- The course is primarily aimed at helping to start use the Python for Text Analysis and will allow them to plan, design, and conduct natural language processing. The course focuses on the following competencies: programming and natural language processing. The course contains three main topics – Getting started with Python, NLTK and Categorizing and Tagging.
- Ability to create a program on Python to made simple program using lists, dictionaries and strings.
- Ability to use NLTK library in your program on Python
- Ability to create a program on Python to made frequency analysis (words, n-grams, stems, normalized words, parts of speech, sentiment markers for the set of English and Russian texts.
- Categorizing and TaggingUsing existing part-of-speech taggers NLTK build and MyStem. Mapping Words to Properties Using Python Dictionaries. N-Gram Tagging
- NLTK Library for text analyticsGetting Started with NLTK. Searching Text. Counting Vocabulary. Frequency Distributions. Collocations and Bigrams (N-Grams). The NLP Pipeline: Normalizing Text. Sentence Segmentation.
- Computing with Language: Texts and Words.Getting started with Python (IDE, PyCharm, Jupyter Notebook). Variables and Data structures: lists, dictionaries, strings. Conditionals. Operating on Every Element. Nested Code Blocks. Looping with Conditions. Regular Expressions for Detecting Word Patterns. Accessing Text from the Web and from Disk. Writing Results to a File
- Wagner, W. (2010). Steven Bird, Ewan Klein and Edward Loper: Natural Language Processing with Python, Analyzing Text with the Natural Language Toolkit. Language Resources & Evaluation, 44(4), 421–424. https://doi.org/10.1007/s10579-010-9124-x
- Beysolow, T. (2018). Applied Natural Language Processing with Python : Implementing Machine Learning and Deep Learning Algorithms for Natural Language Processing. [Berkeley, CA]: Apress. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1892182
- Bhavsar, K., Kumar, N., & Dangeti, P. (2017). Natural Language Processing with Python Cookbook : Over 60 Recipes to Implement Text Analytics Solutions Using Deep Learning Principles. Packt Publishing.
- Dipanjan Sarkar. (2019). Text Analytics with Python : A Practitioner’s Guide to Natural Language Processing: Vol. Second edition. Apress.
- Nirant Kasliwal. (2018). Natural Language Processing with Python Quick Start Guide : Going From a Python Developer to an Effective Natural Language Processing Engineer. Packt Publishing.
- Perkins, J. (2010). Python Text Processing with NLTK 2.0 Cookbook : Over 80 Practical Recipes for Using Python’s NLTK Suite of Libraries to Maximize Your Natural Language Processing Capabilities. Packt Publishing.