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Monthly Challenge – Ontotext – Case

Monthly Challenge: https://www.datasciencesociety.net/events/text-mining-data-science-monthly-challenge/ Mentors’ Weekly Instructions: https://www.datasciencesociety.net/text-mining-data-science-monthly-challenge/ Real Business Problem Classification of companies into industry sectors is a fundamental task for unlocking advanced business […]

CASE Ontotext, Team CENTROIDA

This paper presents a DNN-based approach to learn entities relations from distant-labeled free text. The proposed approach presents task-specific data cleaning, which despite effective in removing textual noise is preserving semantics necessary for the training process. The cleaned-up dataset is then used to build a number of bLSTM attention-based DNN models, hyper-tuned using recall as an optimization objective. The resulting models are then joined into an ensemble that deliver our best result

Ontotext case – Team _A

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.