Jing Jiang

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Web:

Google dictionary:

	As a non-native English speaker, I often want to 
find out the correct usage of a word, and more often, the 
correct usage of a phrase.  Online dictionaries usually only 
show a few examples.  For certain phrases, online 
dictionaries may not even have entries for them.  If I try 
to search for the word or phrase on Google, it usually finds 
web pages where the word or the phrase only appears in the 
title, which is not very helpful.  It would be very useful 
to have a Google dictionary so that if you type in a word or 
a phrase, it shows how the word or phrase is popularly used, 
with summarization and examples.  The users of such a tool 
will be people to whom English is a second language, or kids 
who are still learning new words and phrases.  It would also 
be useful for finding out the meanings of buzzwords.  Since 
it is supposed to be an English dictionary, the system 
should filter out web pages that may contain improper usage 
of English.  The data involved should most likely be news 
articles, online books, essays, and other well-written 
English articles.  The dictionary should ideally summarize 
the usage of the word or phrase into several categories, 
give examples for each of them, and maybe differentiate 
between formal usage and informal usage.  And just like 
other online dictionaries, this dictionary should be able to 
correct the userâ^À^Ùs spelling, or find the best match if the 
user enters a phrase that does not exist.

Email:

A better email routing method in customer support systems:

	I thought of this problem because once after I sent 
an email to userhelp, someone emailed me back, asking me to 
re-send the email to userhelp because the problem was not 
supposed to be assigned to him.  I then realized that there 
must be an automatic router in any customer support system 
that assigns emails to experts who can solve the 
corresponding problems.  This is essentially a text 
categorization problem in the email domain.  People who 
benefit from solving such a problem are both the customers 
and the companies/organizations who provide the service.  
The data involved are emails from customers who have 
different kinds of questions.  A good email router should 
provide several functions: (1) To identify from the email 
text the major problem the customer encountered, and assigns 
the problem to a certain category or several categories. (2) 
To extract information the customer provided to assist the 
human expert to fix the problem, e.g., error message the 
customer received from a computer screen, IP address, etc. 
The information can also include the customerâ^À^Ùs name, phone 
number, customer ID, etc.  (3) If the problem is common, and 
the solution is already available, then the solution should 
be automatically sent back to the customer.  I believe such 
routing systems have already been developed in many places.  
But how satisfactory they are is still questionable.

Literature:

Automatic survey generation:

	A useful tool for researchers may be a program that 
can automatically generate surveys on topics given by the 
user, and/or recommend a state-of-the-art technique that 
works the best for the user regarding the topic/problem.  
The users of such a program are either researchers who want 
to find out the current state-of-the-art technology in some 
research areas new to them (or simply new research areas), 
or researchers and engineers who want to use existing 
methods/tools as building blocks for their research/products 
at a higher level.  The problem is not trivial because (1) 
for new research topics, there probably is no survey paper 
published, yet, (2) even for old research topics, the 
existing survey papers may be out-dated already.  Therefore, 
data involved in this challenge not only include existing 
survey papers on the given topic, but also include the most 
recent research papers that are addressing the problem.  The 
problem is challenging in several aspects. (1) Some topics 
(especially new topics) may not be well defined.  When 
searching for relevant papers, the system needs to consider 
different ways of describing the problem, in addition to 
what the user provides, (similar to query expansion.)  (2) 
How to summarize the methods proposed in different papers, 
and how to compare the pros and cons of these methods may be 
difficult.  This may involve text summarization and 
information extraction.  For example, can the system 
identify a benchmark for the given problem and compare the 
performance of different methods on that benchmark?  (3) If 
the user provides his constraints/requirements, can the 
system recommend a good method that fits the userâ^À^Ùs need the 
best?  This may involve more sophisticated techniques.