Daraksha Parveen


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Automatic Construction of Enterprise Knowledge Base
Junyi Chai | Yujie He | Homa Hashemi | Bing Li | Daraksha Parveen | Ranganath Kondapally | Wenjin Xu
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

In this paper, we present an automatic knowledge base construction system from large scale enterprise documents with minimal efforts of human intervention. In the design and deployment of such a knowledge mining system for enterprise, we faced several challenges including data distributional shift, performance evaluation, compliance requirements and other practical issues. We leveraged state-of-the-art deep learning models to extract information (named entities and definitions) at per document level, then further applied classical machine learning techniques to process global statistical information to improve the knowledge base. Experimental results are reported on actual enterprise documents. This system is currently serving as part of a Microsoft 365 service.


Generating Coherent Summaries of Scientific Articles Using Coherence Patterns
Daraksha Parveen | Mohsen Mesgar | Michael Strube
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing


Topical Coherence for Graph-based Extractive Summarization
Daraksha Parveen | Hans-Martin Ramsl | Michael Strube
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing


Multi-document Summarization Using Bipartite Graphs
Daraksha Parveen | Michael Strube
Proceedings of TextGraphs-9: the workshop on Graph-based Methods for Natural Language Processing