1. Evaluating distinctiveness of document

    Two document sets are compared in natural language processing and the distinctiveness of each constituent element (such as a sentence, term or phrase) of one document set is evaluated by dividing both the target and comparison documents into document segments, constructing the sentence vector of each document segment whose components are the occurring frequencies of terms occurring in the document segment, and projecting all the sentence vectors of both the documents on a projection axis to find a projection axis which maximizes a ratio equal to: (squared sum of projected values originating from the target document)/(squared sum of projected ...
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      Discourse, Entailment, Machine Translation, NER, Parsing, Segmentation, Semantic, Sentiment, Summarization, WSD
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