Unsupervised NLP: Methods and intuitions behind working with unstructured texts

In the fledgling, yet advanced, fields of Natural Language Processing (NLP) and Natural Language Understanding (NLU) — Unsupervised learning holds an elite place. That’s because it satisfies both criteria for a coveted field of science — it’s ubiquitous but it’s quite complex to understand at the same time.

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InduNet v1.0 — An industry predictor using company descriptions

Natural Language Processing (NLP) is a large area of research with many relevant applications for businesses. Being able to take in arbitrary text and extracting sentiment, performing translation, auto-suggest/correct are some typical use cases seen. But the applications are of course endless.

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Stock trend prediction from News Sentiment

Companies sell their shares on the stock market, putting the company squarely in the public domain. While the impact on stock value has various causes and effects, a big factor in price change is the way a company is perceived. Sentiment from news can be used as an predictive indicator of trend. In tis article we give a brief overview of how we analyze sentiment.

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Training better Deep Learning Models for Structured Data using Semi-supervised Learning

In this post, we will use semi-supervised learning to improve the performance of deep neural models when applied to structured data in a low data regime. We will show that by using unsupervised pre-training we can make a neural model perform better than gradient boosting.

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How to use Machine Learning Models to predict Loan eligibility

Build predictive models to automate the process of targeting the right applicants.
Loans are the core business of banks. The main profit comes directly from the loan’s interest. The loan companies grant a loan after an intensive process of verification and validation. However, they still don’t have assurance if the applicant is able to repay the loan with no difficulties.

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Comprehensive guide to Model selection

We recommend a four step process for the model phase of model selection: Evaluate ~
Assess ~ Review ~ Analyze. We’ll illustrate this process with a quick example. On a recent project, our team at Atlas Research was tasked with developing a tool for named entity recognition (NER), a subtask within the field of natural language processing.

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