Dear all,

    With apologies for cross-posting.                 

 

HARVARD-YENCHING LIBRARY FORUM
EAST ASIAN DIGITAL scholarship SERIES

 

From History Books to Digital Humanities Database: Methods, Tools, and Case Studies of Chinese Classics

 

Artificial intelligence technology has rapidly changed the study form of humanities. In this presentation, we will discuss the key issues in using natural language processing, deep learning, GIS, database and visualization technologies to design a new digital humanities database from the electronic texts of ancient books. We will introduce automatic tagging tools for ancient Chinese sentence/word segmentation, named entity tagging. Then, we will present a case study of constructing the DH database of Chinese classics Zuozhuan(×ó‚÷), Shiji(Ê·Ó›) and Shijing(ÔŠ½›), which offers word based multi-functional retrieval in addition to the full-text retrieval. Data analysis and visualization also reveal new facts from the texts, such as the personal social relations and travelling distance. Finally, we discuss the potential improvements and applications of the DH database.

 

SPEAKER: Bin Li

https://scholar.harvard.edu/lib/home

Bin Li, Visiting Scholar of CBDB group at IQSS, Harvard University. Associate Professor of Applied Linguistics in the School of Chinese Language and Literature, Nanjing Normal University. Visiting

Scholar at Computer Department, Brandeis University (2015).

 

His research interest is mainly in semantics and ancient Chinese language and history database. His team has created the Chinese CogBank, a lexical database of cognitive properties of concepts. For the studies of ancient Chinese language, his team has constructed a database of Chinese historical lexicon, the Pre-Qin Chinese corpus, and the DH database of Chinese classics.

 

 

TIME: 12-1PM, Feb 5 (Wed)

Location:  Common Room, Harvard-Yenching Library (2 Divinity Ave.)

Light Refreshment Provided

Please RSVP at https://forms.gle/BSG1esNAVgPsa8p9A

 

 

Best Wishes!

Bin Li

Email: [log in to unmask]

Homepage: https://scholar.harvard.edu/lib

Institute for Quantitative Social Science, Harvard University