課程說明(Course Desc<x>ription)
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<br>This course covers the fundamentals of natural language processing with&#160;R32;emphases on using statistical 
<br>techniques, machine learning, very large&#160;R32;corpora to handle unrestricted text data, in particular on-line 
<br>information,&#160;R32;electronic communication and the World Wide Web.&#160;R32;R32;We will explore the problems 
<br>and solutions 
<br>in lexical, syntactic and&#160;R32;semantic analyses of text and show various techniques that are useful&#160;R32;for a 
<br>wide 
<br>range of applications, including machine translation,&#160;R32;information retrieval, and text categorization.&#160;
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<br>指定用書(Text Books)
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<br>Speech and Language Processing (3rd ed. draft) by Dan Jurafsky and James H. Martin
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<br>Draft chapters in progress, August 19, 2019
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<br>https://web.stanford.edu/~jurafsky/slp3/
<br>https://web.stanford.edu/~jurafsky/slp3/ed3book.pdf
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<br>
<br>參考書籍(References)
<br>
<br>TBA
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<br>教學方式(Teaching Method)
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<br>教學進度(Syllabus)
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<br>Lectures, demos, assignments
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<br>Week | Desc<x>ription | Note
<br>
<br>1   9/9    | 課程簡介 Introduction to NLP | 開學
<br>2   9/16  | 程式語言、工具介紹&#160;| 10/10(四)放假
<br>3   9/23  | 語言學基本概念 Linguistics Essentials: parts of speech, phrase structure, semantics
<br>4   9/30  | 機率理論、語言模型&#160;
<br>5   10/7   | 文件分類、貝式與迴歸分析 Text Classification, Bayes and Log Reg classification
<br>6   10/14 | 詞彙語意解歧 Word Sense Disambiguation&#160;
<br>7   10/21 | 向量語意學 &#160;Vector Semantics
<br>8   10/28 | 類神經網路及語言模型 Neural Networks, Part-of-speech Tagging&#160;
<br>9   11/4    | 文法和剖析器 Probabilistic Context Free Grammar and Parsing&#160;
<br>10  11/11  | 語意角色標示
<br>11  11/18  | 自動問答&#160;
<br>12  11/25 | 聊天機器人、對話代理人
<br>13  12/2  | 機器翻譯&#160;
<br>14  12/9  | 其他自然語言處理應用
<br>15  12/16 | TBA
<br>16  12/23 | TBA
<br>17  12/30 | TBA
<br>18  1/6     | TBA 期末考週
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<br>成績考核(Evaluation)
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<br>作業、考試
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<br>可連結之網頁位址 相關網頁(Personal Website)
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<br>TBA