The language technology lab carries out research in the field of Natural Language Processing
We strongly believe that engineering is a key part of research in this field and that often a new insight is only to be found when re-implementing an approach. We are especially interested in analyzing and processing non-standard, error-prone language as found in social media and learner language.
Consequently, we mainly focus on two areas of specialization:
Educational NLP: Short answer scoring, Essay scoring, Vocabulary Acquisition, Spelling and grammar correction
Social Media Analysis: Robustness of tools, Domain adaption, Large-scale semantic processing
Educational Language Technology
We mainly focus these research areas of educational language technology:
- Short answer scoring
- Essay scoring
- Vocabulary Acquisition
- Spelling and grammar correction
- Exercise generation
- Exercise difficulty prediction
Social Media Analysis
We mainly focus on these research areas of social media analysis:
- Robustness of tools
- Domain adaption
- Large-scale semantic processing
- Stance detection
- Paraphrase and entailment recognition
- Robust and scalable preprocessing
We are committed to reproducible and replicable research. Thus, we develop and maintain multiple open-source software projects:
Human-centered Cyber-Physical Systems
Language Technology Lab is a part of the research profile Human-centered Cyber-physical Systems in the Faculty of Engineering.
Language plays an important role in in cyber-physical systems as a means for communication between humans and such systems. Thus, it is necessary to better understand how machines can automatically understand language. On the one hand, we need to analyze the structure of the language, e.g. by automatically identifying POS tags. On the other hand, we need to semantically analyze a given statement and to contextualize it given a certain interaction. For this purpose, it is necessary to better understand the role of the language users in a communication process.
business_centerUser-Centered Social Media (DFG Research Training Group 2015-2020)
User-Centred Social Media
The Research Training Group “User-Centred Social Media” (UCSM) is an interdisciplinary Research Training Group (Graduiertenkolleg) at the Department of Computer Science and Applied Cognitive Science of the University of Duisburg-Essen. This programme is funded by DFG and starts on October 1, 2015.
The emergence of Social Media marks a significant step in the application of information and communication technology with a profound impact on people, businesses, and society. Social Media constitute complex sociotechnical systems, encompassing potentially very large user groups, both in public and organizational contexts, and exhibiting features such as user-generated content, social interaction and awareness, and emergent functionality. While Social Media use is widespread and increasing, significant research gaps exist with respect to analyzing and understanding the characteristics and determinants of user behaviour, both at the individual and the collective level, as well as regarding the user-centered design of Social Media systems, aiming at empowering users to better appropriate, control and adapt systems for their individual goals. There is a growing demand in academia and in industry for scientifically trained experts that are knowledgeable both in the human-oriented and the technical aspects of Social Media.
More information can be found at the User-Centred Social Media Homepage
business_centerArgument-Based Decision Support for Recommender Systems (DFG SPP RATIO 2018-2020)
Argument-Based Decision Support for Recommender Systems (ASSURE)
ASSURE is a project within the SPP RATIO.
Argumentative statements contained in user-generated texts such as online product reviews can significantly facilitate a user’s decision. Recommender systems aim at alleviating the user’s decision problem by suggesting items the user is likely interested in, but do not exploit the potential of reasoned arguments given for or against a certain item or its properties. The overall objective of ASSURE project is to make use of arguments embedded in online reviews to significantly improve the quality and transparency of recommendations given by the system, and to provide users with a much higher level of interactive control over the recommendation process than is currently the case.
The project aims at advancing the state of the art in several respects: Firstly, we will develop novel methods for extracting arguments from the typically informal texts found in user reviews. We will further enrich the arguments with annotations of how specific and how emotionally intense they are.
Secondly, we will combine the extracted arguments and the additional annotations with user ratings and other item-related data in an integrated user and item model to improve the effectiveness of recommender algorithms. This model will also provide a basis for developing novel techniques through which users can interactively explore, filter, or weight different arguments, as well as other data, to control how recommendations are generated. Thirdly, we will develop methods for providing users with personalized, argument-based explanations of the items recommended. A further important outcome of the project will be a dataset of unprecedented quality and size that is annotated on different layers regarding argumentation. Such a dataset is a prerequisite for further research on argumentation in the context of recommending, and will be suited for use in shared tasks that form part of the priority program.
More information can be found at the ASSURE Homepage.
business_centerAutomatic Scoring of Free-Text Answer (TestDaF 2019-2020)
The TestDaF Institute is one of the biggest providers of language proficiency testing for German as a foreign language. LTL collaborates with the TestDaF institutes on automatic and assisted scoring of free-form answers, more precisely answers given to listening comprehension prompts and learner essays. We explore way how the the scoring workload of humans graders can be reduced and how we can ensure fast and consistent scoring of free text answers.
business_centerBildungsgerechtigkeit im Fokus II (BMBF 2016-2020)
Within the project Bildungsgerechtigkeit im Fokus we are part of Teilprojekt 2: Blended learning.
business_centerSustainability of research software (2018 - 2020)
Reproducibility of experiments is a key requirement of scientifical working. With DKPro Core and DKPro Text Classification, we are working towards an improved reproducibilty of software experiements.
We received a 3 year funding by the DFG to further improve DKPro Core and DKPro Text Classification as landscape marks fit for conducting scientifically experiments.
codeCode and Data
Fast or Accurate? A Comparative Evaluation of PoS Tagger Models
Part-of-Speech tagging is an important preprocessing step for many applications in Natural Language Processing. This importance is reflected by many PoS tagger implementations available today. Which one do you use? Are you sure it is the most suited choice for your demands?
For choosing a PoS tagger there a two properties that should influence your choice:
Speed and Accuracy
Big Data scenarios shift speed stronger into the focus than in Digital Humanities where speed is often of minor importance.
Despite of the well known PoS tagger provided by Stanford or the TreeTagger, there are actually many more alternatives to them. Each implementation provides often more than just one model, which is the best?
We evaluated in total 27 models for 9 different PoS tagger implementations. The tagger implementation are listed below, we evaluated them on two languages, English and German.
In English, we evaluated each tagger model on the following corpora: British National Corpus, Brown, Gimpel, MASC, Switchboard. In German we evaluated on the Tüba-D/Z and Rehbein.
We excluded in English the Wall-Street-Journal and in German the Tiger and Negra corpus as many models have been trained with those corpora.
We evaluate for one language each corpus on each PoS tagger model and measure additionally the runtime of the PoS tagger for the tagging. The measuring starts before the tagger is called and ends right after it. Below figure shows the workflow of our experiment.
To overcome the differences in the tagsets of the various corpora, we harmonised the tags to a coarse grained tagset composing of eleven tags.
The samples highlighted in red are the ones showing the best speed/accuracy combination. The surprising winner is a rule-based Hepple tagger.
The most accurate German tagger is the TreeTagger. HunPos offers a reasonable trade-off between speed and accuracy. We currently do not have a rule-based tagger for German to test whether the results of the Hepple tagger transfer to German.
How to cite us?
Horsmann, Tobias; Erbs, Nicolai; Zesch, Torsten (2015): Fast or Accurate ? – A Comparative Evaluation of PoS Tagging Models. Proceedings of the International Conference of the German Society for Computational Linguistics and Language Technology (GSCL-2015), Essen, Germany.
business_centerSemi-automatic generation of reading comprehension questions (Stifterverband 2018-2019)
The goal of this project is to improve the provision and integration of the reading comprehension tests. We aim to motivate the students to study literatures that are relevant to the lecture. After this, there will be some different types of test (free text, multiple choice, fill the blank, etc.) automatically generated by state-of-the-art language technology and curated by the teachers.
These tasks will be varied based on the curriculums conducted by us and can be directly accepted and used for evaluation. We will make the required software open source and can be easily integrated in existing teaching process through a strait forward integration by module.
The project has an extremely potential for transferring to other disciplines and teaching format. Afterall, whenever source texts are available, reading comprehension tests can be generated for purpose and this will also reduce the amount of manual work.
For more information, please visit FELLOWSHIPS HOCHSCHULLEHRE - FELLOWS 2017
business_centerCAPE - Computer-assisted Programming Exercises (UDE 2018)
In the lecture "Fundamental Artificial Intelligence" (about 250 students) and "Language Technology" (about 50 students), we will prepare some programming tasks for the students, to improve the programming ability of the students. Additionally, in order to lower the access barrier, we use some pre-configured system for the programming tasks.
Link to the website coming soon.
business_centerINDUS - Individualized Language Learning (DFG 2014-2018)
Individualized language learning as a counterpart to standardized classes is now just around the corner due to new developments in the field of language technology. Thus not only commonly spoken languages but also languages with a smaller amount of native speakers can be learned. It becomes apparent however, that embedding those technologies into real learning environments gives rise to new questions, which can only be answered with the framework of interdisciplinary research.
The INDUS-Network („Individualisiertes Sprachlernen” / „Personalized language learning”) unites experts in the fields of language technology, linguistics, educational research, psychology of learning, pedagogical psychology, language acquisition research, and didactics of language learning.
Those experts work together on aspects of individualization, modeling of learners, and adjustments of teaching materials to different initial situations.
More information on the website of the INDUS-Network Homepage.
business_centerGerman-Arab Transformation Partnership (DAAD 2016)