Source: Deep Learning on Medium PrerequisitesCenk ÇorapcıMay 9Despite the fact that this won’t be a very technical post, it would be nice to know a few things about,Deep Learning, e…

Implemented in one code library.

This paper presents a named entity extraction system for detecting attributes in product titles of eCommerce retailers like Walmart. The absence of syntactic structure in such short pieces of text makes extracting attribute values a challenging problem. We find that combining sequence labeling algorithms such as Conditional Random Fields and Structured Perceptron with a curated normalization scheme produces an effective system for the task of extracting product attribute values from titles. To keep the discussion concrete, we will illustrate the mechanics of the system from the point of view of a particular attribute - brand. We also discuss the importance of an attribute extraction system in the context of retail websites with large product catalogs, compare our approach to other potential approaches to this problem and end the paper with a discussion of the performance of our system for extracting attributes.

The Elasticsearch 7.3 release brings support for using vectors in document scoring. This post explores how text embeddings and vector fields can be used to support similarity search.