SemChat: Extracting Personal Information from Chat Conversations (EKAW 2010)
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Transcript of SemChat: Extracting Personal Information from Chat Conversations (EKAW 2010)
By Keith Cortis & Charlie Abela
Instant Messaging (IM) - communication
in real time were messages are transferred
in a seemingly peer-to-peer manner
Increase in the fragmentation of personal
information
Several tools developed to aid users in the
management of their personal information
space
Vision behind Semantic Desktop (SD) -
tackling the difficulties when managing
personal information
Research - towards this area & extraction
of semantics from chat conversations
Improve PIM by linking the different
content found on the desktop with the
extracted semantics
Exploiting and extending NEPOMUK’s
Social Semantic Desktop framework with a
semantic chat client component, ‘SemChat’
Extraction and annotation of important
concepts from a chat conversation
Storage of any concepts that were not
annotated, for reference in future SemChat
sessions
Semantic search for specific concepts (incl.
events) in different ways, for example by
date
Ability to use this plug-in from different
chat clients achievable by using a client that
can handle multiple protocols
General Architecture
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NEPOMUK – allows user to manage alldata found on her desktop and to link thedocuments within the PIMO
Spark IM – XMPP chat client that satisfiedour needs
Spark IM – enhanced with multiprotocolfunctionality via the availability of anXMPP server
End of chat session - non-intrusive system
Cost of interruptions varies on average
between 10-15 minutes before users return
their focus to the disrupted task
Context menus used to represent operations
that a user can do, for each extracted concept
JAPE rules implemented – to recognize
possible events within a chat conversation
using regular expressions in annotations
Rule: EventRule
(
{ Lookup.majorType==event_trigger }
):eventTrigger
-->
{
AnnotationSet matchedAnns= (AnnotationSet) bindings.get("eventTrigger");
FeatureMap newFeatures= Factory.newFeatureMap();
newFeatures.put("rule","EventRule");
outputAS.add(matchedAnns.firstNode(),matchedAnns.lastNode(),
"EventTrigger",newFeatures);
}
Title and prospective date of the extracted
event can be edited by the user
Annotated event will automatically be saved
within Spark’s Task List
User can filter out a search by several criteria for
example by date
No formal evaluation was performed on
any of the semantic chat clients’ projects
that we considered in the related works
section
A session was organized were 8 users tried
out SemChat
6-12 participants are enough to test the
usability of a system (Dumas and Redish)
Features of extracting concepts from chat
conversations – proved as a popular choice
Semantic search feature proved to be less
popular with several users
Majority of users experienced the
extraction of concepts and/or events from
their chat conversation
All extracted concepts/events annotated
by users were successfully stored in the
PIMO and Task List respectively
In some cases important concepts flagged
within a conversation were not extracted
Problem – XtraK4Me selects most
important key phrases ordered by
occurrence rate
Problem addressed by improving XtraK4Me
or possibly using a better key phrase extractor
Limitation – some events not extracted since
they didn’t conform to the structure that
SemChat was implemented to recognize
Possible solution – further extend ANNIE
NER to recognize all possible types of events
that can be present within a chat conversation
Context-aware chat program
Tries to solve semantic conflicts which
occur between chatting users through the
tagging of ambiguous chat messages
Solves part of this problem and is a step
forward towards eliminating semantic
conflicts which occur in chat sessions
Morphological analysis used to extract
proper nouns from the dialogue text
Online images and articles from Wikipedia
related to the extracted nouns are
simultaneously displayed alongside the
dialogue text
Helps in reducing the elements of
ambiguity like searching
Identify and improve problems that IM
systems encounter moving towards the
Networked Semantic Desktop
Chat window offers a taxonomy panel
where annotation of messages is permitted
whilst a user is chatting
Semantic Querying - search of messages
wanted by specifying a particular attribute
System uses existing email transport
technology
Is integrated with NEPOMUK
Handles and keeps track of action items
within email messages
Extracts tasks and appointments found
within email messages which are then
added to the email client’s scheduler
Prototype system
Automatically identifies action items (tasks)
in email messages
Presents user with a task-focused summary
of a message
User can add action items to their “to do”
list
Integration of SemChat with popular
applications such as a an email client like
Thunderbird
Extracted events would be logged
automatically into the client’s event scheduler
Extend ANNIE NER through JAPE so that
other entities could be extracted from
conversations such as: emails, products, etc.
Semantic search feature – further optimize
the searching process
Semantic search feature – further enhanced
to display part of chat transcript satisfying
the search criteria
Semantic annotations generated by
SemChat – quantitatively evaluated in the
future
Investigate slang language in IM into more
depth so that SemChat would be adopted
to be handle it
Ex. : “mt b4 lunch @11.30am nxt tue”
We can further extend ANNIE NER with
JAPE to be able to recognize such an event
‘mt b4’ as being ‘meet before’ and ‘nxt tue’
as being ‘next Tuesday’
We have presented a semantic chat component in
SemChat which was integrated with a SSD
application – NEPOMUK
SemChat contributes further to area of PIM
through the integration of concepts in the user’s
PIMO and the integration of events within an
events scheduler
SemChat also reflects the research being done in
the area of the SD in relation to Semantic Chat
Thank you for your attention !
Any Questions?