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    • 1. 发明专利
    • SYSTEMS AND METHODS FOR ADAPTIVE PROPER NAME ENTITY RECOGNITION AND UNDERSTANDING
    • AU2022263497A1
    • 2022-12-22
    • AU2022263497
    • 2022-11-02
    • PROMPTU SYSTEMS CORP
    • PRINTZ HARRY WILLIAM
    • G10L15/02G01C21/36G06F16/332G06F40/295G10L15/18G10L15/19G10L15/22
    • Abstract A computer-implemented method for recognizing and understanding spoken commands that include one or more proper name entities, comprising: receiving an utterance from a user, performing primary automatic speech recognition (ASR) processing upon said utterance with a primary automatic speech recognizer to output a dataset comprising at least a sequence of nominal transcribed words and putative start and end times for each nominal transcribed word within said utterance, performing understanding processing upon said dataset with a natural language understanding (NLU) processor to generate and augment the dataset with a nominal meaning for the utterance and to determine putative presence and type of one or more spoken proper name entities within said utterance, wherein a contiguous section of audio within said utterance corresponding to each putative proper name entity, as determined from said start and end times of the words of the putative proper name entity as transcribed by the primary automatic speech recognizer, comprises an acoustic span, performing secondary automatic speech recognition (ASR) processing upon each said acoustic span with a secondary automatic speech recognizer, in each instance said secondary automatic speech recognizer specialized to process a given putative type of acoustic span to generate a nominal correct transcription and associated meaning for each said acoustic span, substituting the nominal correct transcription and associated meaning obtained from each secondary recognition as appropriate within the dataset to revise the results of the primary automatic speech recognizer and natural language understanding processor and to create a plurality of complete transcriptions and associated meanings, preparing a complete hypothesis ranking grammar comprised of said plurality of complete transcriptions and decoding the utterance against said complete hypothesis ranking grammar to determine an acoustic confidence score for each complete transcription, determining, for each acoustic span of each complete transcription, an NLU confidence score for each transcription of each acoustic span, normalizing said NLU confidence scores across the plurality of complete transcriptions to determine a normalized NLU confidence score of each complete transcription, combining said acoustic confidence score and NLU confidence score of each complete transcription to generate a final confidence score that each complete transcription and associated meaning is correct, which is used to rank the plurality of aforesaid complete transcriptions and associated meanings, and outputting a ranked list of complete transcriptions and associated meanings for the entire utterance.
    • 3. 发明专利
    • Method, system and apparatus for extracting entity words of diseases and their corresponding laboratory indicators from Chinese medical texts
    • AU2021106425A4
    • 2021-11-04
    • AU2021106425
    • 2021-08-22
    • FENG HONGHAIFENG RUI MS
    • FENG HONGHAIWEI YAJUHOU RUIHUILI JUNZHOU PENGCHENGLU XUZHAO
    • G06F40/295G06F40/279G06F40/30G09B17/02G16H70/20
    • Method and system and apparatus for extracting entity words of diseases and their corresponding laboratory indicators from Chinese medical texts The present invention discloses a method and system and device for extracting disease and laboratory index entities from Chinese medical texts and extracting relationships between their entities, and relates to the field of information extraction. It includes three major parts, the first consists of the reading subsystem that mainly contains the modules read by the computer. The second consists of the computation subsystem that mainly captures four major entity components, that is, laboratory indicators, abnormal values, relational words, and disease names after decomposing the sentence components. Every entity component is a set that is composed of some words or terms. The first step is inputting three of the four initial word sets to learn the left relational word set. Once the relational word set is updated, it is taken as one of the new inputs, and taking one of the previous three word sets as the one being learned. The iteration is run until every word set cannot be updated. The third consists of an output subsystem that contains two parts: a storage unit and an output unit. The storage one is the set of relevant medical entity words, and the output one displays the relationship between the relevant entities. System for extracting entity words for diseases and their corresponding lab indicators from medical texts Reading Computing output subsystem subsystems Subsystem Module Decont Inform for Informat aminat action or Reading Input ion ion at Storage Output storing Module Unit matching verific extract Unit Unit 1mitial .oul .nt mthi ei ion Untni values g unit ation unit unit Figure 1: System Block Diagram