This classifier learns patterns of similarity and divergence of a language's tokens across all reference languages, leading to high accuracy on in-domain n-grams from a legal corpus as well as out-of-domain tokens from an English-Egyptian Arabic code-mixing microblog corpus. The short-string language identification system extracts an n-gram, and selects the closest language out of 373 reference languages by using a Support Vector Machine (SVM) classifier trained on a matrix of language model measurements. The third is a modular part of speech tagger for multilingual code-mixing. The second is a machine transliteration system to convert the Arabizi chat alphabet into Arabic script. The first is a novel short-string language identification system that calculates our Indicator Value Signal. 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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