Solving Math Number Word Problem - What can NLP, Knowledge Engineering and Machine Learning contribute?

Date:           Thursday, 19 Nov 2015
Time:           10:00am - 12 noon
Venue:          Lecture Theater G (near lifts 25/26), HKUST


(Seminar I)

Speaker:        Dr. Chin-Yew LIN
                Principal Research Manager
                Microsoft Research Asia

Title:          "Solving Math Number Word Problem - What can NLP,
                 Knowledge Engineering and Machine Learning contribute?"

Time:           10:00am to 11:00am


With the availability of personal agents such as Cortana, Siri and Google
Now, it seems a world of humans and machines communicate and solve
problems together in natural language is not far away. The scene of freely
chatting with HAL in 2001: A Space Odyssey and Samantha in Her could
happen to us seems within reach. The question is are we ready to go there?
Do we have sufficient and necessary technologies to make it happen? In
this talk, I will use solving math number word problem as an example to
show how the emerging NLP, knowledge engineering and machine learning
technologies can pay the way to this holy grail and what challenges that
we have to address to travel down the path.


Dr. Lin is a Principal Researcher and Research Manager of the Knowledge
Computing group at Microsoft Research Asia. His research interests are
knowledge mining, natural language processing, problem solving, question
answering, and automatic summarization.

Recently, his main research directions are: (1) developing a knowledge
computing framework for real world applications and services including
automatic acquisition of semantic knowledge, machine reading for semantic
indexing, and automatic understanding of user intents; and (2) developing
big social data analytics platform and services - Project Soul. Building
on experiences learned from Project Soul, his team is developing
technologies to automatically learn social interaction knowledge from
large-scale real world online data and transform unstructured and
semi-structured web data into structured data to enable semantic
computing. The goal is to enable context-aware interactive
knowledge-enriched applications powered by intelligent data in the cloud.

He developed automatic evaluation technologies for summarization, QA, and
MT. In particular, he created the ROUGE automatic summarization evaluation
package. It has become the de facto standard in summarization evaluations.
ROUGE has been chosen as the official automatic evaluation package for
Document Understanding Conference since 2004.

Before joining Microsoft, he was a senior research scientist at the
Information Sciences Institute at University of Southern California
(USC/ISI). He was the program co-chair of ACL 2012 and program co-chair of
AAAI 2011 AI & the Web Special Track. He is an Action Editor of
Transactions of ACL and a member of the Editorial Board of Computational