TU Berlin

Quality and Usability LabSalar Mohtaj

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Salar Mohtaj


Research Field

- Natural Language Processing
- Machine Learning
- Information Retrieval

 Research Topics

- Text quality assessment


Salar is a researcher at the Quality and Usability Lab at Technische Universitat Berlin working on natural language processing applications in crowdsourcing.
His main interest is NLP and machine learning models. He received his bachelor degree from Shahrood University of Technology and master in information technology from AmirKabir University of Technology (Tehran Polytechnic).


Quality and Usability Lab
Deutsche Telekom Laboratories
TU Berlin
Ernst-Reuter-Platz 7
D-10587 Berlin, Germany

Email: salar.mohtaj[at]tu-berlin.de
Tel:  +49 30 8353 58394




Using External Knowledge Bases and Coreference Resolution for Detecting Check-Worthy Statements
Citation key mohtaj2019a
Author Mohtaj, Salar and Himmelsbach, Tilo and Woloszyn, Vinicius and Möller, Sebastian
Title of Book Working Notes of (CLEF) Conference and Labs of the Evaluation Forum, Lugano, Switzerland, September 9-12, 2019.
Pages 1–8
Year 2019
Address RWTH Aachen, Ahornstr. 55, 52056 Aachen, Germany
Month sep
Note online
Publisher CEUR-WS.org
Series CEUR Workshop Proceedings
How Published Fullpaper
Abstract With the proliferation of online information sources, it hasbecome more and more difficult to judge the trustworthiness of a state-ment on the Web. Nevertheless, recent advances in natural languageprocessing allow us to analyze information more objectively according tocertain criteria - e.g. whether a proposition is factual or opinative, or eventhe authority or credibility of an author in a certain topic. In this paper,we formulated a ranking schema that can be employed in textual claimsfor speeding up the human fact-checking process. Our experiments haveshown that our proposed method statistically outperformed the base-line. Additionally, this work describes a multilingual data set of claimscollected from several fact-check websites, which was used to fine-tuningour model.
Link to publication Link to original publication Download Bibtex entry


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