Naveen Kumar Laskari

Also published as: Naveen Kumar


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Integrating Knowledge-Supported Search into the INCEpTION Annotation Platform
Beto Boullosa | Richard Eckart de Castilho | Naveen Kumar | Jan-Christoph Klie | Iryna Gurevych
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Annotating entity mentions and linking them to a knowledge resource are essential tasks in many domains. It disambiguates mentions, introduces cross-document coreferences, and the resources contribute extra information, e.g. taxonomic relations. Such tasks benefit from text annotation tools that integrate a search which covers the text, the annotations, as well as the knowledge resource. However, to the best of our knowledge, no current tools integrate knowledge-supported search as well as entity linking support. We address this gap by introducing knowledge-supported search functionality into the INCEpTION text annotation platform. In our approach, cross-document references are created by linking entity mentions to a knowledge base in the form of a structured hierarchical vocabulary. The resulting annotations are then indexed to enable fast and yet complex queries taking into account the text, the annotations, and the vocabulary structure.


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TWINA at SemEval-2017 Task 4: Twitter Sentiment Analysis with Ensemble Gradient Boost Tree Classifier
Naveen Kumar Laskari | Suresh Kumar Sanampudi
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

This paper describes the TWINA system, with which we participated in SemEval-2017 Task 4B (Topic Based Message Polarity Classification – Two point scale) and 4D (two-point scale Tweet quantification). We implemented ensemble based Gradient Boost Trees classification method for both the tasks. Our system could perform well for the task 4D and ranked 13th among 15 teams, for the task 4B our model ranked 23rd position.