1) Speech to Text

THEN

2) NLP

I for one, wouldn’t mind speaking to my computer or laptop and having it do things for me automatically. e.g. “Find me a recipe on making creme brulee” as opposed to typing that out. Why the hell would I type the entire thing out if I can just type, “recipe creme brulee”? It’s not about CONDITIONING - it’s about the INTERFACE (search box). I want to TYPE less because it’s a pain to type more than I need to.

We DON’T TYPE in natural language - WE SPEAK IT. That’s why it’s called LANGUAGE. If we can get the speech to text right - then we need the machines to understand the text part — this is where NLP comes into the picture.

Look at the BIG picture folks.

“There’s the aspect of us being conditioned to use words and short phrases to describe what we are searching for. ”

Its not just this. I think the data that is being presented on the web today is tuned(read “dictated”) for Google - everywhere, instead of it being the other way round. Search Engines should aim to build technology or processes to identify the best content or suitable results to present as a human would have.

I bet more than 50% of websites out there would want to change their titles, heading tags or layout if they did not have to tune it so much for SEO. It is like the websites are themselves defining if they would appear on the search results than google. This is serious thread and severe restrictions on the part of people who contribute content on the web.

For example, if you wanted to call into your bank and check your checking account balance, you could simply say “I want to check my checking account balance” and the system would respond with the amount available in your account.

…For example, …

Consider customer service, for example. The content available through support portals and in call centers is not nearly as disparate as you might find on the web. The density and frequency of product, company and
industry keywords is much greater, which means keyword-matching engines will return very long result lists that force the user to comb through the results to find the information they really need. What the user needs is an engine that can take context into account, and NLP is one of the best ways to capture the context of the search session, and the intent of the searcher.

Look around and you will find numerous examples – across banking, insurance, telecommunications, retail electronics, and more - where keyword search engines like Google and others have been displaced by engines with NLP and other advanced features. It’s not too hard to find examples to compare and contrast keyword search experience from NLP search experiences, even on .com sites. Try T-Mobile vs at&t wireless.

Start with an end in mind (i.e. researching a cell phone or calling plan), then compare the search experiences on each site. Start with basic keyword searches, the type we all do every day to try to game the engine to get good results, and compare the experience against how well you find the information you need to make a purchase decision. Then compare the experiences with longer, more explicit queries that align closer to what you are actually thinking (i.e. what is coverage in NY, phones under $100, phones with a camera, rules for rollover minutes, online bill payment options, cheap phones). I think the differences are clear. Parity on shorter searches, superior experience through NLP with longer queries.

I think keyword engines like Google and some of the other early pioneers were particularly effective in getting people to use search in the first place. But we are still early in terms of search innovation, and the technology continues to improve. To be dismissive of NLP is short-sighted.

And one more point - to position keyword vs NLP as mutually exclusive is misleading. Most NLP engines are equally effective at keyword matching. It’s simply the case that the more words a user supplies, the more
precise the answer.

I use the example of people searching for “calendar of events” and how it’s impossible to know exactly what that searcher wants. If a human can look at a search query and still not understand exactly what the searcher wants, how is a computer algorithm going to do it?

Autonomy tries to address different problems than Google. While Google can build upon the densely linked structure of the Web and an enormous amount of documents, Autonomy is more about finding that specific piece of information you are looking for on your company’s intranet that usually doesn’t sport as extensively linked documents.
For Google it doesn’t matter if they miss some particular document as there is plenty of information available to fill that gap while in an enterprise context finding that particular document can be essential.
This is why one may outperform the other and vice versa: They are both specialised tools that work best in the field they were designed for.

Some of the above comments are right. Google/Microsoft are gonna end up buying companies like Hakia and until then what they (Google) r doing is the best.

Where in fact Google hasn’t adapted to us (humans that is;), just like eskimos in the poles and dessert dwelling natives adapting to their environment we have adapted to Google.

whats he is talking about is pretty much HARD CODING certain search terms that are getting a lot of hits. they know what hasnt been a good hit if people start going past page 1 or 2 and this becomes a question to solve and hard code into the system. so the next time it knows the difference between google bombed and google bombed…

with all it might its exciting to see that even Google can go the way of the original WebCrawler in a couple of years against something that suits our natural base better…

The concept Semantic Web actually hasn’t very much to do with search techology. While the latter is about machines somehow automatically ‘understanding’ the content generated by human beings as it is, Semantic Web means more or less manually enriching the content in order to make it easier to understand for machines.

This is why Semantic Web from my point of view won’t work at the large scale, I simply cannot imagine every content creator to manually create Semantic Web annotations just because Tim Berners-Lee has a vision. It might be justified in certain settings, where you have knowledge engineers dedicated to such kind of work, but don’t expect your average content provider to supply Semantic Web annotation any time soon.

with NLP search, for example, you might perform your queries with your voice, in natural language, into your cell phone, and hear the answer from a vocoded sergey brin.

And I agree.
I know how these things work, I don’t type sentences in search boxes…

Many people I know do.
And every so often, I’m not quite sure how to phrase something so that I get the results I want.

but i’d still consider myself relatively proficient at ”Google Grammar” as such, though I daresay, the extension to the language allowed, and considerably more directed results can’t be all that bad for both advertisers and customers…

Surely an advertiser will pay more to be the ad for a query “Where can I buy Pallet Jacks in Seattle?” than “Pallet Jack” - the directedness is probably quite useful.

But chances are, if you’re viewing this website, you’re one of the more computer proficient individuals on the planet - and I guess such a change doesn’t mean as much to you or me as it might to my 40 year old mother, who gets cranky when results are irrelevent.

Yes, this is the same line that google has taken for a long time.

example of Google’s theory:

query: pallet jack

query: I want to buy a pallet jack

The results are 30% overlapping.

The keywords ‘pallet jack’ over lap - 34% / Google is good.

NLP sucks on Ask, but Ask isn’t about semantic processing. With semantic processing, NLP starts to make a lot more sense. They’re not necessarily causally related concepts, but they’re certainly correlated concepts.

Nevertheless, a serious issue that still persists for natural language search in the backend is scalability when compared to simple keyword-indexing algorithms. While keyword indexing can be done in linear or loglinear time, for example naive parsing algorithms in the worst case can take exponential time, while even rather efficient parsing algorithms like the Earley parser often still need polynomial time (O(n^3) in this case). This is the actual reason why PowerSet still has to prove to me that their particular approach of setting up a large-scale all-the-Web natural language search engine is computationally feasible at all and not just some expensive toy for investors.

Maybe skipping the sentence level and concentrating on the text level is the way to go. Many of the elements that create textual coherence (and hence natural language understanding) operate beyond the scope of sentences. An engine that can create these kind of links could be very useful, though it would be beyond the notion that we have of search engines nowadays.

That’s a pretty arrogant statement. How does he know what most people are? If this is the way google looks at it’s users then shame on them.