Understanding pages: Over years we have invested heavily in our crawl and indexing system. As a result we have a very large and very fresh index. In addition to size and freshness, we have improved our index in other ways. One of the key technologies we have developed to understand pages is associating important concepts to a page even when they are not obvious on the page . We find the official homepage for Sprovieri Gallery in London for the Italian query [galleria sprovieri londra], even though the official page does not have either London or Londra on it. In the U.S., a user searching for [cool tech pc vancouver, wa] finds the homepage www.cooltechpc.com even though the page does not mention anywhere that they are in Vancouver, WA. Other technologies we have developed include distinctions between important and less important words in the page and the freshness of the information on the page.



Understanding queries: It is critical that we understand what our users are looking for (beyond just the few words in their query). We have made several notable advances in this area including a best-in-class spelling suggestion system, an advanced synonyms system, and a very strong concept analysis system.

Did you mean: ; whereas someone searching for [

Most users have used our spelling suggestion system at one time or another. It knows that someone searching for [ kofee annan ] is really searching for Mr. Kofi Annan, and is prompted: kofi annan ; whereas someone searching for [ kofee beans ] is actually looking for coffee beans. Doing this internationally with very high accuracy is hard, and we do it well.

Doctor whereas in [Drive. A user looking for [rear bumper repair. For [Air Base; for the query query [Bed and Breakfasts in Alberta, Canada. We have developed this level of query understanding for almost one hundred different languages, which is what I am truly proud of.

Synonyms are the foundation of our query understanding work. This is one of the hardest problems we are solving at Google. Though sometimes obvious to humans, it is an unsolved problem in automatic language processing. As a user, I don't want to think too much about what words I should use in my queries. Often I don't even know what the right words are. This is where our synonyms system comes into action. Our synonyms system can do sophisticated query modifications, e.g., it knows that the word 'Dr' in the query [ Dr Zhivago ] stands forwhereas in [ Rodeo Dr ] it means. A user looking for [ back bumper repair ] gets results aboutbumper repair. For [ Ramstein ab ], we automatically look for Ramstein; for the query query [ b&b ab ] we search forin, Canada. We have developed this level of query understanding for almosthundred different languages, which is what I am truly proud of.

For example, our algorithms understand that in the query [New York Times. We don't just stop at identifying concepts; we further enhance the query with the right concepts when, for instance, someone looking for [impact of computers on society, or someone who searches for [rain forest lesson plans. Our query analysis algorithms have many such state-of-the-art techniques built into them, and once again, we do this internationally in almost every language we serve.

Another technology we use in our ranking system is concept identification. Identifying critical concepts in the query allows us to return much more relevant results.our algorithms understand that in the query [ new york times square church ] the user is looking for the well-known church in Times Square and not for articles from the. We don't just stop at identifying concepts; we further enhance the query with the right concepts when,someone looking for [ PC and its impact on people ] is in fact looking for, or someone who searches for [ rainforest instructional activities for vocabulary ] is really looking for. Our query analysis algorithms have many such state-of-the-art techniques built into them, and once again, we do this internationally in almost every language we serve.

Understanding user s : Our work on interpreting user intent is aimed at returning results people really want, not just what they said in their query. This work starts with a world class localization system, and adds to it our advanced personalization technology, and several other great strides we have made in interpreting user intent, e.g. Universal Search.



Canada, New Zealand, Israel, Japan, Russia, Saudi Arabia, For example , [Côte d'Or] is a geographic region in

Our clear focus on "best locally relevant results served globally" is reflected in our work on localization. The same query typed in multiple countries may deserve completely different results. A user looking for [ bank ] in the US should get American banks, whereas a user in the UK is either looking for the Bank Fashion line or for British financial institutions. The results for this query should return local financial institutions in other English speaking countries like Australia South Africa . The fun really starts when this query is typed in non-English-speaking countries like Egypt Switzerland . Likewise the query [football] refers to entirely different sports in Australia , the UK , and the US . These examples mostly show how we get the localized version of the same concept correctly (financial institution, sport, etc.). However, the same query can mean entirely different things in different countries., [Côte d'Or] is a geographic region in France - but it is a large chocolate manufacturer in neighboring French-speaking Belgium ; and yes, we get that right too :-).

Personalization is another strong feature in our search system which tailors search results to individual users. Users who are logged-in while searching and have signed up for Web History get results that are more relevant for them than the general Google results. For example, someone who does a lot football-related searches might get more football related results for [giants], while other users might get results related to the baseball team. Similarly, if you tend to prefer results from a particular shopping site, you will be more likely to get results from that site when you search for products. Our evaluation shows that users who get personalized results find them to be more relevant than non-personalized results.





Another case of user intent can be observed for the query [ chevrolet magnum ]. Magnum is actually made by Dodge and not Chevrolet. So we present the results for Dodge Magnum with the prompt See results for: dodge magnum in our result set.

Our work on Universal Search is another example of how we interpret user intent to give them what they (sometimes) really want. Someone searching for [ bangalore ] not only gets the important web pages, they also get a map, a video showing street life, traffic, etc. in Bangalore -- watching this video I almost feel I am there :-) -- and at the time of writing there is relevant news and relevant blogs about Bangalore.

Finally let me briefly mention the latest advance we have made in search: Cross Language Information Retrieval (CLIR). CLIR allows users to first discover information that is not in their language, and then using Google's translation technology, we make this information accessible. I call this advance: give me what I want in any language. A user looking for Tony Blair's biography in Russia who types the query in Russian [Тони Блэр биография] is prompted at the bottom of our results to search the English web with: