Imperial College London

Professor Lucia Specia

Faculty of EngineeringDepartment of Computing

Chair in Natural Language Processing
 
 
 
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Contact

 

l.specia Website

 
 
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Location

 

572aHuxley BuildingSouth Kensington Campus

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Summary

 

Natural Language Processing - COMP97116

Aims

To provide the students the techniques and tools to devise and devel-op Natural Language Processing (NLP) components and applications. The course will cover the foundations, building blocks and applications of NLP, with an emphasis on the necessary linguistic intuitions as well as a broad coverage of statistical and deep learning models that can be used for language tasks. NLP is an important topic in Artificial Intelli-gence with a wide range of applications, from sentiment analysis to machine translation. Modern NLP is primarily based on statistical meth-ods and machine learning algorithms, where  linguistic information is provided by instances of uses of language. For most NLP tasks, state of the art approaches are based on neural models, which will be at the core of this module. However, significant attention will be given to the linguistic principles that underpin the field. 

More specifically, students will:

  • Gain familiarity with important linguistic concepts involved in language understanding and generation, from morphological analysis to pragmatics
  • Gain familiarity with, devise, implement and apply relevant pre-processing steps for natural language processing components and applications
  • Critically compare statistical and deep learning approaches for natural language processing
  • Map various well established techniques in machine learning to specific problems in natural language processing
  • Build, evaluate, critically analyze and improve models using ex-isting machine learning algorithms and frameworks (such as TensorFlow) for a range of natural language processing tasks, including: classification, structured prediction, sequence to se-quence labeling and generation
  • Devise, implement and evaluate classifiers for a range of natu-ral language processing tasks.

Role

Course Leader

Natural Language Processing - COMP97115

Aims

To provide the students the techniques and tools to devise and devel-op Natural Language Processing (NLP) components and applications. The course will cover the foundations, building blocks and applications of NLP, with an emphasis on the necessary linguistic intuitions as well as a broad coverage of statistical and deep learning models that can be used for language tasks. NLP is an important topic in Artificial Intelli-gence with a wide range of applications, from sentiment analysis to machine translation. Modern NLP is primarily based on statistical meth-ods and machine learning algorithms, where  linguistic information is provided by instances of uses of language. For most NLP tasks, state of the art approaches are based on neural models, which will be at the core of this module. However, significant attention will be given to the linguistic principles that underpin the field. 

More specifically, students will:

  • Gain familiarity with important linguistic concepts involved in language understanding and generation, from morphological analysis to pragmatics
  • Gain familiarity with, devise, implement and apply relevant pre-processing steps for natural language processing components and applications
  • Critically compare statistical and deep learning approaches for natural language processing
  • Map various well established techniques in machine learning to specific problems in natural language processing
  • Build, evaluate, critically analyze and improve models using ex-isting machine learning algorithms and frameworks (such as TensorFlow) for a range of natural language processing tasks, including: classification, structured prediction, sequence to se-quence labeling and generation
  • Devise, implement and evaluate classifiers for a range of natu-ral language processing tasks.

Role

Course Leader