Language and Computer

Course Description

This course provides an introduction to Language and Computer in terms of preliminaries for deep learning-based Computational Linguistics and Natural Language Processing (NLP). Text Processing occupies the core of NLP, which inevitably comes with massive data collection, delicate cleaning and efficient handling. The first part of the class focuses on the data preparation including data collection and cleaning and the second part covers various topics from traditional NLP. Although currently deep learning methodologies applied to NLP prevail, traditional notions and theoretical background are still essential to text processing. We will take advantage of modules from various python and related resources. Through lectures and programming assignments students will learn the necessary data processing tricks and theoretical background for making NLP work on practical problems.

Updates

Please install python3.x, nltk, and jupyter notebook
Get ready for Google colab!

Useful Sites

Lectures

Textbook

Speech and Language Processing (3rd ed. Draft) by Dan Jurafsky and James H. Martin

Natural Language Processing With
Python

Natural Language Processing with Python by Steven Bird, Ewan Klein, and Edward Loper

Syllabus

	Date	Topics	Related Materials and Resources	Assignments
1	9/1-9/7	Introduction to Language and Computer Natural Language Processing is Fun! Preliminaries: Python Jupyter notebook Google Colabatory	Python: Pythons for Data Science-From Scratch Python3 Tutorial Python for beginners: Learning Python:From zero to Hero Jupyter notebook: Jupyter notebook for beginners-A tutorial Bring the best out of Jupyter notebooks for Data science-Enhance jupyter notebook’s productivity with these tips & tricks Jump out of the Jupyter Notebook with nbconvert Jupyter Notebook Extensions Google Colab: Primer for Learning Google Colab Getting Started with Google CoLab	Install Python 3.x and Natural Language Tool Kit with Python
2	9/8-9/14	Data Preparation collection Web scraping with Python and BeautifulSoup Web Scraping, Regular expressions, and Data visualization: Doing it all in python Quick and Dirty Data Gathering with Python	BeautifulSoup Documentation Regex: Regex tutorial- A quick cheatsheet by examples
3	9/15-9/21	Data Preprocessing Text Preprocessing in Python: Steps, Tools and Examples Pandas: Minimally Sufficient Pandas A Quick Introduction to the "Pandas" Quick Tip: The easiest way to grab data out of a web page in Python Visualization: Introduction to Data Visualization in Python Advanced Visualization for Data Scientists with Matplotlib	Pandas documentation Matplotlib documentation Matplotlib.pyplot functions Seaborn: statistical data visualiazation Python Seaborn Tutorials for Beginners	Study Kevin Markham's Pandas-Videos Pandas Homework Assignment 1 (refer to ETL)
4	9/22-9/28	Data Preprocessing Text Preprocessing in Python: Steps, Tools and Examples Pandas: Minimally Sufficient Pandas A Quick Introduction to the "Pandas" Quick Tip: The easiest way to grab data out of a web page in Python Visualization: Introduction to Data Visualization in Python Advanced Visualization for Data Scientists with Matplotlib	Pandas documentation Matplotlib documentation Matplotlib.pyplot functions Seaborn: statistical data visualiazation Python Seaborn Tutorials for Beginners	Matplotlib Homework Assignment 2(refer to ETL)
5	9/29-10/5	Text Processing: Tokenization Part-of-Speech Lemmatization Stemming Stop Words Phrase Word Recognition Spell Checking String Matching Theoretical Background Regular Expressions, Text Normalization, Edit Distance	NLP-DataSets: nlp-datasets Naver Sentiment Movie Corpus NLTK: NLTK Python Tutorial NLTK with Python3 for Natural Language Processing Python Data Science Getting Started Tutorial: NLTK	Basic NLP with NLTK
6	10/6-10/12	Text Processing: Tokenization Part-of-Speech Lemmatization Stemming Stop Words Phrase Word Recognition Spell Checking String Matching Theoretical Background Regular Expressions, Text Normalization, Edit Distance	NLP-DataSets: nlp-datasets Naver Sentiment Movie Corpus Regex: Regex tutorial- A quick cheatsheet by examples NLTK: NLTK Python Tutorial NLTK with Python3 for Natural Language Processing Python Data Science Getting Started Tutorial: NLTK
7	10/13-10/19	Language Modeling with N-Grams	Learning NLP Language Models with Real Data
8	10/20-10/26	Language Modeling with N-Grams Entropy Entropy Intro Maximum Entropy Models	Learning NLP Language Models with Real Data Visual Information Theory Information Theory
9	10/27-11/2	Entropy Entropy Intro Maximum Entropy Models	Visual Information Theory Information Theory
10	11/3-11/9	Naive Bayes Classification	Bayes' Theorem: The Holy Grail of Data Science
11	11/10-11/16	Linear Regression and Logistic Regression Notebook	Linear Regression From Scratch with Python Logistic Regression for Machine Learning
12	11/17-11/23	Vector Semantics Euclidean Distance, Cosine Similarity and Jaccard Similarity
13	12/1-12/7	Sebastian Ruder의 On word Embeddings Part1, 2, 3, 4: Part 1: On word embeddings Part 2: Approximating the Softmax Part 3: The secret ingredients of word2vec Part 4: A survey of cross-lingual embedding models Part 5: Word Embeddings in 2017- Trends in Future directions A hands-on Intuitive Approach to Deep Learning Methods for Text Data - Word2Vec, Glove, and FastText The Current Best of Universal Word Embeddings and Sentence Embeddings Notebook Gensim Word2Vec Tutorial Gensim Tutorial	Word Embeddings
14	12/8-12/14	Sebastian Ruder의 On word Embeddings Part1, 2, 3, 4: Part 1: On word embeddings Part 2: Approximating the Softmax Part 3: The secret ingredients of word2vec Part 4: A survey of cross-lingual embedding models Part 5: Word Embeddings in 2017- Trends in Future directions A hands-on Intuitive Approach to Deep Learning Methods for Text Data - Word2Vec, Glove, and FastText The Current Best of Universal Word Embeddings and Sentence Embeddings Notebook Gensim Word2Vec Tutorial Gensim Tutorial	Word Embeddings
15	12/15-12/21	Final Test

100.130: Language and Computer

Course Description

Updates

Useful Sites

Textbook

Syllabus