledge to alleviate its defects in determinacy and interpretability to some extent. In this paper, the characteristics and limitations of two kinds of pre-trained language models, pre-trained word embeddings and pre-trained context encoders, are analyzed. The related concepts of knowledge enhancement are explained. Four types of knowledge enhancement methods of pre-trained word embeddings are summarized and analyzed, which are pre-trained word embeddings retrofitting, hierarchizing the process of encoding and decoding, attention mechanism optimization and knowledge memory introduction. The knowledge enhancement methods of pre-training context encoders are described from two perspectives: 1) task-specific and task-agnostic; 2) explicit knowledge and implicit knowledge. Through the summary and analysis of the knowledge enhancement methods of the pre-trained language model, the basic pattern and algorithm are provided for the human-machine hybrid artificial intelligence.
  • Survey
    WU Yunfang, ZHANG Yangsen
    . 2021, 35(7): 1-9.
    Question generation (QG) aims to automatically generate fluent and semantically related questions for a given text. QG can be applied to generate questions for reading comprehension tests in the education field, and to enhance question answering and dialog systems. This paper presents a comprehensive survey of related researches on QG. We first describe the significance of QG and its applications, especially in the education field. Then we outline the traditional rule-based methods on QG, and make a detailed description on the neural network based models from different views. We also introduce the evaluation metrics of generated questions. Finally, we discuss the limitations of previous studies and suggest future works.
  • Survey
    WANG Wanzhen, RAO Yuan, WU Lianwei, LI Xue
    . 2021, 35(9): 1-14.
    Artificial intelligence is increasingly emphasized in judicial practices in recent years. Based on the literature on intelligent models for assisting judicial cases, this paper suggests the following six challenges in legal judgement decision prediction: multi-feature crime prediction, multi-label crime prediction, multiple sub-task processing, unbalanced data issue, the interpretability of decision prediction and the adaption of existing algorithms to different types of cases. Meanwhile, the paper provides theoretical discussion, technical analysis, technical challenges as well as trend analysis for these problems. The datasets used in this field and the corresponding evaluation metrics are also summarized.
  • Information Extraction and Text Mining
    LI Ren, LI Tong, YANG Jianxi, MO Tianjin, JIANG Shixin, LI Dong
    . 2021, 35(4): 83-91.
    The information extraction for bridge inspection reports is a less addressed issue, which contain a large amount of key business information such as structural component parameters and inspection description. Clarifying the task of named entity recognition in this field, this paper also reveals the characteristics of the entities to be identified, such as location name or route name nesting, character ambiguity, context location correlation and direction sensitivity. A bridge inspection named entity recognition approach is then proposed based on Transformer-BiLSTM-CRF. First, the Transformer encoder is used to model the long-distance position-dependent features of text sequences, and the BiLSTM network is adopted to further capture the direction-sensitive features. Finally, the labeled sequence prediction is implemented via the CRF model. The experimental results show that, compared with the mainstream named entity recognition models, the proposed model achieves better performance.
  • Survey
    QIN Libo, LI Zhouyang, LOU Jieming, YU Qiying, CHE Wanxiang
    . 2022, 36(1): 1-11,20.
    Natural Language Generation in a task-oriented dialogue system (ToDNLG) aims to generate natural language responses given the corresponding dialogue acts, which has attracted increasing research interest. With the development of deep neural networks and pre-trained language models, great success has been witnessed in the research of ToDNLG field. We present a comprehensive survey of the research field, including: (1) a systematical review on the development of NLG in the past decade, covering the traditional methods and deep learning-based methods; (2) new frontiers in emerging areas of complex ToDNLG as well as the corresponding challenges; (3) rich open-source resources, including the related papers, baseline codes and the leaderboards on a public website. We hope the survey can promote future research in ToDNLG.
  • Information Extraction and Text Mining
    HUANG Yuanhang, JIAO Xiaokang, TANG Buzhou, CHEN Qingcai, YAN Jun,
    . 2021, 35(3): 94-99.
    The 5th China Conference on Health Information Processing held a shared task including three tracks on Chinese clinical medical information processing. The first track is normalization of Chinese clinical terminology that assigns standard terminologies to surgical entities extracted from Chinese electronic medical records. All surgical entities in the Track1 dataset were collected from real medical data and annotated with standard surgical terminologies of "ICD9-2017 Clinical Edition". A total of 56 teams signed up for the track, and eventually 20 teams submitted 47 system runs. Accuracy is used to measure the performances of all systems, and the highest accuracy of all submitted system runs reached 0.9483.
  • Sentiment Analysis and Social Computing
    LU Hengyang, FAN Chenyou, WU Xiaojun
    . 2022, 36(1): 135-144,172.
    The COVID-19 rumors published and spread on the online social media have a serious impact on people's livelihood, economy, and social stability. Most existing researches for rumor detection usually assumed that the happened events for modeling and predictions already have enough labeled data. These studies have severe limitations on detecting emergent events such as the COVID-19 which has very few training instances. This article focuses on the problem of few-shot rumor detection, aiming to detect rumors of emergent events with only very few labeled instances. Taking the COVID-19 rumors from Sina Weibo as the target, we construct a Sina Weibo COVID-19 rumor dataset for few-shot rumor detection, and propose a deep neural network based few-shot rumor detection model with meta learning. In the few-shot machine learning scenarios, the experimental results of the proposed model on the COVID-19 rumor dataset and the PHEME public dataset have been significantly improved.
  • 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.
  • Information Extraction and Text Mining
    BAI Lu, ZHOU Ziya, LI Binyang, LIU Yuhan, SHAO Zhixuan, WU Huarui
    . 2021, 35(4): 66-74,82.
    The Eventic Graph(EG) is a directed graph that describes the logic relationship between events, such as continuation and causality, etc. In contrast to the current researches focusing on the event extraction on the open domain. this paper aims at the construction of the eventic graph for the political field. We establish an annotation scheme for political events and construct an event corpus for the political field. Moreover, we present a character embedding based neural network by integrating the attention mechanism and a BERT+BiLSTM framework for political event extraction as the pipeline and the joint model, respectively. Experiments on our constructed corpus show that the porposed method could achieve significant improvement on event classification and argument classification in terms of F1-score compared with previous neural network based methods.
  • Information Extraction and Text Mining
    WANG Zi, WANG Yulong, LIU Tongcun, LI Wei, LIAO Jianxin
    . 2022, 36(3): 82-90.
    Quote attribution in novels aims at determining who says a quote in a given novel. This task is important for assigning appropriate voices to the given quotes when producing vocal novels. In order to fully express the difference of quote types and the semantic features in the context, this paper proposes a Rule-BertAtten method for quote attribution in Chinese novels. The quotes are divided into four categories: the quote with explicit speaker, the quote with pronoun speaker with one-match gender, the quote with pronoun speaker with multi-match gender and the quote with implicit speaker. According to these categories, a rule-based method and the BERT word embedding methods with Attention are applied respectively. The experiment result shows that our method is more accurate than previous approaches.
  • 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.
  • 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
    CHEN Xin, ZHOU Qiang
    . 2021, 35(11): 1-12.
    As a branch of the dialogue system, open domain dialogue has a good prospect in application. Different from task-based dialogue, it has strong randomness and uncertainty. This paper reviews the researches on open domain dialogue from the perspective of reply method, focusing on the application and improvement of sequence-to-sequence model in dialogue generation scenarios. The researches exhibit a clear clue from single-round dialogue to multi-round dialogue, and we further reveal that the sequence to sequence generation model has some problems that the characteristics of the model implementation and the application scenarios do not exactly match in the multi-round dialogue generation. Finally, we explore the possible improvements for the generation of multi-round dialogues from introducing external knowledge, introducing rewriting mechanism and introducing agent mechanism.
  • Information Extraction and Text Mining
    DING Zeyuan, YANG Zhihao, LUO Ling, WANG Lei, ZHANG Yin, LIN Hongfei, WANG Jian
    . 2021, 35(5): 70-76.
    In the field of biomedical text mining, biomedical named entity recognition and relations extraction are of great significance. This paper builds a Chinese biomedical entity relation extraction system based on deep learning technology. Firstly, Chinese biomedical entity relation corpus is construction from the publicly available English biomedical annotated corpora via translation and manual annotation. Then this paper applies the ELMo (Embedding from Language Model) trained in Chinese biomedical text to the Bi-directional LSTM (BiLSTM) combined conditional random fields (CRF) model for Chinese entity recognition. Finally, the relation between entities is extracted using BiLSTM combined with the Attention mechanism. The experimental results show that the system can accurately extract biomedical entities and inter-entity relation from Chinese text.
  • Information Extraction and Text Mining
    LU Xiaolei, NI Bin
    . 2021, 35(11): 70-79.
    An accurate automatic patent classifier is crucial to patent inventors and patent examiners, and is of potential application in the fields of intellectual property protection, patent management, and patent information retrieval. This paper presents BERT-CNN, a hierarchical patent classifier based on pre-trained language model, which is trained by the national patent application documents collected from the State Information Center, China. The experimental results show that the proposed method achieves 84.3% accuracy, much better than the two compared baseline methods, Convolutional Neural Networks and Recurrent Neural Networks. In addition, this article also discusses the differences between hierarchical and flat strategies in multi-layer text classification.
  • Information Extraction and Text Mining
    ZHANG Shiqi, MA Jin, ZHOU Xiabing, JIA Hao, CHEN Wenliang, ZHANG Min
    . 2022, 36(1): 56-64.
    Attribute extraction is a key step of constructing a knowledge graph. In this paper, the task of attribute extraction is converted into a sequence labeling problem. Due to a lack of labeling data in product attribute extraction, we use the distant supervision to automatically label multiple source texts related to e-commerce. In order to accurately evaluate the performance of the system, we construct a manually annotated test set, and finally obtain a new data set for product attribute extraction in multi-domains. Based on the newly constructed data set, we carried out intra-domain and cross-domain attribute extraction for a variety of pre-trained language models. The experimental results show that the pre-trained language models can better improve the extraction performance. Among them, ELECTRA performs the best in attribute extraction in in-domain experiments, and BERT performs the best in cross-domain experiments. we also find that adding a small amount of target domain annotation data can effectively improve the performance cross-domain attribute extraction and enhance the domain adaptability of the model.
  • Information Extraction and Text Mining
    LI Chunnan, WANG Lei, SUN Yuanyuan, LIN Hongfei
    . 2021, 35(8): 73-81.
    Legal named entity recognition(LNER)is a fundamentaltask for the field of smart judiciary.This paper presents a new definition of LNER and a corpus of letters of proposal for prosecution named LegalCorpus. This paper proposes novel BERT based NER model for legal texts, named BERT-ON-LSTM-CRF (Bidirectional Encoder Representations from Transformers-Ordered Neuron-Long Short Term Memory Networks-Conditional Random Field). The proposed model utilizes BERT model to dynamically obtain the semantic vectors according to the context of words. Then the ONLSTM is adopted to extract the text features by modeling the input sequence and hierarchy. Finally, the text features are decoded by CRF to obtain the optimal tag sequence. Experiments show that the proposed model can achieve a F1-value of 86.09%, with 7.8% increased than the best baseline Lattice-LSTM.
  • Survey
    ZHANG Lu, LI Zhuohuan, YIN Xucheng, JIN Zanxia
    . 2021, 35(3): 24-42.
    In recent years, the data-driven end-to-end chatbots have developed rapidly and attracted widespread attentions from both industrial and academic circle. This paper reviews existing automatic evaluation methods for generative model based chatbots. Firstly, the research background and the state-of-the-art of the automatic evaluation methods are introduced. Then the automatic evaluation methods for the basic ability of chatbots to generate reasonable responses are presented, revealing the advantages and disadvantages of each type of methods. The automatic evaluation methods for the expansion ability of chatbots are also introduced, involving the generation of various responses, the dialogue with specific personality or emotion, and the conversation topic in depth and breadth. Besides, the evaluation methods to evaluate the comprehensive ability of chatbots are also addressed together the development direction. After reviewing the method to evaluate automatic chatbots evaluation metrics, this paper finally discusses the challenges and future development trends to develop automatic evaluation methods.
  • Information Extraction and Text Mining
    WAN Ying, SUN Lianying, ZHAO Ping, WANG Jinfeng, TU Shuai
    . 2021, 35(3): 69-77.
    Relation classification is an important semantic processing task in the field of natural language processing. With the development of machine learning technology, the pre-trained model BERT has achieved excellent results in many natural language processing tasks. This paper proposes a relation classification method (EC_BERT) based on entity and entity context information enhanced BERT. This method uses BERT to obtain the sentence feature representation vector, and then combines two target entities and entity context statement information. In addition, the article also carried out experiments on RoBERTa model and DistiBERT model which are the improved model of BERT. The results on the SemEval-2010 task 8 dataset and the KBP-37 dataset show that the Bert based method performs best, achieving 89.69% and 65.92% of the Macro-F1, respectively.
  • Sentiment Analysis and Social Computing
    CHENG Yan, SUN Huan, CHEN Haomai, LI Meng, CAI Yingying, CAI Zhuang
    . 2021, 35(5): 118-129.
    Text sentiment analysis is an important branch in the field of natural language processing. This paper proposes a text sentiment analysis capsule model that combines convolutional neural networks and bidirectional GRU networks. Firstly, the multi-head attention is used to learn the dependency between words and capture the emotional words in the text. Then, the convolutional neural network and bidirectional GRU network are used to extract emotional features of different granularities in the text. After the feature fusion, the global average pooling is used to get the instance feature representation of the text, and the attention mechanism is combined to generate feature vectors for each emotion category to construct an emotion capsule. Finally, the emotion category of the text is judged by the capsule attributes. Tested on the MR, IMDB, SST-5 and Tan Songbo hotel review datasets, the proposed model achieves better classification effect than other baseline models.
  • Survey
    DONG Qingxiu, SUI Zhifang, ZHAN Weidong, CHANG Baobao
    . 2021, 35(6): 1-15.
    Evaluation in natural language processing drives and promotes research on models and methods. In recent years, new evaluation data sets and evaluation tasks have been continuously proposed. At the same time, a series of problems exposed by such evaluations seems to restrict the progress of natural language processing technology. Starting from the concept, composition, development and significance of natural language Processing evaluation, this article classifies and summarizes the tasks and characteristics of mainstream natural language Processing evaluation, and then reveals the problems and their possible causes. In parallel to the human language ability evaluation standard, this paper puts forward the concept of human-like machine language ability evaluation, and proposes a series of basic principles and implementation ideas for human-like machine language ability evaluation from three aspects: reliability, difficulty and validity.
  • Information Extraction and Text Mining
    WU Ting, KONG Fang
    . 2021, 35(10): 73-80.
    As a subtask of information extraction, relation extraction aims to extract the structured knowledge from unstructured text, which is very important for the downstream tasks such as automatic question answering and knowledge graph construction. Focused on document-level relation extraction, this paper proposed a graph attention convolution model to deal with long-distance dependence issue. The model uses a multi-head attention mechanism to construct a dynamic topological graph for coreference, syntax and other information. Then it uses the graph convolution model and dynamic graph to capture global and local dependency information between entities. Experiments on the DocRED corpus and the self-expanding ACE 2005 corpus comfirm improvements on F1 values by 2.03 and 3.93, respectively.
  • Information Retrieval and Question Answering
    WU Kun, ZHOU Xiabing, LI Zhenghua, LIANG Xingwei, CHEN Wenliang
    . 2021, 35(9): 113-122.
    Path selection, as a key step in the Knowledge Base Question Answering (KBQA) task, relies on the the semantic similarity between a question and candidate paths. To deal with massive unseen relations in the test set, a method based on dynamic sampling of negative examples is proposed to enrich the relations in the training set. In the prediction phase, two path pruning methods, i.e., the classification method and the beam search method, are compared to tackle the explosion of candidate paths. On the CCKS 2019-CKBQA evaluation data set containing simple and complex problems, the proposed method achieves an average F1 value of 0.694 for the single-model system, and 0.731 for the ensemble system.
  • Knowledge Representation and Acquisition
    LIU Yangguang, QI Fanchao, LIU Zhiyuan, SUN Maosong
    . 2021, 35(4): 23-34.
    Sememes are defined as the minimum semantic units of human languages that cannot be subdivided. The meaning of a word can be defined by a combination of multiple sememes. Sememe-based linguistic knowledge bases(KBs), in which words are manually annotated with sememes, have been successfully constructed and utilized in many NLP tasks. However, the manual annotation of sememes is time-consuming and labor-intensive, and person bias will be inevitably introduced, which prejudices annotation consistency and accuracy. In this paper, we for the first time propose a method to conduct automatic consistency check of sememe annotations in HowNet. Experimental results demonstrate the effectiveness of out method, which show that our method can be applied to the annotation consistency check and extension of HowNet.
  • 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.