NLP Classifier Models & Metrics
Natural Language Processing is the capability of providing structure to unstructured data which is at the core of developing Artificial Intelligence centric technology. NLP classification helps us tag data with categories such as sentiments expressed in reviews or concepts associated with texts.
In this talk I will go into details of NLP classifications:
1. importance of data collection
2. a deep dive into models, and
3. the metrics necessary to measure the performance of the model.
In order to gain a proper understanding of modeling, I will explain traditional NLP techniques using TFIDF approaches and go into details of different deep learning architectures such as feed-forward neural networks and convolutional neural networks (CNN).
I will talk about the different components of building a network such as loss function, activation function. Along with these concepts, I will also show code snippets in Keras to build the classifier.
I will conclude with some of the metrics commonly used in measuring the performance of the classifier.
At the end of the presentation, the audience will be armed with the tools and concepts necessary to build their own NLP classifier.
Staff Data Scientist at Chegg
Sanghamitra Deb is a Staff Data Scientist at Chegg, she works on problems related to school and college education to sustain and improve the learning process.
Her work involves recommendation systems, computer vision, graph modeling, deep NLP analysis, data pipelines, and machine learning.
Previously, Sanghamitra was a data scientist at Accenture where she worked on a wide variety of problems related to data modeling, architecture, and visual storytelling.
She is an avid fan of python and has been programming for more than a decade.