Special Issue "AI for Digital Humanities"

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (30 June 2018).

Special Issue Editors

Prof. Yudong Liu
E-Mail Website
Guest Editor
Computer Science Department, Western Washington University, Bellingham, Washington 98225, USA
Interests: natural language processing; information extraction; application of eye-tracking data
Prof. James Hearne
E-Mail Website
Guest Editor
Computer Science Department, Western Washington University, Bellingham, Washington 98225, USA
Interests: data mining; computational linguistics; artificial intelligence; computer music

Special Issue Information

Dear Colleagues,

Digital humanities (DH) is a newly-emerging field that brings together humanities scholars, social scientists and computer and information scientists to work on both fundamental and applied research in humanities. As techniques in Artificial Intelligence/Machine Learning and Data Mining have matured, there has been a wide range of computational tools, methods, and techniques that have enabled humanities scholars to conduct research at a scale once thought impossible. This Special Issue is calling for the submission of novel research results and digital research tool developments demonstrating the success and challenges of applying Artificial Intelligence techniques in digital humanities research, such as data discovery, digital data creation, management, data analytics (including text mining, image mining and data visualization, etc.) in literature, linguistics, culture heritage, media, social science, history, music and acoustics, and Artificial Intelligence for Digital Humanities in pedagogy and academic curricula. This Special Issue aims, not only to serve a venue for presenting work in this area, but also to build a community and share information in this area. The Special Issue invites submissions on any aspect of Artificial Intelligence for Digital Humanities.

Prof. Yudong Liu
Prof. James Hearne
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Artificial Intelligence/Data Mining/Big Data/Machine Learning
  • Digital Humanities
  • Computational Humanities
  • Culture Analytics

Published Papers (2 papers)

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Research

Article
Knowledge Acquisition from Critical Annotations
Information 2018, 9(7), 179; https://doi.org/10.3390/info9070179 - 20 Jul 2018
Viewed by 1803
Abstract
Critical annotations are important knowledge sources when researching one’s oeuvre. They describe literary, historical, cultural, linguistic and other kinds of information written in natural languages. Acquiring knowledge from these notes is a complex task due to the limited natural language understanding capability of [...] Read more.
Critical annotations are important knowledge sources when researching one’s oeuvre. They describe literary, historical, cultural, linguistic and other kinds of information written in natural languages. Acquiring knowledge from these notes is a complex task due to the limited natural language understanding capability of computerized tools. The aim of the research was to extract knowledge from existing annotations, and to develop new authoring methods to facilitate the knowledge acquisition. After structural and semantic analysis of critical annotations, authors developed a software tool that transforms existing annotations into a structured form that encodes referral and factual knowledge. Authors also propose a new method for authoring annotations based on controlled natural languages. This method ensures that annotations are semantically processable by computer programs and the authoring process remains simple for non-technical users. Full article
(This article belongs to the Special Issue AI for Digital Humanities)
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Article
Tag-Driven Online Novel Recommendation with Collaborative Item Modeling
Information 2018, 9(4), 77; https://doi.org/10.3390/info9040077 - 05 Apr 2018
Viewed by 2363
Abstract
Online novel recommendation recommends attractive novels according to the preferences and characteristics of users or novels and is increasingly touted as an indispensable service of many online stores and websites. The interests of the majority of users remain stable over a certain period. [...] Read more.
Online novel recommendation recommends attractive novels according to the preferences and characteristics of users or novels and is increasingly touted as an indispensable service of many online stores and websites. The interests of the majority of users remain stable over a certain period. However, there are broad categories in the initial recommendation list achieved by collaborative filtering (CF). That is to say, it is very possible that there are many inappropriately recommended novels. Meanwhile, most algorithms assume that users can provide an explicit preference. However, this assumption does not always hold, especially in online novel reading. To solve these issues, a tag-driven algorithm with collaborative item modeling (TDCIM) is proposed for online novel recommendation. Online novel reading is different from traditional book marketing and lacks preference rating. In addition, collaborative filtering frequently suffers from the Matthew effect, leading to ignored personalized recommendations and serious long tail problems. Therefore, item-based CF is improved by latent preference rating with a punishment mechanism based on novel popularity. Consequently, a tag-driven algorithm is constructed by means of collaborative item modeling and tag extension. Experimental results show that online novel recommendation is improved greatly by a tag-driven algorithm with collaborative item modeling. Full article
(This article belongs to the Special Issue AI for Digital Humanities)
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