Improving Search in Workday Products using Natural Language Processing

Improving Search in Workday Products using Natural Language Processing

Tuesday, June 19
2:50 PM - 3:30 PM
Executive Ballroom 210D/H

Workday is a leading provider of cloud-based enterprise software products such as Human Capital Management, Talent, Finance, Student, Planning etc. These products produce a wealth of natural language data. However, this data is unstructured and denormalized. Retrieving relevant information from such data is a challenging task. Using simple index-based search methods can only take us so far. The Data Science team at Workday is determined to apply Machine Learning and AI to make search better across Workday’s products.
In this session, we present to you, how we use word embeddings to normalize the data and add structure to it. We will also talk about using word representations to make search intelligent. The specific use cases we will discuss are adding synonyms detection and entity-recommendation.
In this talk, we will focus on the word-embeddings techniques explored, metrics used to evaluate Natural Language Processing Models, tools built, and future work as a part of improving search.

SPEAKERS

Namrata Ghadi
Software Development Engineer (Data Science)
Workday Inc
Namrata Ghadi is a Software Development Engineer (ML and Data Science) in Workday’s Syman team. She has been working on ML and DS based projects for 2+ years and as a Software Engineer for 6+ years. Namrata has a MS in CS from Carnegie Mellon University.
Adam Baker
Sr Software Engineer
Workday
Adam Baker is a Senior Software Development Engineer (ML and Data Science) in Workday’s Machine Learning team. He's worked on computational linguistics in graduate school and in NLP and ML for one year at Workday. He's worked in software development for 7 years. He has a MA in Linguistics from University of Chicago and B.S. in CS from Ohio State University.