IMG_20160720_195528 Measure of similarity between concepts
Similarity between concepts can be measured by computing distances in a taxonomy tree, and some people have written papers about it, as we see in the slide.
This was the fifth of the five meeting series on natural language processing, hosted by Women Who Code Austin at Rackspace. The instructor, Diana, introduced us to the basics of natural language processing. She did several demos of simple text analysis one can do with Python Natural Language Toolkit (NLTK). Examples of such actions are reading in the text, tokenizing it, and tagging parts of speech, which can involve a lot of interesting ambiguity.
A notable part of what we did was to extract metadata from Hillary Clinton's emails, which Diana obtained as a dataset from Kaggle.
Then we ventured deeper into natural language processing to discuss where and how it is used, including such fields as sentiment analysis. Diana talked about challenges present in those fields, such as for example determining similarity between concepts. We need to be able to handle that so as to extract accurate meanings from texts. This is where ontologies can be handy.
Here is my whole article on Natural Language Processing meeting series hosted by Women Who Code Austin.