Deep neural networks have been proven to work extremely well when provided with a large number of data points. However, a dearth of data for building large search engines has been a primary issue for most engines. Traditional approaches to searching in text data involved domain understanding and keyword mapping. These provided search engines with a knowledge graph that included sufficient information about the topics, so that the engine would be able to find answers. The search engine would then also be able to expand to new topics, by obtaining the relationships between topics.
In this chapter, we will explore how to build a search engine using machine learning (ML), and we will use it for tasks such as matching and deduplication. In order to understand how deep learning can improve our searches, we will build baseline methods, using traditional...
