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Automatic Detection and Visualization of Information Structure in English

Published: 27 June 2023 Publication History

Abstract

This paper describes the design and development of an online tool that identifies and visualizes information structure in user-submitted texts written in English. Non-native users of English find it difficult to distinguish between structures that are marked and unmarked. Markedness is evaluated based on acceptability and frequency of a sequence of word tokens. Marked sentences stand out as being unnatural to native speakers, but few native speakers can explain why. Information structure can, however, frequently explain markedness. The tool detects the three principles of information structure: information focus, information flow and end weight. Information focus explains the sequence of elements within sentences. Information flow explains the sequence of elements within paragraphs. End weight explains the relative position of phrases and clauses within a sentence. Through exposure to these principles in context, this tool aims to help writers of English understand which structural language features may be judged as marked.

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  • (2023)Linguistic-first approach to learning Python for natural language generation: Problem breakdown to pseudocodeETLTC-ICETM2023 INTERNATIONAL CONFERENCE PROCEEDINGS: ICT Integration in Technical Education & Entertainment Technologies and Management10.1063/5.0183649(020002)Online publication date: 2023

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      NLPIR '22: Proceedings of the 2022 6th International Conference on Natural Language Processing and Information Retrieval
      December 2022
      241 pages
      ISBN:9781450397629
      DOI:10.1145/3582768
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 27 June 2023

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      Author Tags

      1. information structure
      2. online learning platform

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      • (2023)Linguistic-first approach to learning Python for natural language generation: Problem breakdown to pseudocodeETLTC-ICETM2023 INTERNATIONAL CONFERENCE PROCEEDINGS: ICT Integration in Technical Education & Entertainment Technologies and Management10.1063/5.0183649(020002)Online publication date: 2023

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