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An important characteristic of English written text is the abundance of nouncompounds - sequences of nouns acting as a single noun, e.g., colon cancertumor suppressor protein. While eventually mastered by domain experts, theirinterpretation poses a major challenge for automated analysis. Understandingnoun compounds' syntax and semantics is important for many natural languageapplications, including question answering, machine translation, informationretrieval, and information extraction. I address the problem of noun compoundssyntax by means of novel, highly accurate unsupervised and lightly supervisedalgorithms using the Web as a corpus and search engines as interfaces to thatcorpus. Traditionally the Web has been viewed as a source of page hit counts,used as an estimate for n-gram word frequencies. I extend this approach byintroducing novel surface features and paraphrases, which yieldstate-of-the-art results for the task of noun compound bracketing. I also showhow these kinds of features can be applied to other structural ambiguityproblems, like prepositional phrase attachment and noun phrase coordination. Iaddress noun compound semantics by automatically generating paraphrasing verbsand prepositions that make explicit the hidden semantic relations between thenouns in a noun compound. I also demonstrate how these paraphrasing verbs canbe used to solve various relational similarity problems, and how paraphrasingnoun compounds can improve machine translation.

Using the Web as an Implicit Training Set: Application to Noun Compound Syntax and Semantics