Past Projects

Automatic Quality Assessment and Feedback in eLearning 2.0 (AQUA)

The project investigates the use of Natural Language Processing and Machine Learning techniques to automatically measure the quality of user generated textual documents in Web 2.0, such as forum posts, Wikipedia articles, or blog entries. This can be utilized to recommend the user (e.g. the learner) high-quality materials, to implement quality-aware information retrieval, or to predict the popularity of web sites for computational advertising.

Ambient Semantic Computing (ASC)

Video lectures, audio recordings, wiki content, and forum entries are often seen as separate entities. The goal of ASC is the integration of these multimodal content streams by combining techniques from Natural Language Processing and Human Computer Interfaces.

Feel free to download our  ASC Flyer

Loewe Research Center Digital Humanities: Text as an Instance

Descriptions of natural language grammars tend to focus on the canonical constructions of a language, yet actual usage also displays constructions that are in different ways marked and thus deviate from the canonical form. The project aims to validate the hypothesis that natural language grammars constitute systems of construction that centered on a set of canonical constructions of a particular language which are complemented by a set of peripheral non-canonical constructions. A contrastive investigation of non-canonical grammatical constructions between English and German is performed using corpus-based methods.

Loewe Research Center Digital Humanities: Text as a Process

In this project, we aim at gaining insights into collaboration-, production- and reception processes of collaboratively created Web 2.0 texts. We aim at analyzing the change of collaboratively created texts over time, discovering quality measures and identifying successful collaboration patterns. While focusing onWikipedia as one of the most popular instances of collaboration plattforms, our research results can be generalized to other areas of collaboration in the Web 2.0 and will foster research both in NLP and in the humanities.

Loewe Research Center Digital Humanities: Text as Product

This project examines the correspondence of linguistic concepts and automatically extracted topic models. 
For our analysis, we annotate a text corpus with lexical cohesion relations and automatically acquire topics. Then, we use LDA topic models to predict lexical cohesion, at this using topic membership of lexical items and significance scores between lexical items to inform an automatic system for lexical chain annotation. Besides aiming at a state-of-the art system for lexical chain identification, we analyse the semiotic interpretability  of stochastic methods.

Mining Lexical-Semantic Knowledge from Dynamic and Linguistic Sources and Integration into Question Answering for Discourse-Based Knowledge Acquisition in eLearning (QA-EL)

The project investigates novel applications of dynamic lexical-semantic resources for information search in eLearning. On the one hand, we develop novel ways of mining knowledge from Wikipedia and other Web 2.0 knowledge repositories. On the other hand, we apply question answering in the area of discourse-based knolwedge acquisition in eLearning for the first time. 

Feel free to download our QA-EL Flyer.

Open window

Open window is concerned to give the oportunity for learners to look into interlinked educational content in the World Wide Web. As part of the Open Window project, technologies for automatic linking educational content with different collaboratively created media are developed. These collaborative created media include encyclopedias, such as Wikipedia, and social media services, such as Twitter.

Semantic Assistance Services for Career Integration and Personal Competence Development

The SABINE project (German: "Semantische Assistenzdienste für die berufliche Integration und Persönliche Kompetenzentwicklung") develops methods to interlink the databases of recruitment agencies, personnel services and human resources departments by means of semantic methods. The UKP Lab's contribution will be in methods which extract semantic knowledge from domain-independent sources like Wikipedia by means of statistical text analysis.

Semantic Information Retrieval (SIR-3)

This project systematically investigates the semantic and lexical relationships between words and concepts and its usefulness in information retrieval (IR) tasks. The current phase (III) of the project focuses on the development of large-scale word sense disambiguated multilingual lexical semantic resources and the development of novel semantics-based approaches to cross-lingual IR (CLIR).

Sentiment Analysis for User-Generated Discourse in eLearning 2.0

The project aims to support easy exploration of subjective content and feedback generation to content providers. We develop components for subjectivity identification, opinion and topic extraction. 

Feel free to download our SENTAL Flyer.

Secure Documents using Individual Markers (SiDiM)

The primary goal of the project is to develop novel methods that individualize electronic documents through the manipulation of their textual content that is unrecognizable by a reader. The marks are supposed to be difficult to remove, and at the same time to have no recognizable affect to the meaning of the content. This solution will be embedded in an electronic document distribution environment and remain transparent to an end user.

Semantics- and Emotion-Based Conversation Management in Customer Support (SIGMUND)

The project is concerned with an ultimately new area in the situation-aware support of phone-base customer support: optimizing the work of call center agents through an automatic call monitoring and a dynamic selection and presentation of the relevant documents during the call. Our contribution is in the area of semantic document analysis and context-aware information retrieval.

Feel free to download our  SIGMUND Flyer

Semantic Information Retrieval Part 1 & 2 (SIR)

This project systematically investigates the possible usage of semantic and lexical relationships between words or concepts for improving the information retrieval process. The main focus is on semantic relatedness measures using different knowledge sources (e.g. WordNetGermaNet, or Wikipedia). 

Feel free to download our  SIR Flyer


The project investigates the use of semantic technologies to enable future business value networks. Our main focus is the use of NLP and semantic IR technologies to enable automatic service search and discovery as well as community mining methods to recognize opinions and trends about services. 

Feel free to download our  THESEUS Texo Flyer

Wikulu – Self-Organizing Wikis

Wikulu assists the user while creating, editing, or searching content. The self-organizing abilities of the wiki are enabled through Natural Language Processing algorithms like keyphrase extraction, document summarization, document clustering, or graph-based term weighting. 

Feel free to download our WIKULU Flyer

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