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

Information Extraction

Semantic Parsing

  • We are interested in mapping utterances to deep meaning representations that take into account the compositional and quantification structure of language.

Sentiment and Social Meaning

Dropout Learning and Feature Noising

  • Algorithms that aim to preventing feature co-adaptation using fast dropout training by sampling from or integrating a Gaussian approximation, or equivalently as adaptive regularizers, which can be generalized to semi-supervised learning settings.

Deep Learning in Natural Language Processing

  • The use of continuous-space distributed representations (neural nets) for tackling various problems in natural language processing and vision, including parsing, sentiment analysis and paraphrase detection.

Parsing & Tagging

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

Machine Translation

  • Language modeling, re-ordering models, phrase extraction techniques, syntactic methods, and better training for statistical machine translation:

Dialog and Speech Processing

The History of Computational Linguistics

Unsupervised and Semisupervised Learning of Linguistic Structure

Multilingual NLP

Past Projects

Links

  • Computational Linguistics @ Stanford
  • Other groups at Stanford doing NLP-related research:

  • The Computational Semantics Lab at CSLI
  • The LinGO/LKB project at CSLI
  • Martin Kay

  • Other links:

  • Statistical NLP resources
  • Linguistics resources