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A parser for medical free text reports has been developed that is based on a chemistry/physics inspired ldquofield theoryrdquo for word-word sentence-level dependencies. The transition from the linguistic world to the world of interacting particles with potential energies is guided by a psycholinguistics thought experiment related to the amount of ldquoworkrdquo required to bring a reference word into an anchored configuration of words. Calibration experiments involving four and five grams were conducted. Data from these experiments were used as a knowledge source for estimating field conditions for words in sentences sampled from a corpus of medical reports. The result of the parser is a dependency tree that represents the global minimum energy state of the system of words for a given sentence. The system was trained and tested on a corpus of radiology reports. Preliminary performance, as quantified by link recall and precision statistics, is 84.9% and 89.9%, respectively.