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http://www.truevert.com/

Our approach is to mimic the way that people learn language. When people learn a new word they learn its meaning by it relation to the other words in the sentence or paragraph. . . . Even if you learn it from a dictionary, its meaning is still from the context of other words. Similarly, when people understand a sentence, each word in the sentence helps to disambiguate the other words in the sentence. For example, consider the sentence, “the tree surgeon examined the young man’s palm.” By the time you get to the word “palm,” you have a pretty good idea what that word means.
Our system also learns meaning from the context. We provided the system with a set of green documents. These documents are broken into paragraphs and the word relationships within each paragraph are computed using our patent-pending modeling software. Each word in the paragraph becomes the context for the other words in the paragraph.
Then, when a user submits a query, that query is transmitted to the model and a set of additional terms, that are most closely related to that query are generated. All of these terms are then submitted to BOSS. The snippets that come back from BOSS are then re-ranked by according to the match between the snippet and the expanded and weighted list of query terms. The result is a set of pages that match the query in the context.