Members of the Stanford NLP Group pursue research in a broad variety of topics:

Computational Semantics

• Extraction of meaning representations from text, ranging from shallow representations like named entities and thematic roles, to deep structures like quantification in logic. Research topics include:

  • Detecting contradictions in text
  • Named Entity Recognition (NER) and Information Extraction (IE)
    • The Stanford Edinburgh Entity Recognition (SEER) Project
    • The Edinburgh And Stanford Information Extraction (EASIE) Project
  • Shallow Semantic Parsing
  • Question Answering (QA)
  • Knowledge Representation from Text
    • The NLKR Project: solving natural-language logic puzzles
    • Exploring the Utility of ResearchCyc in natural-language applications
  • Semantic Taxonomy Induction
  • Word Sense Disambiguation (WSD)

Parsing & Tagging

• Algorithms for assigning part of speech and syntactic structure, emphasizing probabilistic and discriminative models. Research topics include:

  • Probabilistic Parsing
  • Part-of-speech (POS) tagging

Multilingual NLP

• A variety of NLP investigations in Chinese, Arabic, and German, including tagging, segmentation, probabilistic syntactic parsing, and semantic role parsing. Research topics include:

  • Chinese NLP
  • Arabic NLP
  • German NLP

Unsupervised Induction of Linguistic Structure

• Probabilistic and other corpus-based algorithms for learning syntactic, morphological, and phonological structures from corpora. Research topics include:

  • Grammar Induction
  • Morphology & Phonology Induction
  • Semantic Taxonomy Induction

Other

• A number of other research areas in the computational study of human language. Research topics include:

  • Personalized PageRank algorithms
  • Clustering Models
  • Computational Lexicography: Kirrkirr
  • Text Categorization
  • Discriminative Models

Links

Other groups at Stanford doing NLP-related research:

Other links: