Friday March 2, 2017
Natural language processing (NLP) refers to the methods and technologies used to allow computers to process, understand, and perform tasks using human language. Common NLP tasks include sentiment analysis, part-of-speech tagging, named entity recognition, machine translation, document classification, clustering, and topic extraction. This course will introduce fundamental concepts in NLP including word and document representation, text processing, document classification, document similarity, and clustering, and dimensionality reduction. The course will be taught using jupyter notebooks in python. NLP tools covered will be sci-kit learn and ntlk.
Who: This course is targeted primarily at graduate students and researchers who have some experience with machine learning and python, but are new to NLP.
Requirements: Participants must bring a laptop with a few specific software packages installed (see Pre-Workshop Instructions).
Prerequisites: A previous course in programming is strongly recommended. Experience with basic machine learning is recommended.
Contact: Please mail [email protected] for more information.
| Time | |
|---|---|
| 8:30-9:00 | Sign-in (coffee & bagels) |
| 9:00-10:30 | Text Processing and Document Classification |
| 10:30 - 10:45 | Break |
| 10:45-1:00 | Document Similarity and Clustering |
- Introduction/Preparation
- Common NLP Tasks
- Word and Document Representation
- Text Processing
- Document Classification
- Text Processing
- TFIDF
- Evaluation
- Clustering
- Document Similartiy
- Dimensionality Reduction
- Topic Modeling
- Visualization
You will need the following programs to run the jupyter notebook:
- Git
- Python
- Python modules (see requirements.txt)
Python modules can be installed using either anaconda (recommended for beginners) or pip.
You will need to install git. After installing git, run the following command to clone the workshop repository:
git clone https://github.com/UCIDataScienceInitiative/NLP.git
You will need python installed. If you do not already have python installed, we recommend downloading Anaconda, which will include python and the modules required for this workshop. The notebook will run with versions 3.5 and 2.9.
This is the recommended method of installation for users newer to python, or those that have not used pip. Anaconda should have all required modules. After installing Anaconda, run
conda update conda
to update all modules. If the LatentDirchletAllocation method will not import, it may help to update scikit-learn by running
conda update scikit-learn
To run this script you will need the packages listed in requirements.txt. To install run
pip install -r requirments.txt
in the command line.
I was able to get the notebook running using python 3.5 and 2.9 on both Mac and Windows machines. If you are having trouble installing any of the required software, please come to the workshop a few minutes early. Additionally, we will have scheduled setup time to address any problems.
The data are comments and metadata from two mental health subreddits /r/SuicideWatch and /r/depression. The data were filtered from this dataset.
To unzip the data, run gunzip RC_2015-05.json.gz
If you would like to see the presentation in slide form, rather than an ipython notebook, you can run the following commands:
jupyter nbconvert --to slides IntroNLP.ipynb --post serve