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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.
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.
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Course Credit: MIT
|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 (cont.)||00:50:00|
|Tagging and History-based Models||01:20:00|
|Computational Modeling of Discourse||01:00:00|
|Local Coherence and Coreference||01:15:00|
|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 (cont.)||00:50:00|
|Submit Your Assignment||00:00:00|
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