Dear 'www-annotation' subscribers,
I am a French PhD student in Computer Science, working on Web annotation. In the context of discussion threads support through
annotation, I am currently looking for people to take part in an online experimentation (see below for more details). You can start
this experimentation (and resume it at anytime) by following this link:
Do not hesitate to send me your feedback and comments :) Thank you in advance. Yours sincerely.
PS: If the above link doesn't work, you may need to install the Java environment freely available here http://www.java.com/en/.
PS2: The command "javaws http://www.irit.fr/~Guillaume.Cabanac/expe/expe.jnlp" is also available to start the experimentation.
** What is The Task of a Participant in This Experimentation? **
In the proposed experimentation, a "participant" has to evaluate 13 argumentative discussion threads (a discussion thread is a tree
whose nodes contain people's statements; they are chronologically organized as in Usenet or Web forums). You can see a screen
capture of a discussion thread evaluation here: http://www.irit.fr/~Guillaume.Cabanac/expe/example.png.
Evaluating a discussion thread requires 2 steps:
1. The participant labels each node by identifying its *opinion*: does the node confirm (pro), refute (against) or is neutral
regarding its direct parent? On the screen capture, the participant has labeled the nodes thanks to the "flag" buttons (nodes are
then displayed in color). For example, Tom refutes Bob, which is refuting Alice.
2. For each replied node, the participant synthesizes the opinions of its replies. This *mental synthesis* value ranges from
refutation to confirmation. On the example, the participant feels that Bob's statement is rather confirmed, and Alice's one is
** What is The Aim of This Experimentation? **
The main aim of this experimentation is to compare i) mental synthesis of opinions that are expressed within discussion threads with
ii) the results of the 2 algorithms that we have developed.
These algorithms take as input an opinion-labeled discussion thread, they compute a value that corresponds to the synthesis of the
opinions, ranging from "the root statement is refuted" to "the root statement is confirmed". The first algorithm computes a
statistical score whereas the second one is based on a AI research framework (bipolar argumentation framework). If you are
interested in these algorithms, I may send you a research paper (RIAO'2007) upon request by mail.
A minor aim is to check if people label the discussion thread in the same way (step 1).
** Possible Applications of This Research **
The main topic of my Computer Science thesis work is "digital annotation" on the Web where amounts of digital documents are freely
accessible. With a classical Web browser, people are rather passive as they can only read documents. Indeed, one cannot indicate a
mistake he has found, ask a question, link the document to another one or simply express his thought.
In order to enable people to interact with a digital document, "annotation systems" have been developed since the early 1990's, cf.
(Wolfe, 2002, http://dx.doi.org/10.1016/S8755-4615(02)00144-5). Such software make it possible to annotate every digital document
the same way as paper, for personal purposes, e.g. critical reading, proofreading, learning, etc.
Moreover, as modern computers are networked, digital annotation can be stored in a common database. This makes it possible to
display a document along with its annotations that may come from numerous readers all over the world. Then readers can reply to
annotations and also to replies, forming "discussion threads" that are displayed in the context of commented documents.
When documents are massively annotated (see a video demonstration (http://g.cabanac.free.fr/publications/2005-11-IWAC/demoAmaya.wmv)
with the Amaya annotation system) and discussed (each annotation can spark off a discussion thread), it seems to me that the reader
is overwhelmed. Reading an annotation, its replies that are hierarchically organized and synthesize their opinions is a difficult
In order to overcome this problem, annotation systems could compute the "social validation" of each annotation. This requires that
annotators give an explicit opinion type to they annotations; NLP algorithms can also be applied, e.g. (Pang et al., 2002,
http://portal.acm.org/citation.cfm?id=1118704&dl=GUIDE). Then, the reader can decide to focus on discussions that have reached
consensus (totally refuted or confirmed). On the other hand, he may focus on ongoing discussions identified by a neutral social
validation. Moreover, intra-discussion thread social validations may guide the reader that can identify "supporting" and "defeating"
I hope that these explanations help to understand the aims of my experimentation. Please, let me know what you think about this
Guillaume Cabanac http://www.irit.fr/~Guillaume.Cabanac
PhD student in Computer Science
IRIT - Computer Science Research Institute of Toulouse University, France
Information Systems team
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