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How Important is Natural Language to the Future of Search?

Ask Thinks the Best Results Don't Get Clicked

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Where Google is a search engine, and Bing is a "decision engine," Ask.com seeks to be an answer engine. Ask thinks the future of search is in questions and answers. This means, you should be able to ask a direct question and get a specific answer, rather than pages of results, which can lead you to finding the answer on your own.

It’s natural language search, and it’s not exactly a new concept. However, Ask says it is dedicated to improving how well this works. It makes sense, since the Q&A niche has been the area of search, which Ask has carved out for itself. Rather than trying to compete directly with Google as Bing does, Ask appears to be more interested in setting itself apart as a place to go simply to find answers. "Asking a question isn’t the same as searching," says Ask.

How imporant will natural language search be in the future? Share your thoughts.

Ask illustrates the difference with a couple sample queries, saying that the most successful answers won’t get clicked:

Comparing Q&A Results

Comparing Q&A Results

The company says it is seeing increased loyalty from users who conduct question searches, and has seen "a pronounced increase" in the percentage of users who conduct queries in the form of a question. In fact, they claim to see three times more questions as a share of total queries than their competitors.

"Indeed, the information that is directly relevant to many questions most certainly exists; it’s just that it’s locked in people’s heads or captured in unpublished conversations, and therefore inaccessible by traditional search," says Ask President Doug Leeds. "Obviously, this is not a trivial deficiency in a world that is increasingly interconnected and clamoring for perspective, guidance, and shared knowledge at an interpersonal level online."

Ask is setting out to extract and rank existing answers, and index sources of answers that have not yet been published. "To extract and rank existing answers, as opposed to merely ranking web pages that contain information, we have and are continuing to develop a unique set of algorithms and technologies that are based on new signals for relevance specifically tuned to questions and answers," says Leeds, outlining these signals with the following images.

Ask Algorithm Elements

Ask Algorithm Elements

Ask Algorithm Elements

Right now, Ask is focused on developing a new algorithm that utilizes the signals highlighted above. "But our work doesn’t end with extraction and ranking of existing, published answers," says Leeds. "Where our vision really comes to life is in our efforts to index the sources of unpublished knowledge that can generate answers specifically in response to a question, in the moment it’s asked. This is the long tail of questions that are nearly impossible for search engines to answer, but which create incredible value for users when they are."

These include complex questions (like "What is the cheapest way to get to the Austin airport from downtown Austin?"), temporally dependant questions (like "When will the Oakland Bay Bridge re-open?"), and subjective questions (like "What should you do to save a withering tomato plant?").

Ask has reached a milestone of 400 million Q&A pairs in its database, so the engine is already capable of answering a significant amount of questions you might have, but there’s a lot of work to be done in order to give users the "best answers on the planet" in real time, as the company intends to do. It will be interesting to see how Ask’s progress comes along. Leeds promises updates on the company blog as they work their way along.

Do you think Q&A search is an important part of search’s future? Do you think Ask will play a key role in it? Do you ever use Ask to find answers? Tell us what you think in the comments.

Related Articles:

> Ask Gets More Serious About Answers

> Ask.com Gets New U.S. Leadership

> Microsoft Tipped As Most Likely Ask.com Buyer

> Searching for Answers Google Doesn’t Have

How Important is Natural Language to the Future of Search?
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