Ranking parser for a natural language processing system

Details Ranking parser for a natural language processing system Ranking parser for a natural language processing system

2006-11-28

2006-11-28

Abebe, Daniel (Department: 2655)

Data processing: speech signal processing, linguistics, language

Speech signal processing

Recognition

C704S257000

Reexamination Certificate

active

07143036

ABSTRACT:
A natural language parse ranker of a natural language processing (NLP) system employs a goodness function to rank the possible grammatically valid parses of an utterance. The goodness function generates a statistical goodness measure (SGM) for each valid parse. The parse ranker orders the parses based upon their SGM values. It presents the parse with the greatest SGM value as the one that most likely represents the intended meaning of the speaker. The goodness function of this parse ranker is highly accurate in representing the intended meaning of a speaker. It also has reasonable training data requirements. With this parse ranker, the SGM of a particular parse is the combination of all of the probabilities of each node within the parse tree of such parse. The probability at a given node is the probability of taking a transition (“grammar rule”) at that point. The probability at a node is conditioned on highly predicative linguistic phenomena, such as “phrase levels,” “null transitions,” and “syntactic history”.

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