NLPExplorer

NLPExplorer indexes, searches, and visualizes NLP research literature to provide analytic insights into papers, authors, venues, topics, datasets, and temporal trends.

Key Features:

• Automatic Indexing and Search: Automatically indexes an extensive collection of NLP-related scientific articles and supports search over that index.
• Visualization Tools: Provides interactive visualizations that represent relationships and temporal trends in NLP research.
• Curated Topical Categories: Uses manually curated, coarse-grained, non-exclusive categories including Linguistic Targets (Syntax, Discourse), Tasks (Tagging, Summarization), Approaches (Unsupervised, Supervised), Languages (English, Chinese), and Dataset Types (News, Clinical Notes).
• Young Popular Authors: Identifies and curates lists of emerging authors making significant contributions to NLP.
• Popular URLs and Datasets: Surfaces frequently referenced URLs and datasets within the NLP literature.
• Topically Diverse Papers: Highlights papers that span multiple topical categories within NLP.
• Recent Popular Papers: Identifies recently influential papers in the domain.
• Temporal Statistics: Computes yearwise popularity trends for topics, datasets, and seminal papers.

Scientific Applications:

• Research Trend Analysis: Quantifies yearwise popularity trends for topics, datasets, and seminal papers to monitor evolving research directions.
• Topic Categorization and Mapping: Assigns papers to curated categories (Linguistic Targets, Tasks, Approaches, Languages, Dataset Types) to map topical structure in NLP.
• Author Impact and Discovery: Identifies emerging authors and measures author-centric contributions within the field.
• Resource and Dataset Discovery: Surfaces frequently referenced URLs and datasets to aid identification of common resources.
• Cross-topic Paper Identification: Detects and highlights papers that span multiple topical categories to reveal interdisciplinary work.

Methodology:

Implements automatic indexing, search, and visualization, uses manually curated coarse-grained non-exclusive topical categories, and computes yearwise popularity statistics for topics, datasets, and papers.