[AI Seminar] AI Seminar sponsored by Apple -- Chenyan Xiong -- March 06

[Adams Wei Yu]

Dear faculty and students,

We look forward to seeing you next Tuesday, March 06, at noon in *NSH 1507* for
AI Seminar sponsored by Apple. To learn more about the seminar series,
please visit the AI Seminar webpage <http://www.cs.cmu.edu/~aiseminar/>.

On Tuesday,  Chenyan Xiong <https://www.cs.cmu.edu/~cx/> will give the
following talk:

Title: Text Representation, Retrieval, and Understanding with Knowledge
Graphs

Abstract:

Search engines and other information systems have started to evolve from
retrieving documents to providing more intelligent information access.
However, the evolution is still in its infancy due to computers' limited
ability in representing and understanding human language. This talk will
present my work addressing these challenges with knowledge graphs. The
first part is about utilizing entities from knowledge graphs to improve
search. I will discuss how we build better text representations with
entities and how the entity-based text representations improve text
retrieval. The second part is about better text understanding through
modeling entity salience (importance), as well as how the improved text
understanding helps search under both feature-based and neural ranking
settings. This talk concludes with future directions towards the next
generation of intelligent information systems.


Bio:

Chenyan Xiong is a Ph.D. candidate at Carnegie Mellon University. His
research lies in the intersection of machine learning and information
retrieval. His current research focus is on improving text representation
and understanding in real-world information systems using knowledge graphs
and neural networks. He is a recipient of Allen Institute for Artificial
Intelligence research fellowship. Besides publishing papers, he also
co-organizes NTCIR WWW Tracks about deep learning for search, the first
SIGIR workshop on knowledge graphs and semantics for text retrieval and
analysis, and a special issue in Information Retrieval Journal about
knowledge graph for IR.
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