Attribute Extraction from Product Titles in eCommerce
This addresses the challenge of processing large product catalogs for retail websites like Walmart, though it appears incremental as it builds on existing sequence labeling methods.
The paper tackles the problem of extracting attribute values from unstructured product titles in eCommerce by combining sequence labeling algorithms (Conditional Random Fields and Structured Perceptron) with a normalization scheme, resulting in an effective system for this task.
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.