Sequence Package Analysis: A New Method for Intelligent Mining of Patient Dialog, Blogs and Help-line Calls

<p class="MsoNormal" style="text-align: left; margin: 0cm 0cm 0pt; layout-grid-mode: char;" align="left"><span class="text"><span style="font-family: ";Arial";,";sans-serif";; font-size: 9pt;">The ambiguities, repetitions and ellipses commonly found in natural language dialog continue to</span></span><span style="font-family: ";Arial";,";sans-serif";; font-size: 9pt;"> <span class="text">hinder speech (and text) analytic mining programs that glean business intelligence data from consumer help-line calls, or extract important medical diagnostic information from doctor-patient interviews or consumer-generated health-related blogs. This poses an even greater problem when such mining programs attempt to extract critical emotional data from natural language dialog. At present, speech (and text) analytic programs that mine natural language dialog for signs of distress, frustration, anger or other human emotions are still largely ineffective, because conventional speech systems that are limited to a set of key words and phrases cannot process speech as it actually occurs; if a speaker or blogger fails to use the word(s) found in the speech application’s vocabulary, the program yields a poor statistical word match (or no match). This paper shows how Sequence Package Analysis is informed by a set of algorithms – representing some of the more complex semantic aspects of communication in addition to syntax – that can interpret less than perfect natural speech, enhancing intelligent mining of recordings of doctor-patient interviews, customer care help-line calls, and consumer generated health-related blogs.</span></span></p>