Digital Analysis in Digital Humanities
A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Big Data and Augmented Intelligence".
Deadline for manuscript submissions: 30 September 2024 | Viewed by 8709
Special Issue Editors
2. Leibniz Institute for Educational Media | Georg Eckert Institute, 38118 Braunschweig, Germany
Interests: digital analysis and digital humanities; machine learning; natural language processing; knowledge discovery and data linking of structured and unstructured open linked data; semantic technology and knowledge management of big data
Special Issues, Collections and Topics in MDPI journals
Interests: data and web mining; human-machine interaction; information processing; user and data modeling; semantic web and information retrieval
Special Issue Information
Dear Colleagues,
The aim of this Special Issue on “Digital Analysis in Digital Humanities” is to gather results on the interdisciplinary area of innovative data analytics methods and customized technologies for digital humanities research and analysis.
This Special Issue is calling for submissions of novel and innovative research results on innovative methods and technologies with a clear reference to data collection, data processing, data analysis and data visualizations that can be exploited in the field of digital humanities and cultural heritage.
This Special Issue also invites submissions that concentrate on the well-founded idea that digital analysis enables the digital humanities domain to digitally transform its research analysis and results, becoming more innovative and forward-looking in its decision making.
In addition, this Special Issue aims to emphasize the role of humanists in data analysis through demonstrating the successful and challenging application of human interaction to interpretate the quantitative analysis and define new advanced and customized solutions.
The Special Issue thus intends to focus on any aspect of “Digital Analysis in Digital Humanities” referring to computer science and humanist research interests in addition to recent research trends.
Dr. Francesca Fallucchi
Prof. Dr. Ernesto William De Luca
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 submissions that pass pre-check are 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. Future Internet 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 1600 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
- digital humanities
- digital analysis
- digital libraries
- information technology
- data mining
- machine learning
- natural language processing
Planned Papers
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Generating Synthetic Resume Data with Large Language Models for Enhanced Job Description Classification
Authors: Panagiotis Skondras; Panagiotis Zervas; Giannis Tzimas
Affiliation: University of Peloponnese
Abstract: The rapid increment of online resume platforms and the escalating influence of digital recruitment (e-recruitment) have resulted in a surge of data, intensifying the challenge of extracting valuable insights and metadata from resumes. This article addresses the extraction of metadata from digital resumes marked by big data attributes, which demand frequent updates and continuous monitoring.
We propose a multi-faceted approach. We first deploy web crawlers designed to proficiently aggregate resumes from online sources (i.e., indeed.com). Subsequently, we leverage natural language processing (NLP) techniques for data sanitization and preprocessing to ensure the consistency and quality of the gathered resumes.
A major obstacle is the absence of annotated data, critical for the efficiency of machine learning algorithms. To overcome this, we turn to ChatGPT and other advanced text-generative models. Through prompt engineering, these Large Language Models (LLMs) utilized to generate annotated data for varied resume-based tasks, such as candidate categorization, skills identification, and experience assessment. This approach amplifies the advantages of LLMs, ensuring the generation of reliable and accurate annotated datasets for efficient metadata extraction.
Additionally, enhancing the extraction process we exploit the potential of embeddings and deep learning. Training deep learning models on LLM-generated annotated datasets, we aim to capture the intricate contextual subtleties within representations. This strategy enables precise and swift metadata extraction, benefiting from the inherent power of deep learning algorithms and the rich semantics of embeddings.
In conclusion, this article presents a novel approach for digital resume metadata extraction, navigating the challenges posed by big data and optimizing deep learning techniques. Experiments reveal that merging synthetic data with real-data yields superior outcomes, validating our approach's effectiveness in producing accurate metadata, setting the stage for improved candidate matching and recruitment decisions.
Keywords: Metadata extraction, Resumes, CV, Big data, Web crawling, Data preprocessing, ChatGPT, Large Language Models, Deep learning, Embeddings, Text Classification, Labor market analysis