Corpus Text Annotation

Purpose An automatic text annotation system (ATAS) that can collect resources from different databases through Linked Data (LD) for automatically annotating ancient texts was developed in this study to support digital humanities research. It allows the humanists referring to resources from diverse databases when interpreting ancient texts as well as provides a friendly text annotation reader for humanists interpreting ancient text through reading. The paper aims to discuss whether the ATAS is helpful to support digital humanities research or not. Design/methodology/approach Based on the quasi-experimental design, the ATAS developed in this study and MARKUS semi-ATAS were compared whether the significant differences in the reading effectiveness and technology acceptance for supporting humanists interpreting ancient text of the Ming dynasty’s collections existed or not. Additionally, lag sequential analysis was also used to analyze users’ operation behaviors on the ATAS. A semi-structured in-depth interview was also applied to understand users’ opinions and perception of using the ATAS to interpret ancient texts through reading. Findings The experimental results reveal that the ATAS has higher reading effectiveness than MARKUS semi-ATAS, but not reaching the statistically significant difference. The technology acceptance of the ATAS is significantly higher than that of MARKUS semi-ATAS. Particularly, the function comparison of the two systems shows that the ATAS presents more perceived ease of use on the functions of term search, connection to source websites and adding annotation than MARKUS semi-ATAS. Furthermore, the reading interface of ATAS is simple and understandable and is more suitable for reading than MARKUS semi-ATAS. Among all the considered LD sources, Moedict, which is an online Chinese dictionary, was confirmed as the most helpful one. Research limitations/implications This study adopted Jieba Chinese parser to perform the word segmentation process based on a parser lexicon for the Chinese ancient texts of the Ming dynasty’s collections. The accuracy of word segmentation to a lexicon-based Chinese parser is limited due to ignoring the grammar and semantics of ancient texts. Moreover, the original parser lexicon used in Jieba Chinese parser only contains the modern words. This will reduce the accuracy of word segmentation for Chinese ancient texts. The two limitations that affect Jieba Chinese parser to correctly perform the word segmentation process for Chinese ancient texts will significantly affect the effectiveness of using ATAS to support digital humanities research. This study thus proposed a practicable scheme by adding new terms into the parser lexicon based on humanists’ self-judgment to improve the accuracy of word segmentation of Jieba Chinese parser. Practical implications Although some digital humanities platforms have been successfully developed to support digital humanities research for humanists, most of them have still not provided a friendly digital reading environment to support humanists on interpreting texts. For this reason, this study developed an ATAS that can automatically retrieve LD sources from different databases on the Internet to supply rich annotation information on reading texts to help humanists interpret texts. This study brings digital humanities research to a new ground. Originality/value This study proposed a novel ATAS that can automatically annotate useful information on an ancient text to increase the readability of the ancient text based on LD sources from different databases, thus helping humanists obtain a deeper and broader understanding in the ancient text. Currently, there is no this kind of tool developed for humanists to support digital humanities research.

Download Full-text

A Literature Research on Machine Learning Techniques used for Training Annotated Corpus

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b1063.0882s819 ◽

2019 ◽

Vol 8 (2S8) ◽

pp. 1331-1337

Keyword(s):

Machine Learning ◽

Machine Learning Techniques ◽

Automatic Annotation ◽

Research Papers ◽

Text Data ◽

Literature Research ◽

Annotation Task ◽

Text Annotation ◽

Subsequent Research ◽

Learning Techniques

The development of research in the annotation area is growing. Researchers perform annotation task using various forms of datasets such as text, sound, images, and videos. Various algorithms are used to perform tasks. The purpose of this survey is to find out algorithms that are often used by researchers to perform annotation tasks, especially on text data. The literature surveys thirteen research papers on text annotation from the last 5 years. The results of this review indicate that SVM is the algorithm used for all three annotation methods: manual, automatic and semi-automatic annotation, with a significant accuracy above 80%. The result of this survey will be referred by the authors as the basis for subsequent research that will be conducted, especially in the semi-automatic annotation method.

Download Full-text

Text Annotation Structure

Formalizing Natural Languages ◽

10.1002/9781119264125.ch10 ◽

2016 ◽

pp. 205-212

Keyword(s):

Text Annotation

Download Full-text

Automatic Learning of Medical Text Annotation Rules – a Case Study on Endoscopies

International Conference on Advancements of Medicine and Health Care through Technology; 12th - 15th October 2016, Cluj-Napoca, Romania - IFMBE Proceedings ◽

10.1007/978-3-319-52875-5_53 ◽

2017 ◽

pp. 248-251

Author(s):

R. R. Slavescu ◽

M. N. Oltean ◽

A. P. Torok ◽

K. C. Slavescu

Keyword(s):

Automatic Learning ◽

Text Annotation ◽

Medical Text

Download Full-text

A Chinese ancient book digital humanities research platform to support digital humanities research

The Electronic Library ◽

10.1108/el-10-2018-0213 ◽

2019 ◽

Vol 37 (2) ◽

pp. 314-336 ◽

Cited By ~ 1

Author(s):

Chih-Ming Chen ◽

Chung Chang

Keyword(s):

Social Network ◽

Network Analysis ◽

Chinese Text ◽

Digital Humanities ◽

Word Segmentation ◽

Digital Archives ◽

Content Type ◽

Text Annotation ◽

Research Platform ◽

Ancient Texts

PurposeWith the rapid development of digital humanities, some digital humanities platforms have been successfully developed to support digital humanities research for humanists. However, most of them have still not provided a friendly digital reading environment and practicable social network analysis tool to support humanists on interpreting texts and exploring characters’ social network relationships. Moreover, the advancement of digitization technologies for the retrieval and use of Chinese ancient books is arising an unprecedented challenge and opportunity. For these reasons, this paper aims to present a Chinese ancient books digital humanities research platform (CABDHRP) to support historical China studies. In addition to providing digital archives, digital reading, basic search and advanced search functions for Chinese ancient books, this platform still provides two novel functions that can more effectively support digital humanities research, including an automatic text annotation system (ATAS) for interpreting texts and a character social network relationship map tool (CSNRMT) for exploring characters’ social network relationships.Design/methodology/approachThis study adopted DSpace, an open-source institutional repository system, to serve as a digital archives system for archiving scanned images, metadata, and full texts to develop the CABDHRP for supporting digital humanities (DH) research. Moreover, the ATAS developed in the CABDHRP used the Node.js framework to implement the system’s front- and back-end services, as well as application programming interfaces (APIs) provided by different databases, such as China Biographical Database (CBDB) and TGAZ, used to retrieve the useful linked data (LD) sources for interpreting ancient texts. Also, Neo4j which is an open-source graph database management system was used to implement the CSNRMT of the CABDHRP. Finally, JavaScript and jQuery were applied to develop a monitoring program embedded in the CABDHRP to record the use processes from humanists based on xAPI (experience API). To understand the research participants’ perception when interpreting the historical texts and characters’ social network relationships with the support of ATAS and CSNRMT, semi-structured interviews with 21 research participants were conducted.FindingsAn ATAS embedded in the reading interface of CABDHRP can collect resources from different databases through LD for automatically annotating ancient texts to support digital humanities research. It allows the humanists to refer to resources from diverse databases when interpreting ancient texts, as well as provides a friendly text annotation reader for humanists to interpret ancient text through reading. Additionally, the CSNRMT provided by the CABDHRP can semi-automatically identify characters’ names based on Chinese word segmentation technology and humanists’ support to confirm and analyze characters’ social network relationships from Chinese ancient books based on visualizing characters’ social networks as a knowledge graph. The CABDHRP not only can stimulate humanists to explore new viewpoints in a humanistic research, but also can promote the public to emerge the learning interest and awareness of Chinese ancient books.Originality/valueThis study proposed a novel CABDHRP that provides the advanced features, including the automatic word segmentation of Chinese text, automatic Chinese text annotation, semi-automatic character social network analysis and user behavior analysis, that are different from other existed digital humanities platforms. Currently, there is no this kind of digital humanities platform developed for humanists to support digital humanities research.

Download Full-text

Automatic Discovery Approach of Digital Image Topic

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.610.449 ◽

2014 ◽

Vol 610 ◽

pp. 449-453

Author(s):

Cong Jin ◽

Shu Wei Jin

Keyword(s):

Digital Image ◽

Training Image ◽

Experimental Results ◽

Text Annotation ◽

Training Images ◽

Topic Discovery ◽

Automatic Discovery

A topic discovery approach of the image has been proposed. First, the training images are segmented into some blocks. After clustering blocks, we obtained class set generated by cluster centers, and extracted all nouns from text annotation of each training image to obtain a keyword set. Secondly, the un-label testing image is also segmented into some blocks as same as training images, we calculated the correlation between the block and keyword, and the keyword set for each block may be obtained. Finally, the number of the same keyword appearing in the each block is calculated, we let the keywords with maximum to appear times be as the topics of the image. The experimental results confirm that proposed approach for the image is effectiveness and has good performance.

Download Full-text

TeamTat: a collaborative text annotation tool

Nucleic Acids Research ◽

10.1093/nar/gkaa333 ◽

2020 ◽

Vol 48 (W1) ◽

pp. W5-W11

Author(s):

Rezarta Islamaj ◽

Dongseop Kwon ◽

Sun Kim ◽

Zhiyong Lu

Keyword(s):

Project Managers ◽

Biomedical Literature ◽

Task Completion ◽

Annotation Tool ◽

Web Based ◽

Domain Experts ◽

Plain Text ◽

Text Annotation ◽

User Friendly ◽

Input Format

Abstract Manually annotated data is key to developing text-mining and information-extraction algorithms. However, human annotation requires considerable time, effort and expertise. Given the rapid growth of biomedical literature, it is paramount to build tools that facilitate speed and maintain expert quality. While existing text annotation tools may provide user-friendly interfaces to domain experts, limited support is available for figure display, project management, and multi-user team annotation. In response, we developed TeamTat (https://www.teamtat.org), a web-based annotation tool (local setup available), equipped to manage team annotation projects engagingly and efficiently. TeamTat is a novel tool for managing multi-user, multi-label document annotation, reflecting the entire production life cycle. Project managers can specify annotation schema for entities and relations and select annotator(s) and distribute documents anonymously to prevent bias. Document input format can be plain text, PDF or BioC (uploaded locally or automatically retrieved from PubMed/PMC), and output format is BioC with inline annotations. TeamTat displays figures from the full text for the annotator's convenience. Multiple users can work on the same document independently in their workspaces, and the team manager can track task completion. TeamTat provides corpus quality assessment via inter-annotator agreement statistics, and a user-friendly interface convenient for annotation review and inter-annotator disagreement resolution to improve corpus quality.

Download Full-text