KR890013549A - 품사결정과 이용방법 - Google Patents

품사결정과 이용방법 Download PDF

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KR890013549A
KR890013549A KR1019890001364A KR890001364A KR890013549A KR 890013549 A KR890013549 A KR 890013549A KR 1019890001364 A KR1019890001364 A KR 1019890001364A KR 890001364 A KR890001364 A KR 890001364A KR 890013549 A KR890013549 A KR 890013549A
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speech
word
message
probability
contextual
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워드 처어치 케니스
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엘리 웨이스
아메리칸 텔리폰 앤드 텔리그라프 캄파니
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    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/16Sound input; Sound output
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00Handling natural language data
    • G06F40/20Natural language analysis
    • G06F40/205Parsing
    • G06F40/211Syntactic parsing, e.g. based on context-free grammar [CFG] or unification grammars
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
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    • G06COMPUTING; CALCULATING OR COUNTING
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    • G06F40/20Natural language analysis
    • G06F40/253Grammatical analysis; Style critique
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00Handling natural language data
    • G06F40/20Natural language analysis
    • G06F40/279Recognition of textual entities
    • G06F40/284Lexical analysis, e.g. tokenisation or collocates
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00Handling natural language data
    • G06F40/20Natural language analysis
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    • G06F40/289Phrasal analysis, e.g. finite state techniques or chunking
    • GPHYSICS
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    • G09B19/00Teaching not covered by other main groups of this subclass
    • G09B19/04Speaking
    • GPHYSICS
    • G10MUSICAL INSTRUMENTS; ACOUSTICS
    • G10LSPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L13/00Speech synthesis; Text to speech systems
    • G10L13/08Text analysis or generation of parameters for speech synthesis out of text, e.g. grapheme to phoneme translation, prosody generation or stress or intonation determination
    • G10L13/10Prosody rules derived from text; Stress or intonation
    • GPHYSICS
    • G10MUSICAL INSTRUMENTS; ACOUSTICS
    • G10LSPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L13/00Speech synthesis; Text to speech systems
    • G10L13/02Methods for producing synthetic speech; Speech synthesisers
    • G10L13/04Details of speech synthesis systems, e.g. synthesiser structure or memory management

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Abstract

내용 없음.

Description

품사결정과 이용방법
본 내용은 요부공개 건이므로 전문내용을 수록하지 않았음
제 1 도는 본 발명에 품사 지정 방법의 흐름도,
제 2 도는 본 발명에 따른 명사구 분석 방법의 흐름도,
제 3 도는 제 1 도 및 제 2 도의 방법을 이용하는 음성 합성기를 보인 블록도.

Claims (8)

  1. 메세지의 저장된 표현들을 전자적으로 판독하는 단계와 ; 각 단어가 특정 품사로될 어휘상 확율을 생성하는 단계와 ; 상기 대상 단어에 대해 상기 어휘상 확율에 응답하고 적어도 하나의 이웃하는 단어가 특정품사로된 문맥상의 확율에 응답하여, 상기 대상 단어가 특정 품사로될 상기 문맥상의 확율을 선택하는 단계를 포함하는 상기 메세지 내의 단어들에 대해 품사를 지정하는 자동화된 방법에 있어서 ; 상기 각 단어의 어휘상 확율을 생성하는 상기 단계가 적어도 어떤 단어에 대한 품사 빈도를 보정하는 단계를 포함하여, 상기 메세지의 비언어적인 기록으로 절대적으로 결정된 발생 빈도를 가지는 단어로 구두점, 문장 전후의 공란 및 어떤 단어들을 표시하는 단계를 포함하고 ; 상기 선택단계가 적어도 다음 단어를 포함하는 근접단의 품사에 관련된 상기 문맥상 확율을 최대화하는 단계를 포함하는 것을 특징으로 하는 메세지 내의 단어들에 대해 품사를 지정하는 자동화된 방법.
  2. 제1항에 있어서, 모든 가능한 명사구 경계를 지정하는 단계와 ; 쌍을 이루지 않을 모든 경계를 제거하는 단계와 ; 문맥상 명사구 경계 확율을 최적화 하는 단계를 특징으로 하는 메세지 내의 단어들에 대해 품사를 지정하는 자동화된 방법.
  3. 제2항에 있어서, 상기 최적화 단계의 결과에 의존하여 단어 강세를 지정하는 단계를 특징으로 하는 메세지 내의 단어들에 대해 품사를 지정하는 자동화된 방법.
  4. 제3항에 있어서, 상기 지정된 단어 강세에 응답하여 상기 메세지에 대응하는 음성을 합성하기 위한 수단을 특징으로 하는 메세지 내의 단어들에 대해 품사를 지정하는 자동화된 방법.
  5. 제2항에 있어서, 메세지 내의 단어들에 대해 가장 높게 선택된 문맥상 확율을 이용하여 상기 메세지 내의 문맥상의 오류를 검출하기 위한 단계를 특징으로 하는 메세지 내의 단어들에 대해 품사를 지정하는 자동화된 방법.
  6. 메세지 내에서 각 명사 주위의 단어들이 명사구의 일부가 될 수 있는 것인가를 평가하는 단계와 그 결과의 평가를 이용하는 단계를 포함하는, 적당한 확율로 품사가 지정된 명사구의 시작과 종료를 상기 메세지 내에서 결정하기 위한 자동화된 방법에 있어서 ; 모든 가능한 명사구 경계를 지정하는 단계와 ; 쌍을 이루지 않은 모든 경계를 제거하는 단계와 ; 문맥상의 명사구 경계 확율을 최적화 하는 단계를 특징으로 하는 명사구의 시작과 종료를 상기 메세지 내에서 결정하기 위한 자동화된 방법.
  7. 제1항 또는 제6항에 있어서, 근접단어들의 품사에 관하여 “n-그램”분석에 의해 상기 메세지 내의 품사를 지정하는 단계가 상기 메세지의 비언어적인 기록으로서 절대적으로 결정된 발생빈도를 가지는 단어로서 어떤 비단어들을 표시하는 단계와 ; 상기 비단어의 용도를 달리하는 문맥상 품사 확율에 관련하여, 상기 메세지 내의 서로 근접한 단어가 특정 품사로 될 정규화된 문맥상의 최적확율을 계산하는 단계(여기에서, 상기 정규화된 문맥상의 확율은 “트라이그램”품사 확율을 “바이그램”품사 확율을 나눈 것으로, 이들 트라이그램 및 바이그램은 공란을 포함하는 문장의 끝에서 시작하는 것에 의해 결정된다)를 포함하는 것을 특징으로 하는 자동화된 방법.
  8. 제1항에 있어서, 상기 생선 단계는 특정 품사로서의 발생빈도가 비교적 낮은 단어의 품사 활용에 대해 사전을 참조하여 빈도를 간략화 하는 단계를 포함하고 ; 상기 선택 단계는 어휘상 확율과 문맥상 확율의 곱을 결정하는 단계와, 여기서, 상기 어휘상 확율은 특정 품사로서의 상기 단어의 발생 빈도를 모든 품사로서의 그의 발생 빈도로 나는 몫으로 계산되고, 상기 문맥상 확율은 “트라이그램”빈도를 “바이그램”빈도는 두 개의 후속단어에 대해 기결정된 두 개의 후속 품사를 갖는 특정 품사의 발생 빈도이고, 상기 “바이그램”빈도는 두 번째 후속단어에 대해 기결정된 두 번째 품사와 연속하는 첫 번째 후속단어의 특정 품사 발생빈도이고, 동일한 단어에 대해 선행 곱을 초과하는 곱을 유지하는 단계를 포함하여 다수의 가능한 품사의 조합에 대하여 상기 결정 단계를 반복하는 단계를 더 포함하는 것을 특징으로 하는 메세지 내의 단어들에 대해 품사를 지정하는 자동화된 방법.
    ※ 참고사항 : 최초출원 내용에 의하여 공개하는 것임.
KR1019890001364A 1988-02-05 1989-02-04 품사결정과 이용방법 KR970006402B1 (ko)

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US07/152,740 US5146405A (en) 1988-02-05 1988-02-05 Methods for part-of-speech determination and usage
US152,740 1988-02-05
US152740 1988-02-05

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KR970006402B1 KR970006402B1 (ko) 1997-04-28

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US (1) US5146405A (ko)
EP (1) EP0327266B1 (ko)
JP (1) JPH0769910B2 (ko)
KR (1) KR970006402B1 (ko)
AU (1) AU617749B2 (ko)
CA (1) CA1301345C (ko)
DE (1) DE68923981T2 (ko)
ES (1) ES2076952T3 (ko)
IN (1) IN175380B (ko)

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