Alexander Panchenko
aName: | Dr. Alexander Panchenko |
Position: | Postdoctoral researcher |
Email: | panchenko...informatik.uni-hamburg.de |
Phone: | +49 40 42883 2368 |
Fax: | +49 40 42883 2345 |
Office: | F-416 |
Address: |
Informatikum Vogt-Kölln-Straße 30 22527 Hamburg |
Hello, I am Alexander, a postdoctoral researcher in Natural Language Processing working with Prof. Chris Biemann. My main research interest is computational lexical semantics, including semantic relatedness, word sense induction, and disambiguation.
In the past, I worked on other NLP-related topics including short text classification, NLP for social media analysis, and skill extraction from text. More generally, I am interested in statistical natural language processing, information retrieval, semantic web, machine learning and intersections/interactions of these fields. Currently, I am
Recent News:
- I
co-organize a special issue in the Natural Language Engineering journal on "Informing Neural Architectures for NLP with Linguistic and Background Knowledge" together with Simone Paolo Ponzetto and Ivan Vulić. - A keynote talk at the 24th International Conference on Computational Linguistics and Intellectual Technologies (Dialogue'2018): From unsupervised induction of linguistic structures to applications in deep learning.
- The best paper award in the category "Impact on Society" of Fraunhofer IGD and the Visual Computing Groups of TU Darmstadt for the paper "new/s/leak - Information Extraction and Visualization for Investigative Data Journalists".
- I co-organize the 7th Conference on Analysis of Images, Social Networks, and Texts (AIST'2018). The selected papers will be published in the Springer LNCS series.
- An invited talk at the Global WordNet Conference (GWC'2018) in Singapore on inducing interpretable word senses for word sense disambiguation and enrichment of lexical resources.
- An article is accepted in the Natural Language Engineering article on graph-based distributional semantics.
- I co-organize a shared task on word sense induction for the Russian language. 18 teams participated in the task submitting 383 models. An overview of the results is available in this preprint.
- The release of a web-scale dependency-parsed corpus of English texts, based on the CommonCrawl web crawls. The corpus features over 250 billion of tokens and is available at Amazon S3.
- The release of a web demo of the unsupervised, knowledge-free, and interpretable system for word sense disambiguation presented at EMNLP 2017 in Copenhagen.
Click on the image below to see a demo of a word sense disambiguation system, which integrates a few ideas from my research: