Automatically suggests annotations for new text based on models trained directly on your manually labeled data, significantly speeding up the labeling process.
Provides a shared workspace where multiple annotators can work on the same documents simultaneously, with features for task assignment, review workflows, and consensus tracking.
Allows users to define custom entity types, relations, and classification labels through an intuitive web interface without writing any code or configuration files.
Supports the creation of dictionaries (lists of terms) and regex-like rules to automatically pre-annotate documents, which can then be refined manually or used to bootstrap AI models.
Offers a full REST API for programmatic document upload, annotation retrieval, and project management, alongside exports in standard NLP formats like JSON, CoNLL, and spaCy.
Tracks all changes to annotations, including who made them and when, allowing teams to revert to previous versions and maintain a complete history of dataset evolution.
Researchers and bioinformaticians use Tagtog to extract entities like genes, proteins, diseases, and chemical compounds from scientific publications and clinical notes. By training custom models on domain-specific corpora, they can rapidly build datasets for relation extraction (e.g., drug interactions) and accelerate discoveries. This enables systematic reviews and meta-analyses at scale, turning unstructured text into structured knowledge.
Law firms and legal tech companies employ Tagtog to annotate contracts, court rulings, and regulatory documents for clauses, parties, obligations, and risks. The collaborative platform allows legal teams to define their own annotation schemas and review each other's work. The resulting labeled data trains NLP models to automate routine contract analysis, reducing manual review time and improving consistency.
Product managers and market researchers upload customer reviews, survey responses, and social media posts to Tagtog. They annotate text for specific product features, sentiment polarity, and emerging issues. The AI-assisted features help scale annotation across large volumes of feedback. The exported datasets train models to automatically categorize and prioritize customer insights, informing product development and support strategies.
Students and academics in computational linguistics use Tagtog to create gold-standard datasets for novel NLP tasks, such as detecting linguistic phenomena or annotating low-resource languages. The no-code interface allows them to design complex annotation layers without software development overhead. Collaboration features facilitate work among research groups, while version control ensures the dataset's integrity for publication and sharing.
Financial analysts and fintech companies annotate earnings reports, news articles, and SEC filings to extract entities like company names, financial metrics, and market events. By building custom models, they automate the monitoring of specific triggers or sentiments affecting investments. The structured data feeds into dashboards and alert systems, enabling faster, data-driven decision-making in volatile markets.
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