Acronyms are abbreviations formed from the initial com-

ponents of words or phrases. Acronym usage is becoming

more common in web searches, email, text messages, tweets,

blogs and posts. Acronyms are typically ambiguous and

often disambiguated by context words. Given either just

an acronym as a query or an acronym with a few context

words, it is immensely useful for a search engine to know the

most likely intended meanings, ranked by their likelihood.

To support such online scenarios, we study the offine min-

ing of acronyms and their meanings in this paper. For each

acronym, our goal is to discover all distinct meanings and for

each meaning, compute the expanded string, its popularity

score and a set of context words that indicate this meaning.

Existing approaches are inadequate for this purpose. Our

main insight is to leverage "co-clicks" in search engine query

click log to mine expansions of acronyms. There are several

technical challenges such as ensuring 1:1 mapping between

expansions and meanings, handling of "tail meanings" and

extracting context words. We present a novel, end-to-end

solution that addresses the above challenges. We further

describe how web search engines can leverage the mined in-

formation for prediction of intended meaning for queries con-

taining acronyms. Our experiments show that our approach

(i) discovers the meanings of acronyms with high precision

and recall (ii) significantly complements existing meanings

in Wikipedia and (iii) accurately predicts intended meaning

for online queries with over 90% precision.

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Publisher  WWW Conference 2013

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