Computational Linguistics Commons^™

Syntax-Enhanced Boundary-Aware Named Entity Recognition Model, Chuanming Yu, Bin Deng, Zhengang Zhang

Journal of Scientific Information Research

[Purpose/significance] This study addresses the issue of inadequate perception of entity boundaries in traditional character-level modeling-based named entity recognition models by integrating syntax information containing entity boundary features into the task using a multi-head graph attention network with dense connections. This integration enhances the effectiveness of named entity recognition.

[Method/process] This study proposes a Syntax-enhanced Boundary-aware Named Entity Recognition Model (SynBNER), which utilizes BERT for text semantic representation and integrates syntax information using a dense-connected graph attention network. This integration incorporates implicit entity boundary information from syntax information into word representations, thereby enhancing the model's entity boundary perception capability.

[Result/conclusion] …

Research On The Application Of Part-Of-Speech Tagging Of Ancient Books Under The Domain Large Language Model, Danhao Zhu, Zhao Zhixiao, Die Hu, Wenhua Zhao

Journal of Scientific Information Research

[Purpose/significance]The development of the large language model has brought new ideas for ancient text mining, and combining the large language model with the digitisation and intelligence of ancient books is a necessary path for the work of ancient books in the new era. [Methods/process]This paper uses the lexically annotated corpus of Zuozhuan to construct a batch of high-quality lexically annotated instruction data through data cleaning and preprocessing, on the basis of which 500, 1 000, 2 000, and 5 000 pieces of data are used to fine-tune the instructions of the large language model, and the performance test is carried …

Research On Topic Discovery And Evolution Trend Based On Temporal Keyword Characteristics Analysis, Shuqing Li, Juntao Zhu, Wan Wang

Journal of Scientific Information Research

[Purpose/significance]Excavating the research topics in a large number of articles, sorting out the evolution context and correlation of the research topics, predicting the frontier hot spots of the topics can be helpful to enhance the scientificity and vividness of the evolution results.[Method/precess]This paper puts forward the concept of time series influence factor as an important feature in keyword extraction, uses the method of time window to mine and identify topics by using topic model, and makes visual analysis. By applying time series model in the field of deep learning, the purpose of predicting topic popularity is achieved.[Result/concluson]It is verified that …