Making search better in Catalonia, Estonia, and everywhere else
Posted by Paul Haahr and Steve Baker,
Software Engineers, Search Quality
We recently began a
series of posts on how we harness the power of data.
id="m2px" >Earlier we told
you how data has
been critical to the advancement of search; about using data to
id="cxjy" >make our
products safe and to
id="thga" >prevent fraud; this post is the newest in
the series. -Ed.
One of the most important uses of data at Google is building
language models. By analyzing how people use language, we build
models that enable us to interpret searches better, offer spelling
corrections, understand when alternative forms of words are needed,
offer
>language
translation, and even
id="e-d4"
>suggest
when searching in another language is appropriate.
One place we use these models is to find alternatives for words
used in searches. For example, for both English and French users,
"GM" often means the company "General Motors,"
but our language model understands that in French searches like
>seconde GM, it means "Guerre
Mondiale" (World War), whereas in
>STI GM it means "Génie Mécanique"
(Mechanical Engineering). Another meaning in English is
"genetically modified," which our language model
understands in
>GM corn. We've learned this based on the
documents we've seen on the web and by observing that users
will use both "genetically modified" and "GM"
in the same set of searches.
We use similar techniques in all languages. For example, if a
Catalan user searches for
id="w3e2" >resultat elecció barris BCN
(searching for the result of a neighborhood election in Barcelona),
Google will also find pages that use the words
"resultats" or "eleccions" or that talk about
"Barcelona" instead of "BCN." And our language
models also tell us that the Estonian user looking for
id="b8_i" >Tartu juuksur, a barber in
Tartu, might also be interested in a "juuksurisalong," or
"barber shop."
In the past, language models were built from dictionaries by hand.
But such systems are incomplete and don't reflect how people
actually use language. Because our language models are based on
users' interactions with Google, they are more precise and
comprehensive — for example, they incorporate names, idioms,
colloquial usage, and newly coined words not often found in
dictionaries.
When building our models, we use billions of web documents and as
much historical search data as we can, in order to have the most
comprehensive understanding of language possible. We analyze how
our users searched and how they revised their searches. By looking
across the aggregated searches of many users, we can infer the
relationships of words to each other.
Queries are not made in isolation — analyzing a single search in
the context of the searches before and after it helps us understand
a searcher's intent and make inferences. Also, by analyzing how
users modify their searches, we've learned related words,
variant grammatical forms, spelling corrections, and the concepts
behind users' information needs. (We're able to make these
connections between searches using cookie IDs — small pieces of
data stored in visitors' browsers that allow us to distinguish
different users. To understand how cookies work,
id="e0o:" >watch this video.)
To provide more relevant search results, Google is constantly
developing new techniques for language modeling and building better
models. One element in building better language models is
id="z0.8" >using more data collected over
longer periods of time. In languages with many documents and users,
such as English, our language models allow us to improve results
deep into the "long tail" of searches, learning about
rare usages. However, for languages with fewer users and fewer
documents on the web, building language models can be a challenge.
For those languages we need to work with longer periods of data to
build our models. For example, it takes more than a year of
searches in Catalan to provide a comparable amount of data as a
single day of searching in English; for Estonian, more than two and
a half years worth of searching is needed to match a day of
English. Having longer periods of data enables us to improve search
for these less commonly used languages.
At Google, we want to ensure that we can help users everywhere find
the things they're looking for; providing accurate, relevant
results for searches in all languages worldwide is core to
Google's mission. Building extensive models of historical usage
in every language we can, especially when there are few users, is
an essential piece of making search work for everyone, everywhere.
Tags: , Baker, engineers, Haahr, Paul, Posted, software, Steve
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