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is analyzed independently of others. This can lead to
mistakes in the subject identification. For example,
if one message has the words “neural nets” and the
next one has “learning”, the system should
recognize that “learning” is related to “machine
learning”, since it is assumed that the discussion
would not deviate from the context. However, in the
current state, the system may identify “learning” in a
context like “Computers in Education”. We are
studying a solution that considers the context of a
discussion, that is, the system will assume that
discussions do not move far from one subject to
other. Using the hierarchy of concepts, it is possible
to identify the distance of each movement from one
message to another, and this will be used to
disambiguate a message: when two or more subjects
are identified in a message, the nearest one must be
used.
ACKNOWLEDGEMENTS
This work is partially supported by CNPq, an entity
of the Brazilian government for scientific and
technological development.
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ANALYZING WEB CHAT MESSAGES FOR RECOMMENDING ITEMS FROM A DIGITAL LIBRARY
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