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been made in this direction over the past few years. Examples include explicit manipulation of human-defined concepts and their use to augment the bag of words in information retrieval (Egozi et al., 2011), or using Wikipedia for better word sense disambiguation (Bunescu and Pasca, 2006; Cucerzan, 2007).

One way to use CGC repositories is to treat them as huge additional corpora, for instance, to compute more reliable term statistics or to construct comprehensive lexicons and gazetteers. They can also be used to extend existing knowledge repositories, increasing the concept coverage and adding usage examples for previously listed concepts. Some CGC repositories, such as Wikipedia, record each and every change to their content, thus making the document authoring process directly observable. This abundance of editing information allows us to come up with better models of term importance in documents, assuming that terms introduced earlier in the document life are more central to its topic. The recently proposed Revision History Analysis (Aji et al., 2010) captures this intuition to provide more accurate retrieval of versioned documents.

An even more promising research direction, however, is to distill the world knowledge from the structure and content of CGC repositories. This knowledge can give rise to new representations of texts beyond the conventional bag of words and allow reasoning about the meaning of texts at the level of concepts rather than individual words or phrases. Consider, for example, the following text fragment: “Wal-Mart supply chain goes real time.” Without relying on large amounts of external knowledge, it would be quite difficult for a computer to understand the meaning of this sentence. Explicit Semantic Analysis (ESA) (Gabrilovich and Markovitch, 2009) offers a way to consult Wikipedia in order to fetch highly relevant concepts such as “Sam Walton” (the Wal-Mart founder); “Sears,” “Target,” and “Albertsons” (prominent competitors of Wal-Mart); “United Food and Commercial Workers” (a labor union that has been trying to organize Wal-Mart’s workers); and “hypermarket” and “chain store” (relevant general concepts). Arguably, the most insightful concept generated by consulting Wikipedia is “RFID” (radio frequency identification), a technology extensively used by Wal-Mart to manage its stock. None of these concepts are explicitly mentioned in the given text fragment, yet when available they help shed light on the meaning of this short text.

In the remainder of this article, I first discuss using CGC repositories for computing semantic relatedness of words and then proceed to higher-level applications such as information retrieval.

COMPUTING SEMANTIC SIMILARITY OF WORDS AND TEXTS

How related are “cat” and “mouse?” And what about “preparing a manuscript” and “writing an article?” Reasoning about semantic relatedness of natural language utterances is routinely performed by humans but remains challenging for computers. Humans do not judge text relatedness merely at the level of text words. Words trigger reasoning at a much deeper level that manipulates concepts—the



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