Johannes Kirschnick


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Streaming Text Analytics for Real-Time Event Recognition
Philippe Thomas | Johannes Kirschnick | Leonhard Hennig | Renlong Ai | Sven Schmeier | Holmer Hemsen | Feiyu Xu | Hans Uszkoreit
Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017

A huge body of continuously growing written knowledge is available on the web in the form of social media posts, RSS feeds, and news articles. Real-time information extraction from such high velocity, high volume text streams requires scalable, distributed natural language processing pipelines. We introduce such a system for fine-grained event recognition within the big data framework Flink, and demonstrate its capabilities for extracting and geo-locating mobility- and industry-related events from heterogeneous text sources. Performance analyses conducted on several large datasets show that our system achieves high throughput and maintains low latency, which is crucial when events need to be detected and acted upon in real-time. We also present promising experimental results for the event extraction component of our system, which recognizes a novel set of event types. The demo system is available at


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Real-Time Discovery and Geospatial Visualization of Mobility and Industry Events from Large-Scale, Heterogeneous Data Streams
Leonhard Hennig | Philippe Thomas | Renlong Ai | Johannes Kirschnick | He Wang | Jakob Pannier | Nora Zimmermann | Sven Schmeier | Feiyu Xu | Jan Ostwald | Hans Uszkoreit
Proceedings of ACL-2016 System Demonstrations

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JEDI: Joint Entity and Relation Detection using Type Inference
Johannes Kirschnick | Holmer Hemsen | Volker Markl
Proceedings of ACL-2016 System Demonstrations


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A Marketplace for Web Scale Analytics and Text Annotation Services
Johannes Kirschnick | Torsten Kilias | Holmer Hemsen | Alexander Löser | Peter Adolphs | Heiko Ehrig | Holger Düwiger
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: System Demonstrations

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Freepal: A Large Collection of Deep Lexico-Syntactic Patterns for Relation Extraction
Johannes Kirschnick | Alan Akbik | Holmer Hemsen
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

The increasing availability and maturity of both scalable computing architectures and deep syntactic parsers is opening up new possibilities for Relation Extraction (RE) on large corpora of natural language text. In this paper, we present Freepal, a resource designed to assist with the creation of relation extractors for more than 5,000 relations defined in the Freebase knowledge base (KB). The resource consists of over 10 million distinct lexico-syntactic patterns extracted from dependency trees, each of which is assigned to one or more Freebase relations with different confidence strengths. We generate the resource by executing a large-scale distant supervision approach on the ClueWeb09 corpus to extract and parse over 260 million sentences labeled with Freebase entities and relations. We make Freepal freely available to the research community, and present a web demonstrator to the dataset, accessible from


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Effective Selectional Restrictions for Unsupervised Relation Extraction
Alan Akbik | Larysa Visengeriyeva | Johannes Kirschnick | Alexander Löser
Proceedings of the Sixth International Joint Conference on Natural Language Processing