Browse Prior Art Database

Context-Dependent E-Mail Classification

IP.com Disclosure Number: IPCOM000013641D
Original Publication Date: 2001-Mar-01
Included in the Prior Art Database: 2003-Jun-18
Document File: 4 page(s) / 109K

Publishing Venue

IBM

Abstract

Current e-mail classification mechanisms use incremental machine learning techniques for e-mail classification. A known e-mail classification system uses a modified decision tree algorithm which uses word frequency for e-mail classification. Such system work well if the task is, for example, to route incoming customer requests to the right customer representative and to automatically display a short list of mail-templates for answering the request. The reason for this is that the request and the responses to these requests are not context-dependent and do not change much over time.

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Context-Dependent E-Mail Classification

  Current e-mail classification mechanisms use incremental machine
learning techniques for e-mail classification. A known e-mail
classification system uses a modified decision tree algorithm which
uses word frequency for e-mail classification. Such system work
well if the task is, for example, to route incoming customer
requests to the right customer representative and to automatically
display a short list of mail-templates for answering the request.
The reason for this is that the request and the responses to these
requests are not context-dependent and do not change much over
time.

The situation is quite different when the goal is to sort the
mail of office workers engaged in project-oriented work.
Classification here will be based not only on the content and the
sender of the massage, but as well on the status of the projects
the receiver is involved in. For example, the same message from
the secretary "Here are your flight and hotel reservation
details" will be filed under very different projects each time a
trip is done for a different project.

The disclosed mechanism assumes that each project an
office-workers is working has a set of filing agents (or other
computer interpreted records) which listen for the expected
incoming mails for that project. So, for example, by sending a
request for trip planning to the secretary in the context of a
project a user automatically generates a filing agent that will
listen for incoming messages from the secretary with respect to
trip planning. Once the filing agent detects such a message it
will file it into the associated project. Furthermore the agent
might also be set up in such a way that it issues a warning if a
matching message did not arrive within a specified time frame.
Thus, as a by-product of working on a project and issuing
requests to other users filing agents for context dependent
e-mail classification are set up. In addition to these implicit
setup mechanism filing agent can also be set up explicitly for
each project by specifying expected future events.

The proposal disclosed here does not try to replace current
mechanisms for e-mail classification, but to augment it.
Filtering agents as discussed in the examples above will use
"classical" classification mechanisms to determine their
applicability to a g...