Understanding the current research trends, problems, and their innovative solutions remains a bottleneck due to the ever-increasing volume of scientific articles. In this project, we propose NLPExplorer, a completely automatic portal for indexing, searching, and visualizing Natural Language Processing (NLP) research volume. NLPExplorer presents interesting insights from papers, authors, venues, and topics. In contrast to previous topic modelling based approaches, we manually curate five course-grained non-exclusive topical categories namely Linguistic Target (Syntax, Discourse, etc.), Tasks (Tagging, Summarization, etc.), Approaches (unsupervised, supervised, etc.), Languages (English, Chinese, etc.) and Dataset types (news, clinical notes, etc.). Some of the novel features include a list of young popular authors, popular URLs and datasets, list of topically diverse papers and recent popular papers. Also, it provides temporal statistics such as yearwise popularity of topics, datasets, and seminal papers. To facilitate future research and system development, we make all the processed dataset accessible through API calls. The current system is available at http://nlpexplorer.org.

For full paper, refer Arxiv Link

For detailed description, scroll down the home page of http://nlpexplorer.org.

@misc{parmar2019nlpexplorer,
    title={NLPExplorer: Exploring the Universe of NLP Papers},
    author={Monarch Parmar and Naman Jain and Pranjali Jain and P Jayakrishna Sahit and Soham Pachpande and Shruti Singh and Mayank Singh},
    year={2019},
    eprint={1910.07351},
    archivePrefix={arXiv},
    primaryClass={cs.IR}
}

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