Coherence and cohesion are crucial for organizing text semantics and syntax. They both may be described in terms of topic-focus structure, but the surface syntactic topic-focus structure does not coincide with that of deep semantics, and the automatic analysis of coherence which refers to the meaning of the whole text is complicated. The paper presents a Topic-Focus Annotating Parser (TFAP) that was trained on the corpus of Russian unprepared child oral narratives (213 narratives elicited by native Russian children aged from two years seven months to seven years six months). According to the results, children develop their narrative skills both in coherence and cohesion, but at the earlier stages of language acquisition, parsing errors reflect the speaker’s low level of narrative skills, while at the later stages (from five years seven months to seven years six months), when the basic rules of narrative organization are already acquired, parsing errors may be caused by the deficiencies of the parser. The topic-focus schemes we obtained support Leonid Sakharny’s theoretical approach to cognitive representation of coherence.

Original languageEnglish
Title of host publicationSpeech and Computer
Subtitle of host publication20th International Conference, SPECOM 2018, Leipzig, Germany, September 18–22, 2018, Proceedings
Place of PublicationCham
PublisherSpringer Nature
Pages145-154
ISBN (Electronic)978-3-319-99579-3
ISBN (Print)978-3-319-99578-6
DOIs
StatePublished - 1 Jan 2018
Event20th International Conference on Speech and Computer - Leipzig, Germany
Duration: 18 Sep 201822 Sep 2018

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Nature
Volume11096
ISSN (Print)0302-9743

Conference

Conference20th International Conference on Speech and Computer
Abbreviated titleSPECOM 2018
Country/TerritoryGermany
CityLeipzig
Period18/09/1822/09/18

    Research areas

  • Child language, Coherence, Cohesion, Spoken narrative, Topic-Focus Annotating Parser

    Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

ID: 71303139