Hi again Eugene,
Sorry, my last post appeared as "anonymous". I'm re-posting here...
Hi Eugene,
Thanks for your comment. "Little acts of disrespect" refers to the micro-inequities that are a result of hidden bias. However if they are recorded and viewed together, the bias is apparent. A personal dialogic agent can be set up to capture a record of these micro-inequities, and then guide users through a conversation about how they occur, and even develop indicators of how they occur that can be used to prevent them in the "moment of action".
"Meaning" is indeed a slippery concept, since as Bakhtin pointed out, it depends on context and history as much as connotation and denotation. For example, in a dysfunctional organization to treat someone "with respect" may mean putting on a facade of friendliness while plotting to sabotage their efforts. This may be moderated by the setting in which the phrase is used as well as the personal experiences of the participants with others. The personal agents in a dialogic web would guide the user to capturing his or her meanings associated with critical events or phrases, recognizing the polysemous ("many meaning'ed") nature of interaction. (I remember discussions with many people in the early '90's for whom "business process re-engineering" meant "management wants to fire us".)
For my prototype I used simple pattern-matching and boolean logic. This was sufficient to produce some significant improvements in group function, though it would have surely been better with some more sophisticated techniques. But, I was pleased with the results I got while just using CSci undergrads to do most of the programming. (Unfortunately, the last "real" coding I did was assembly language in the late 1980's. I just didn't have the time to keep up while I was doing other work.)
As far as I can tell in the future, computational "understanding" of polysemic / polysemiotic language is not possible. There are, however, many resources, such as AffectNet and WordNet that can help with the detection of patterns through data mining. Taxonomic approaches, as I believe Google and Siri use, are also very helpful for the broad strokes of capturing semantics. However the dialogic web relies on pattern matching and data mining along with user-supplied meanings which supplement the data mining. Sharing this information between personal agents can be very powerful.
I hope this clarifies things. Let me know if you have more questions.
Thanks,
Ray