Hello and welcome back to the NLP Tutorials blog post series. We shall cover an interesting topic in the unsupervised learning section under NLP — Topic Modelling. Topic Modelling is a process wherein the algorithm is able to automatically detect the topics covered/occuring in a given text extract/document. It can segregate the topics in a vector space and provides access to them via ‘topic clusters’ for viewing the terms contributing towards a topic. One major application would be to have a system to find out the latest trending topic or emerging topic for a big corpus.
Author: Applied Singularity
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Hello and welcome back to the NLP Tutorials series. In the last two articles, we have looked into a few applications in the NLP domain , NER and Summarization, which can be found in various real-world settings. In this article, we shall get back to the understanding one of the latest and most interesting architecture s - Infinite-former aka Infinite Transformer! The paper vouches for an attention mechanism which can cater to unbounded long-term contextual memory, which is groundbreaking since many architectures have tried solving the complexity and memory constraints of vanilla transformers albeit with a limited memory (longer sequences but limited). The authors also introduced sticky memories which are able to model very long contexts with a fixed computational requirement.
Welcome back to another article in the NLP Tutorials series! Continuing our quest towards mastery in NLP, we will be looking at an exciting application in NLP — Text Summarization. In the current times where data is being generated at a massive scale, at times we want to shrink them and have an overview only than the entire length. This is where text summarization plays a key role in condensing the document/data into a concise form. It is a challenging problem to solve since it depends on the cognitive intellect, language understanding and domain knowledge. In this article, we shall have a brief overview on the types of Text Summarization and attempt to implement a basic model ourselves using the NLP concepts and libraries we have come across so far.
In this article we won’t be looking at high-end architectures but instead explore a key concept in the NLP domain — Named Entity Recognition (NER). We might have heard of this term as one of the important concepts that has a lot of applications in real-world scenarios. Let’s understand what it means and how we can create a NER model using a few popular NLP libraries.