Speaker: Xiaodan Zhu, National Research Council in Ottawa
Title: Several Neural Network Models for Semantic Composition
Modeling semantic compositionality is a core problem in NLP. In this talk, I will describe several neural-net models we developed recently, including a framework that considers both compositional and non-compositional property in semantic composition. I will discuss two specific networks that extend Long Short Term Mermory (LSTM) to tree and DAG (directed acyclic graph) structures.
Xiaodan Zhu is a research scientist of National Research Council Canada in Ottawa, Canada. His research interests include natural language processing, spoken document understanding, and machine learning. Xiaodan received his Ph.D. from the Department of Computer Science of the University of Toronto in 2010 and Masters of Engineering from the Department of Computer Science and Technology of Tsinghua University, Beijing in 2000.