Zoom link: https://durhamuniversity.zoom.us/j/91336059453?pwd=azdKZ0srR0c5Z0VsNEp6bzJOV3ZuQT09

Setup:

• Recommended web browsers: Firefox or Chrome; Safari and Edge should also work for most tasks, but have not been fully tested.
• Create a ctext account and log in
• Check your e-mail (and spam folder) for an e-mail sent from the system, and click the link in the e-mail to validate your account.
• Go to “Settings” at the bottom left, enter the API key specified in the live session in the box under “API key”, and click “Save”
• Install the Text Tools plugin by opening this link, and then clicking “Install”
• Install the Annotation plugin by opening this link, and then clicking “Install”

Some parts of the material that will be covered in the session are available in step-by-step tutorials, which also include other details and examples and might be useful if you want to come back to the material later:

• Practical introduction to ctext.org – interactive guide to core functionality of the Chinese Text Project.
• Text Tools for ctext.org – interactive guide to using the Text Tools plugin for the Chinese Text Project for text mining and data visualization.
• the posts on text reuse and regular expressions on Digital Sinology.
• Classical Chinese Digital Humanities (on Digital Sinology) – step-by-step guide to getting started with programming in Python, and accessing the CTP API for simple text mining.

These parts of the ctext.org instructions should also be useful:

• Editing linked texts
• Wiki formatting rules
• Semantic annotation
• Annotation plugin

Lastly, some of these papers may be of interest:

• Digitizing Premodern Text with the Chinese Text Project, Journal of Chinese History 2020, 4(2).
• Chinese Text Project: a dynamic digital library of premodern Chinese, Digital Scholarship in the Humanities (2019)
• Digital Approaches to Text Reuse in the Early Chinese Corpus, Journal of Chinese Literature and Culture 2018, 5(2).
• Large-scale Optical Character Recognition of Pre-modern Chinese Texts, International Journal of Buddhist Thought and Culture 2018, 28(2).
• Unsupervised Identification of Text Reuse in Early Chinese Literature, Digital Scholarship in the Humanities (2018)

Poster presented at Linked Pasts 2020

For more details, please see the extended abstract “Crowdsourcing the historical record: knowledge base construction for Chinese history at scale” presented at Taiwan’s DADH 2020 conference.

Abstract

The tasks of semantically annotating historical primary source materials and systematically recording knowledge about historical entities have closely connected conceptual relationships. Annotations can be leveraged to extract knowledge about entities, and knowledge about entities can be leveraged to aid in the efficient annotation of texts. Both tasks also rely in practice upon accurate transcriptions of primary source materials, which – like annotations and structured knowledge – are costly to produce in the first instance. This poster describes a crowdsourced approach in which all three tasks are carried out in parallel, involving not only the distributed transcription of texts, but also the creation of a knowledge base continually expanded through user contributions made partly through annotation, immediately and automatically contributing to improved automated assistance with ongoing and future crowdsourced annotation.

Setup

• Create a free account on ctext.org and log in.
• Make sure to validate your e-mail address by opening the link the system sent you (if not, the link above will display a warning/reminder in red to do so).
• Enter the API key “aas2019″ (without quotes) in the box labeled “API key”, and click “Save”.
• [Optional] Install the “Text Tools” plugin into your ctext account.

Some parts of the “Practical introduction to ctext.org” and “Text Tools for ctext.org” will be demonstrated – please refer to the tutorials for step-by-step instructions.

Direct link to Text Tools: http://ctext.org/plugins/texttools/#help

Other suggested examples

In addition to the examples shown in the tutorials:

• Try comparing the aggregate vocabulary of two texts (e.g. the 墨子 and 呂氏春秋) using the “Vectors” tab. Click “Toggle values” to display the heatmap, and try inspecting some of the comparisons.
• Try the “Run PCA” link with these or other texts.
• Try creating vectors that model only a specifically selected subset of vocabulary use. To do this, start by entering multiple search terms in the Regex tool (one per line) – one example would be grammatical particles such as 而, 也, 以, 乎, 之, 矣, 亦. From the “Summary” tab, click “Create vectors”, and then from the output choose “Run PCA”.
• Using the regex tool with “Group rows by” set to “None” and “Extract groups” checked, try extracting data about biographies in the 宋史. You may want to start by using a small part of the text, e.g. 列傳第二十一 (ctp:ws281485). Example regex: (\w+)，字(\w+)，(\w+)人。
• A few additional examples and instructions for using materials not written in classical Chinese are available on the SUTD workshop page.