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Educational Natural Language Processing

Educational Natural Language Processing (e-NLP) aims at

finding new applications of Natural Language Processing for educational purposes, and
developing new techniques and software taking into account the specific needs in the educational domain.

We especially focus on NLP applications for eLearning 2.0, which is characterized by a worldwide learning community where educational material is produced both by students and teachers. This brings about new challenges for NLP since the amount of user-generated discourse and social media content such as wikis and blogs is constantly growing and requires intelligent automatic processing.

Objectives of Natural Language Processing for eLearning 2.0

Support eLearning 2.0 by mining and representing knowledge in and for Web 2.0
Provide semantically enhanced information management (information retrieval, question-answering, summarization)
Analyze user-generated content (opinion mining, quality assessment)

Projects in Educational Natural Language Processing

SIR – electronic career guidance. This project systematically investigates the possible usage of semantic and lexical relationships between words or concepts for improving the information retrieval process in the domain of electronic career guidance. The main focus is on semantic relatedness measures using different knowledge sources (e.g., WordNet, GermaNet, and Wikipedia).
QA-EL – Question Answering for discourse-based knowledge acquisition. This project investigates novel applications of dynamic lexical-semantic resources for information search in eLearning. We develop novel ways of mining knowledge from Wikipedia and other Web 2.0 knowledge repositories, and we apply question answering in the area of discourse-based knowledge acquisition in eLearning for the first time.
SENTAL – Sentiment Analysis for User-generated Discourse
EduWeb – Educational Web 2.0. This project explores the use of Natural Language Processing and collaboratively constructed resources on the Web (i.e., Wikipedia and Wiktionary) for innovative applications in technology enhanced education.
WiWeb – This project examines the usefulness of selected, innovative language technologies according to psychological processes and models. This research project will lay important groundwork by bringing together scientists from computer science, industrial science, and psychology.

We also take part in the following joint software development projects at the UKP Lab:

WikiMining. This projects aims at providing structured access to information nuggets stored in Wikipedia and Wiktionary like redirects, categories, articles, and link structure. Knowledge extracted from Wiktionary and Wikipedia is used in the SIR and QA-EL projects to enhance information retrieval and question answering for eLearning with semantic information.
Darmstadt Knowledge Processing (DKPro) Repository. All software developed within our research projects is integrated in the DKPro Repository.

Selected publications

Helping Our Own 2011: UKP Lab System Description
Torsten Zesch
In: In Proceedings of the Helping Our Own Working Group Session at ENLG 2011, p. 260--262, September 2011.
Helping Our Own Challenge
Integrating Semantic Knowledge into Text Similarity and Information Retrieval
Christof Müller, Iryna Gurevych, Max Mühlhäuser
In: Proceedings of the First IEEE International Conference on Semantic Computing (ICSC),pp. 257–264, 2007.
Notetaking in University Courses and its Implications on eLearning Systems
Jürgen Steimle, Iryna Gurevych, Max Mühlhäuser In: Tagungsband der fünften e-Learning Fachtagung Informatik. Christian Eibl, Johannes Magenheim, Sigrid Schubert, Martin (Eds.). pp. 45–56, Gesellschaft für Informatik e.V., Bonn, Germany, 2007. ISBN 978-3-88579-205-5.
Automatically Assessing the Post Quality in Online Discussions on Software
Markus Weimer, Iryna Gurevych, Max Mühlhäuser In: Companion Volume of the 45rd Annual Meeting of the Association for Computational Linguistics (ACL), pp. 125–128, 2007.