However, experience has revealed that the ambiguities that afflict the desired mappings are far too numerous, subtle, and interrelated to be amenable to heuristic arbitration. Instead, linguistic phenomena need to be treated as effectively stochastic, and the distributional properties resulting from these stochastic processes need to be systematically exploited to arrive at reasonably reliable hypotheses about underlying structure. (The Geoff Pullum quote above is relevant to this point: The inadmissiblity of contracting the first occurrence of I am to I'm in “I'd rather be hated for who I am, than loved for who I am not” is not easily ascribed to any grammatical principle, yet—based on positive evidence alone—becomes part of our knowledge of English usage.) Thus the emphasis has shifted, at least for the time being, to viewing NLP as a problem of uncertain inference and learning in a stochastic setting.