  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.
