However, the simplest models of this type do not model the statistics of actual language corpora very accurately, because the expansion probabilities for a given phrase type (or part of speech) X ignore the surrounding phrasal context and the more detailed properties (such as head words) of the generated constituents. Yet context and detailed properties are very influential; for example, whether the final prepositional phrase in “She detected a star with {binoculars, planets}” modifies detected or planets is very dependent on word choice. Such modeling inaccuracies lead to parsing inaccuracies (see next subsection), and therefore generative grammar models have been refined in various ways, for example (in so-called lexicalized models) allowing for specification of particular phrasal head words in rules, or (in tree substitution grammars) allowing expansion of nonterminals into subtrees of depth 2 or more. Nevertheless, it seems likely that fully accurate distributional modeling of language would need to take account of semantic content, discourse structure, and intentions in communication, not only of phrase structure. Possibly construction grammars (e.g., Goldberg 2003), which emphasize the coupling between the entrenched patterns of language (including ordinary phrase structure, clichés, and idioms) and their meanings and discourse function, will provide a conceptual basis for building statistical  models of language that are sufficiently accurate to enable more nearly human-like parsing accuracy. 