To exemplify the framework, this dissertation makes three additional contributions. At the end of the pipeline, feature signals are unified to produce a single annotated output stream. As the event controller indicates each domain change with an event signal, pipeline processes assigned to specific indicator function values are executed to process the segment, and add additional feature signals to the feature signal stack. The event controller can activate slowly over large spans of text, or rapidly and intrasententially. This feature signal is monitored for domain changes by an event controller, which segments the stream into feature chunks. At every word, an Indicator Function calculates a quantitative feature signal we call an Indicator Value Signal, that runs in parallel to the input stream. A tokenized text input stream is received by the system. ![]() The system reduces natural language processing (NLP) ambiguity by segmenting text by domain, allowing for domain-specific downstream processes to analyze each segment independently. This dissertation demonstrates a framework for incremental model selection and processing of highly variant speech transcripts and user-generated text.
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