Kaufland Case – Team3 No content
In this article I will describe my approach using bi-directional LSTM and eventually stacking them for creating deeper network resulting in better results.
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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.