Adaptive and scalable method for resolving natural language ambiguities

This invention describes a computer method for figuring out the correct meaning of words in text that can have multiple meanings. It does this by using a step-by-step process, starting with basic analysis and adding more detail as needed, and uses confidence scores to track how sure it is about its decisions. Specifically, the method trains computer programs called probabilistic classifiers using examples where the correct meaning is already labeled, and then uses these to process new text by breaking it into parts and identifying their likely meanings.

Why it matters: Filed before the widespread adoption of deep learning and large language models, this invention's reliance on probabilistic classifiers for ambiguity resolution has been largely superseded by more advanced and powerful NLP techniques. While the problem remains, the specific methods described in the claims are now less effective than modern approaches.

Status: In forceA lapse was cured by a late fee and protection was reinstated.
How hard to build: SpecializedSoftware logic, training data, probabilistic classifiers

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