As Natural Language Processing(NLP)continues to advance,driven by the emergence of sophisticated large language models such as ChatGPT,there has been a notable growth in research activity.This rapid uptake reflects in...As Natural Language Processing(NLP)continues to advance,driven by the emergence of sophisticated large language models such as ChatGPT,there has been a notable growth in research activity.This rapid uptake reflects increasing interest in the field and induces critical inquiries into ChatGPT’s applicability in the NLP domain.This review paper systematically investigates the role of ChatGPT in diverse NLP tasks,including information extraction,Name Entity Recognition(NER),event extraction,relation extraction,Part of Speech(PoS)tagging,text classification,sentiment analysis,emotion recognition and text annotation.The novelty of this work lies in its comprehensive analysis of the existing literature,addressing a critical gap in understanding ChatGPT’s adaptability,limitations,and optimal application.In this paper,we employed a systematic stepwise approach following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)framework to direct our search process and seek relevant studies.Our review reveals ChatGPT’s significant potential in enhancing various NLP tasks.Its adaptability in information extraction tasks,sentiment analysis,and text classification showcases its ability to comprehend diverse contexts and extract meaningful details.Additionally,ChatGPT’s flexibility in annotation tasks reducesmanual efforts and accelerates the annotation process,making it a valuable asset in NLP development and research.Furthermore,GPT-4 and prompt engineering emerge as a complementary mechanism,empowering users to guide the model and enhance overall accuracy.Despite its promising potential,challenges persist.The performance of ChatGP Tneeds tobe testedusingmore extensivedatasets anddiversedata structures.Subsequently,its limitations in handling domain-specific language and the need for fine-tuning in specific applications highlight the importance of further investigations to address these issues.展开更多
Automatic web image annotation is a practical and effective way for both web image retrieval and image understanding. However, current annotation techniques make no further investigation of the statement-level syntact...Automatic web image annotation is a practical and effective way for both web image retrieval and image understanding. However, current annotation techniques make no further investigation of the statement-level syntactic correlation among the annotated words, therefore making it very difficult to render natural language interpretation for images such as "pandas eat bamboo". In this paper, we propose an approach to interpret image semantics through mining the visible and textual information hidden in images. This approach mainly consists of two parts: first the annotated words of target images are ranked according to two factors, namely the visual correlation and the pairwise co-occurrence; then the statement-level syntactic correlation among annotated words is explored and natural language interpretation for the target image is obtained. Experiments conducted on real-world web images show the effectiveness of the proposed approach.展开更多
为有效地解决当前相关标准和标准数据匮乏的问题,通过分析中文文本中地理空间关系描述的语言特点,提出中文文本的地理空间关系标注体系,并以GATE(General Architecture for Text Engineering)为标注工具,以《中国大百科全书中国地理》...为有效地解决当前相关标准和标准数据匮乏的问题,通过分析中文文本中地理空间关系描述的语言特点,提出中文文本的地理空间关系标注体系,并以GATE(General Architecture for Text Engineering)为标注工具,以《中国大百科全书中国地理》为文本数据源,采用交叉校验方式建立了地理空间关系标注语料库。实现了中文文本中地理空间关系描述的结构化表达,提供了地理空间关系信息抽取的标准化测试数据。展开更多
地理信息的语义解析有效地解决自然语言与地理信息系统之间的语义障碍问题。在分析中文文本和地理信息系统中地理实体描述和表达机制差异的基础上,结合地理命名实体描述的语言特点,制定中文文本的地理命名实体标注体系和标注规范,并以GA...地理信息的语义解析有效地解决自然语言与地理信息系统之间的语义障碍问题。在分析中文文本和地理信息系统中地理实体描述和表达机制差异的基础上,结合地理命名实体描述的语言特点,制定中文文本的地理命名实体标注体系和标注规范,并以GATE(General Architecture for Text Engineering)作为标注平台,构建基于《中国大百科全书中国地理》的大规模标注语料库,以解决当前相关标准和规模化标准数据匮乏的问题。展开更多
文摘As Natural Language Processing(NLP)continues to advance,driven by the emergence of sophisticated large language models such as ChatGPT,there has been a notable growth in research activity.This rapid uptake reflects increasing interest in the field and induces critical inquiries into ChatGPT’s applicability in the NLP domain.This review paper systematically investigates the role of ChatGPT in diverse NLP tasks,including information extraction,Name Entity Recognition(NER),event extraction,relation extraction,Part of Speech(PoS)tagging,text classification,sentiment analysis,emotion recognition and text annotation.The novelty of this work lies in its comprehensive analysis of the existing literature,addressing a critical gap in understanding ChatGPT’s adaptability,limitations,and optimal application.In this paper,we employed a systematic stepwise approach following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)framework to direct our search process and seek relevant studies.Our review reveals ChatGPT’s significant potential in enhancing various NLP tasks.Its adaptability in information extraction tasks,sentiment analysis,and text classification showcases its ability to comprehend diverse contexts and extract meaningful details.Additionally,ChatGPT’s flexibility in annotation tasks reducesmanual efforts and accelerates the annotation process,making it a valuable asset in NLP development and research.Furthermore,GPT-4 and prompt engineering emerge as a complementary mechanism,empowering users to guide the model and enhance overall accuracy.Despite its promising potential,challenges persist.The performance of ChatGP Tneeds tobe testedusingmore extensivedatasets anddiversedata structures.Subsequently,its limitations in handling domain-specific language and the need for fine-tuning in specific applications highlight the importance of further investigations to address these issues.
基金Project supported by the National Natural Science Foundation of China (Nos 60533090 and 60603096)the National High-Tech Research and Development Program (863) of China (No 2006AA 010107)
文摘Automatic web image annotation is a practical and effective way for both web image retrieval and image understanding. However, current annotation techniques make no further investigation of the statement-level syntactic correlation among the annotated words, therefore making it very difficult to render natural language interpretation for images such as "pandas eat bamboo". In this paper, we propose an approach to interpret image semantics through mining the visible and textual information hidden in images. This approach mainly consists of two parts: first the annotated words of target images are ranked according to two factors, namely the visual correlation and the pairwise co-occurrence; then the statement-level syntactic correlation among annotated words is explored and natural language interpretation for the target image is obtained. Experiments conducted on real-world web images show the effectiveness of the proposed approach.
文摘为有效地解决当前相关标准和标准数据匮乏的问题,通过分析中文文本中地理空间关系描述的语言特点,提出中文文本的地理空间关系标注体系,并以GATE(General Architecture for Text Engineering)为标注工具,以《中国大百科全书中国地理》为文本数据源,采用交叉校验方式建立了地理空间关系标注语料库。实现了中文文本中地理空间关系描述的结构化表达,提供了地理空间关系信息抽取的标准化测试数据。
文摘地理信息的语义解析有效地解决自然语言与地理信息系统之间的语义障碍问题。在分析中文文本和地理信息系统中地理实体描述和表达机制差异的基础上,结合地理命名实体描述的语言特点,制定中文文本的地理命名实体标注体系和标注规范,并以GATE(General Architecture for Text Engineering)作为标注平台,构建基于《中国大百科全书中国地理》的大规模标注语料库,以解决当前相关标准和规模化标准数据匮乏的问题。
