Multi-label/Class Question Classification for Natural Questions.

Prashant Niranjan (~prashant30)


Description:

Word Embedding

To convert your words into numbers. To process machine learning algorithm on a sentence. These words cannot interpreted by machine learning algorithm. So we need to convert these words to numbers.

1) Word2Vec 2) Count Vectorizer 3) TF-IDF Vectorizer

Multi Class/ Multi-label Classification

Binary Classification: Classification tasks with two classes. Multi-class Classification: Classification tasks with more than two classes. Some algorithms are designed for binary classification problems. Examples include: Logistic Regression , Perceptron , Support Vector Machines, As such, they cannot be used for multi-class classification tasks, at least not directly. Two different examples of this approach are the One-vs-Rest and One-vs-One strategies.

One-vs-rest (OvR for short, also referred to as One-vs-All or OvA) is a heuristic method for using binary classification algorithms for multi-class classification. It involves splitting the multi-class dataset into multiple binary classification problems.

One-vs-One (OvO for short) is another heuristic method for using binary classification algorithms for multi-class classification. Like one-vs-rest, one-vs-one splits a multi-class classification dataset into binary classification problems.

Prerequisites:

Basics of machine learning & knowledge of python

Speaker Info:

Prashant Y. Niranjan is an Assistant Professor in the Department of Computer Science and Engineering, KLS Gogte Institute of Technology, Belagavi, Karnataka, India. He completed his BE in Information Science and Engineering from Visvesaraya Technological University Belagavi in 2011 and MTech in Computer Science and Engineering from Visvesaraya Technological University Belagavi in 2014. His working in the field of Natural Language Processing & Machine Learning.

Section: Data Science, Machine Learning and AI
Type: Talks
Target Audience: Advanced
Last Updated: