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שנה"ל תש"ף

  עיבוד שפה טבעית (הקורס ינתן בשפה האנגלית)
  Natural Language Processing  
0368-3077
מדעים מדויקים
קבוצה 01
סמ'  א'0900-1200005 דאךשיעור פרופ ברנט יהונתן
הקורס מועבר באנגלית
ש"ס:  3.0

סילבוס מקוצר

Natural Language Processing (NLP) aims to develop methods for processing, analyzing and understanding natural language. The goal of this class is to provide a thorough overview of modern methods in the field of Natural Language Processing. The class will not assume prior knowledge in NLP.

  • We will cover:
    • Word representations
    • Language modeling (LSTMs, Transformers, etc.)
    • Tagging (sequence models)
    • Parsing (trees)
    • sequence-to-sequence models
Course description

This course will provide an overview of modern natural language processing, focusing on tasks where the output is a structure. We will cover language models, sequence models, and tree parsing models, focusing on the interface between structured prediction and deep learning.

 

סילבוס מפורט

מדעים מדויקים
0368-3077-01 עיבוד שפה טבעית (הקורס ינתן בשפה האנגלית)
Natural Language Processing
שנה"ל תש"ף | סמ'  א' | פרופ ברנט יהונתן

סילבוס מפורט/דף מידע

Natural Language Processing (NLP) aims to develop methods for processing, analyzing and understanding natural language. The goal of this class is to provide a thorough overview of modern methods in the field of Natural Language Processing. The class will not assume prior knowledge in NLP.

Syllabus:

  • word embeddings (word2vec and friends)
  • Language models
    • n-gram langauge modles
    • feed-forward LMs
    • RNN LMs
    • Vanishing and exploding gradients
  • Sequence models
    • LSTMs
    • GRUs
    • Transformers
  • Contextualized word representations (BERT, etc.)
  • Tagging
    • HMMs
    • Locally normalized log-linear models
    • Globally-normalized log-linear models
    • Viterbi
    • Forward-backward
    • Deep tagging models (BiLSTMs)
  • Syntactic Parsing
    • CFGs, PCFGs
    • CKY
    • Lexicalized PCFGs
    • Local and global log-linear parsing models
    • Deep syntactic parsing models
  • Sequence-to-sequence models
    • attention
    • pointer networks
    • semantic parsing
  • Sub-word models

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