He's not lying; you're not reading carefully. (Score 1)

Quoting from the summary:

'We think what's important about natural language is the mapping of words onto the concepts that users are looking for. But we don't think it's a big advance to be able to type something as a question as opposed to keywords ... understanding how words go together is important ... That's a natural-language aspect that we're focusing on. Most of what we do is at the word and phrase level; we're not concentrating on the sentence.'"

That is, he explicitly says that _most_ (that is, not all) of their work is at the word/phrase level. This implies that some is at levels of abstraction above that. They may not be "concentrating on the sentence" but that doesn't mean that they're ignoring it entirely. Furthermore, there are well-known ways of creating good approximations of the meaning of a document that don't consider word order at all. The classic is the TF-IDF model, but there are others (Latent Semantic Analysis, other types of topic models) that are richer and more descriptive. No, they don't capture everything about the semantics or pragmatics of a document, but they do well enough to (for instance) provide good predictors of the grade of an essay as assigned by a panel of human graders.