Information Visualization: Beyond the Horizon
Information Visualization: Beyond the Horizon
Extracting and Querying Relations in Scientific Papers
KI '08 Proceedings of the 31st annual German conference on Advances in Artificial Intelligence
Automatic classification of citation function
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
The ACL Anthology Network corpus
NLPIR4DL '09 Proceedings of the 2009 Workshop on Text and Citation Analysis for Scholarly Digital Libraries
Visualization of relational structure among scientific articles
VISUAL'07 Proceedings of the 9th international conference on Advances in visual information systems
The design, implementation, and use of the Ngram statistics package
CICLing'03 Proceedings of the 4th international conference on Computational linguistics and intelligent text processing
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: Systems Demonstrations
Advances in deep parsing of scholarly paper content
NLP4DL'09/AT4DL'09 Proceedings of the 2009 international conference on Advanced language technologies for digital libraries
Citation chain aggregation: an interaction model to support citation cycling
Proceedings of the 20th ACM international conference on Information and knowledge management
ACL '12 Proceedings of the ACL-2012 Special Workshop on Rediscovering 50 Years of Discoveries
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Scientific authors urgently need help in managing the fast increasing number of publications. We describe and demonstrate a tool that supports authors in browsing graphically through electronically available publications, thus allowing them to quickly adapt to new domains and publish faster. Navigation is assisted by means of typed citation graphs, i.e. we use methods and resources from computational linguistics to compute the kind of citation that is made from one paper to another (refutation, use, confirmation etc.). To verify the computed citation type, the user can inspect the highlighted citation sentence in the original PDF document. While our classification methods used to generate a realistic test data set are relatively simple and could be combined with other proposed approaches, we put a strong focus on usability and quick navigation in the potentially huge graphs. In the outlook, we argue that our tool could be made part of a community approach to overcome the sparseness and correctness dilemma in citation classification.