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The purpose of the [course_title] course is to introduce you to the study of human language from a computational perspective. You will learn natural language processing covering syntactic, semantic and discourse processing models, emphasising machine learning or corpus-based methods and algorithms. You will learn the application of these methods and models in syntactic parsing, information extraction, statistical machine translation, dialogue systems, and summarisation. Other topics included in the course are EM Algorithm, Lexical Similarity, Global Linear Models, Dialogue Processing, Graph-based Methods for NLP Applications, and more.

Assessment

This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer need to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.

Certification

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Course Credit: MIT

Course Curriculum

Introduction and Overview 00:45:00
Parsing and Syntax I 01:30:00
Smoothed Estimation, and Language Modeling 00:45:00
Parsing and Syntax II 01:15:00
The EM Algorithm 01:15:00
The EM Algorithm Part II 00:45:00
Lexical Similarity 01:14:00
Lexical Similarity (cont.) 00:50:00
Log-Linear Models 00:55:00
Tagging and History-based Models 01:20:00
Grammar Induction 00:56:00
Computational Modeling of Discourse 01:00:00
Text Segmentation 00:45:00
Local Coherence and Coreference 01:15:00
Machine Translation 00:45:00
Machine Translation (cont.) 01:05:00
Machine Translation (cont.) 2 01:00:00
Machine Translation (cont.) 3 00:40:00
Graph-based Methods for NLP Applications 01:00:00
Word Sense Disambiguation 01:00:00
Global Linear Models 01:18:00
Global Linear Models Part II 00:42:00
Dialogue Processing 01:05:00
Dialogue Processing (cont.) 00:50:00
Text Summarization 00:50:00
Assessment
Submit Your Assignment 00:00:00
Certification 00:00:00

Course Reviews

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