Barney writes that:
I agree with Ron Kaplan's view at last week's Accelerating Change
Conference that Conversational User Interfaces will come of age in over
the next 5-10 years.
I've recently been doing a literature survey on models of agreement and disagreement in dialogue and found very little out there other than this paper which is close, but not quite what I am after (even when extending the survey off net and speaking to linguistitians). This page on Rhetorical Structure Theory (RST) is a good place to start, as is this page on Discourse Modeling, but I'm looking for research that focuses on the act of agreement/disagreement, and light-weight processes for detecting cues in text. For example, imagine looking for agreement in email exchanges.
It turns out that searches for linguistic issues are especially hard. The term 'agreement' has a number of special meanings in linguistics (agreement of features, agreement of labellers in labeling tasks). MSN search does a better job here than Google in surfacing the notion of speaker agreement in the query 'theories of discourse agreement.' But lo, Clusty aced this problem. A query on 'discourse model agreement' produced a cluster (the first) for which the first result (via citeseer) was:
Dialog Act Modeling for Conversational Speech (1998)
Andreas Stolcke, Elizabeth Shriberg, Rebecca Bates, Noah Coccaro, Daniel Jurafsky, Rachel Martin, et al.
with the abstract:
We describe an integrated approach for statistical
modeling of discourse structure for natural conversational
speech. Our model is based on 42 `dialog acts'
(e.g., Statement, Question, Backchannel, Agreement,
Disagreement, Apology), which were hand-labeled in
1155 conversations from the Switchboard corpus of
spontaneous human-to-human telephone speech.
Ok - so it is not about agreement/disagreement in text, but it does look promising.
For these types of non trivial search, the generic solutions of Google, et al are simply not working. There is a long tail of search problems that mine above, but they seem to have little impact on the product direction of search companies. I wonder if there is a relationship between the complexity of a query and the likelihood of click-through on ads which means that the optimal point for Google is closer to serving very simple searches rather than really doing a good job at hard search problems.
On a side note, Google also does poorly with 'NLP' which it believes is largely about Neuro Linguistic Programming, not Natural Language Processing. There may well be a lot more pages on the former, but a search for 'Michael Jordan' shows that Google does attempt to provide results on the first page for various meanings of the search phrase - perhaps they haven't figured out how to do this for abbreviations and acronyms.
I need to look into Rollyo...