In this article, you will learn about the technology that makes these applications tick, and you will learn how to develop natural language processing software of your own.
On Coreference Extraction from Identric’s Documents Business Understanding: Identrics would like to extract knowledge from unstructured text. One use case for that is to automate the process of information extraction from news articles. Some of Indentrics clients are from the finance industry and have a need to understand the impact of news on the valuation of […]
The objective of our task is extract parent-subsidiary relationship in text. For example, a news from techcruch says this, ‘Remember those rumors a few weeks ago that Google was looking to acquire the plug-and-play security camera company, Dropcam? Yep. It just happened.’. Now from this sentence we can infer that Dropcam is a subsidiary of Google. But there are million of companies and several million articles talking about them. A Human being can be tired of doing even 10! Trust me 😉 We have developed some cool Machine learning models spanning from classical algorithms to Deep Neural network do this for you. There is a bonus! We just do not give you probabilities. We also give out that sentences that triggered the algorithm to make the inference! For instance when it says Orcale Corp is the parent of Microsys it can also return that the sentence in its corpus ‘Oracle Corp’s Microsys customer support portal was seen communicating with a server’, triggered its prediction.
Today, with machine learning and large amounts of data harvested from social media and review sites, we can train models to identify the sentiment of a natural language passage with fair accuracy.
In this tutorial, you will learn how you can build a bot that can analyze the sentiment of emails that it receives and notify you about emails that may require your attention immediately.