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Recap: Future of Semantic Search Panel @ Web 3.0

I had the good fortune on Thursday to be a part of a panel on semantic search at the Web 3.0 Conference. The panel was organized Mark Johnson(Bing/Powerset) and featured the likes of Connie Kenneally (TextWise), Will Hunsinger (Evri), Tim Musgrove (TextDigger), and yours truly (LCC, Swingly, Extractiv, etc.).

Mark put on an absolutely great panel. In addition to being one of the most knowledgeable people in our industry, he’s a natural-born moderator and a talented discussion leader. He’s got great journalistic chops too: definitely not one to shy away from asking the tough questions.

Since I wasn’t able to capture video of the panel, I thought I’d try to recreate my side of the discussion. Here are some of the questions that Mark asked — and the gist of the answers I gave. (Or would have given.)

More after the jump…

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Don’t Miss: “The Evolution of Semantic Search” @ Web 3.0

Tracking Sentiment from Day 1 of Defrag

Language Computer has been tracking what Twitter users are saying about the first day of Defrag 2009 using Positively, our sentiment extraction tool.

The Good

Here are the top 8 topics people are positive about (ranked in order of how strongly people are feeling).

1. People: So happy,  favorite,  fun,  great,  incredibly interesting,  interesting,  really nice,  smart,  super smart,  way more interesting

2. Day: good,  great,  lovely,  really fun

3. Dinner: Awesome,  Excellent,  absolutely delightful,  lovely

4. Topics: good,  positive

5. Discussions: Liking,  interesting,  lively

6. Talks: interesting,  really interesting,  visually engaging

7. Wifi: as good as the weather,  enjoying,  hella sweet

8.  EventVue, livestream, backchannel: Really enjoying, nice

(Ranking was determined on the inherent expected strength of the sentiment (e.g. hella sweet >> okay) and the number of tweets we found which expressed the same sentiment.  We’ve only listed the unique sentiments up above.)

The Bad

Far fewer negative sentiments.  In most cases, we couldn’t find something that three people were griping about.  Here are the two that reached that threshold:

1.  Kessler’s talk:  dangerous, doesn’t care, stupid, bad, provocative, pointless, useless

2.  language on stage: offended, unnecessary, pointless

The Day 1 “Winners”

After Day1, here are the most positively regarded Twitterers using the #defrag or #defragcon hashtags:  @bpm140, @stoweboyd, @sacca, @benkepes

The Overall Score

So far, tweets are running 88% positive overall for Defrag 2009 after day 1.

Want more info on sentiment tracking on Twitter using Positively?  Contact me at andy@languagecomputer.com.

Avoiding Search Overload

Like you, we’ve heard a lot this summer about the challenges facing America:  the financial crisis, healthcare reform, and worst of all:  search overload.

Well, here at Swingly HQ, we’ve been doing our part.  We’ve been trying to find new ways to figure out what kinds of information are most relevant to a particular search topic.

While relevance modeling isn’t exactly new, it’s becoming an increasingly important problem for semantic search applications.   Information Extraction apps are rapidly increasing the amount of factual information that’s available from the Internet.  That’s good.  Unfortunately, instead of being buried under mountains of irrelevant information, we’re now being overwhelmed with gigabytes of factual information which may (or may not) be exactly what we’re looking for.  That’s bad.

So, what’s a new semantic search app to do?  Full details after the jump.

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