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An often occurring problem in information retrieval (IR) is the gap between the vocabulary used in formulating the user's information need (topic) and the vocabulary used in writing the documents of the collection to be queried. An example for this problem is the domain of electronic career guidance where an IR system helps young people to decide which profession to choose by automatically computing a ranked list of professions according to the user's interests. The IR system compares a short essay written by the user with descriptions of professions written by domain experts. Typically, people seeking career advice use different words for describing their professional preferences as those employed in the professionally prepared descriptions of professions. Therefore, lexical semantic knowledge and soft matching, i.e. matching semantically related terms, must be especially beneficial to such a system.
Improve the performance of IR on domain specific document collections:
Christof Müller and Iryna Gurevych: |
Wisdom of Crowds versus Wisdom of Linguists - Measuring the Semantic Relatedness of Words |
Torsten Zesch and Iryna Gurevych: |
Torsten Zesch and Iryna Gurevych: |
A Study on the Semantic Relatedness of Query and Document Terms in Information Retrieval |
Christof Müller and Iryna Gurevych: |
Semantic relations in a bilingual corpus of different registers |
Oliver Čulo and Kerstin Kunz and Torsten Zesch: |
Extracting Professional Preferences of Users from Natural Language Essays |
Cigdem Toprak and Christof Müller and Iryna Gurevych: |
Using Wikipedia and Wiktionary in Domain-Specific Information Retrieval |
Christof Müller and Iryna Gurevych: |
Iryna Gurevych: |
Iryna Gurevych: |
Graph-Theoretic Analysis of Collaborative Knowledge Bases in Natural Language Processing |
Konstantina Garoufi and Torsten Zesch and Iryna Gurevych: |
Representational Interoperability of Linguistic and Collaborative Knowledge Bases |
Konstantina Garoufi and Torsten Zesch and Iryna Gurevych: |
Using Tag Semantic Network for Keyphrase Extraction in Blogs |
Lizhen Qu and Christof Müller and Iryna Gurevych: |
Melanie Hartmann and Torsten Zesch and Max Mühlhäuser and Iryna Gurevych: |
Using Wikipedia and Wiktionary in Domain-Specific Information Retrieval |
Christof Müller and Iryna Gurevych: |
Torsten Zesch and Christof Müller and Iryna Gurevych: |
Closing the Vocabulary Gap for Computing Text Similarity and Information Retrieval |
Christof Müller and Iryna Gurevych and Max Mühlhäuser: |
Extracting Lexical Semantic Knowledge from Wikipedia and Wiktionary |
Torsten Zesch and Christof Müller and Iryna Gurevych: |
Christof Müller and Torsten Zesch and Mark-Christoph Müller and Delphine Bernhard and Kateryna Ignatova and Iryna Gurevych and Max Mühlhäuser: |
What to be? - Electronic Career Guidance Based on Semantic Relatedness |
Iryna Gurevych, Christof Müller, Torsten Zesch: |
Cross-lingual Distributional Profiles of Concepts for Measuring Semantic Distance |
Saif Mohammad and Iryna Gurevych and Graeme Hirst and Torsten Zesch: |
Iryna Gurevych, Max Mühlhäuser, Christof Müller, Jürgen Steimle, Markus Weimer, Torsten Zesch: |
Iryna Gurevych, Christof Müller, Torsten Zesch: |
Integrating Semantic Knowledge into Text Similarity and Information Retrieval |
Christof Müller, Iryna Gurevych, Max Mühlhäuser: |
Analysis of the Wikipedia Category Graph for NLP Applications |
Torsten Zesch and Iryna Gurevych: |
Comparing Wikipedia and German Wordnet by Evaluating Semantic Relatedness on Multiple Datasets |
Torsten Zesch and Iryna Gurevych and Max Mühlhäuser: |
Analyzing and Accessing Wikipedia as a Lexical Semantic Resource |
Torsten Zesch and Iryna Gurevych and Max Mühlhäuser: |
Automatically creating datasets for measures of semantic relatedness |
Torsten Zesch and Iryna Gurevych: |
Exploring the Potential of Semantic Relatedness in Information Retrieval |
Christof Müller, Iryna Gurevych: |
In 2006 the SIR project team offered a Seminar on Unstructured Information Management at the University of Tübingen.
The Division of Computational Linguistics at the University of Tübingen is co-applicant of the SIR project. Their research focus is on further development of the GermaNet ontology using the BERUFEnet corpus.
In cooperation with the German Federal Agency for Employment (Bundesagentur für Arbeit), we employ semantic information retrieval algorithms to realize electronic career guidance. Using a natural language essay of the person seeking advice, relevant professions are found based on their natural language descriptions.
This project is funded by Deutsche Forschungsgemeinschaft (German Research Foundation).