NATURAL LANGUAGE PROCESSING
Language is the primary medium through which human knowledge is created, shared, and preserved — yet the vast majority of it remains locked in unstructured text and speech, inaccessible to systematic analysis. INSTAR's NLP research program investigates how computational approaches can make language machine-legible without discarding the context, ambiguity, and nuance that give it meaning. Our work spans understanding, extraction, and interaction — grounded in the applied needs of science, enterprise, and public-benefit research.
Language Understanding & Analytics
Extracting meaning, structure, and insight from unstructured text and speech at scale requires more than pattern matching — it demands representations that capture intent, context, and domain knowledge. INSTAR investigates language understanding approaches suited to heterogeneous corpora: scientific literature, regulatory documents, conversational records, and multilingual sources. The goal is analytics that surface what matters, not just what is frequent.
Learn More
Information Extraction & Knowledge
Documents and conversations contain structured knowledge that conventional databases cannot capture. INSTAR researches methods for turning unstructured language into structured, actionable knowledge — including entity recognition, relationship extraction, document classification, and the construction of knowledge representations that can be queried, reasoned over, and kept current as source material evolves. Applied focus areas include scientific literature, legal and regulatory text, and institutional records.
Learn More
Conversational & Agentic Language Systems
Language interfaces are becoming the primary way humans delegate tasks to computational systems. INSTAR studies how language systems can communicate clearly, handle ambiguity, maintain coherence across multi-turn exchanges, and remain aligned with human intent — questions that matter for research assistants, domain-specific tools, and human-in-the-loop workflows. This theme connects closely to the Cognitive AI & Persona Systems lab, where language capability intersects with behavioral modeling and interaction design.
Learn MoreGrounded in Open Data
INSTAR NLP research draws on open corpora and community-maintained datasets that enable reproducible, verifiable language science.
Linguistic Data Consortium
LDC provides annotated corpora of text and speech that ground our language understanding and information extraction research in well-characterized, citable datasets.
Visit LDCCommon Crawl
Common Crawl's petabyte-scale open web archive is a foundational resource for large-scale language modeling experiments and multilingual corpus studies at INSTAR.
Visit Common CrawlMozilla Common Voice
Mozilla's crowd-sourced speech corpus supports INSTAR's spoken language research, providing multilingual voice data for acoustic modeling and speech-to-text development.
Visit Common VoiceData.gov
U.S. federal open data from Data.gov supplies domain-specific document collections for government text analytics, regulatory NLP, and public-sector information extraction studies.
Visit Data.govOUR PARTNERS
Join the INSTAR Fellowship
Doctoral and postdoctoral researchers working at the intersection of language, computation, and applied science are invited to explore the INSTAR Consortium Fellowship — a structured residency combining independent research with real-world deployment.