Visualising text cluster in 2D using Sentence Context
Nikita Sharma
Data Science InternIn the last post, we talked about how we can cluster the sentence using Bert. This post is about how we can visualize the text cluster in 2-dimensions.
In the last post, we talked about how we can cluster the sentence using Bert. This post is about how we can visualize the text cluster in 2-dimensions.
I would like to share my project story, with challenges faced and learnings during revamp and transformation to build readily available templates that could facilitate with forms autofill and swiftly generate refined and cleaner data to assist in future ML solutions, adding value to forms automation.
I would like to share my perspective and experience on selecting ‘Topic Modeling’ algorithm for Short text form titles i.e., Latent Dirichlet Allocation and Gibbs Sampling Dirichlet Multinomial Mixture and how the modelling is done along with training of the model.
This post is about identifying context captured in text sentences and grouping/clustering similar sentences together. Understanding the context means that we need to understand every possible way a sentence could be written.
Here we will use BERT to identify the similarity between sentences and then we will use the Kmeans clustering approach to cluster the sentences with the same context together.