Understanding the types of entities expressed in a search query (Company, Skill, Job Title, etc.) enables more intelligent information retrieval based upon those entities compared to a traditional keyword-based search. Because search queries are typically very short, leveraging a traditional bag-of-words model to identify entity types would be inappropriate due to the lack of contextual information. We implemented a novel entity type recognition system which combines clues from different sources of varying complexity in order to collect real-world knowledge about query entities. We employ distributional semantic representations of query entities through two models: 1) contextual vectors generated from encyclopedic corpora like Wikipedia, and 2) high dimensional word embedding vectors generated from millions of job postings using Spark MLlib. In order to enable real-time recognition of entity types, we utilize Apache Solr to cache the embedding vectors generated by Spark MLlib. This approach enable us to recognize entity types for entities expressed in search queries in less than 60 milliseconds which makes this system applicable for real-time entity type recognition.
Khalifeh AlJadda holds Ph.D. in computer science from the University of Georgia (UGA), with a specialization in machine learning. He has experience implementing large scale, distributed machine learning algorithms to solve challenging problems in domains ranging from Bioinformatics to search and recommendation engines. He is the lead data scientist on the search data science team at CareerBuilder, which is one of the largest job boards in the world. He leads the data science effort to design and implement the backend of CareerBuilder's language-agnostic semantic search engine leveraging Apache Spark and the Hadoop ecosystem.