Using Automatic Measurements of Morphological Features to Distinguish Spoken and Written Discourse.

EasyChair Preprint 3912

Abstract

Morphological accuracy, complexity, and awareness are often considered important benchmarks in language acquisition and performance. Though morphology is underexplored in natural language processing, automatic measurement of morphological complexity in English can lend insights into various aspects of text and discourse processing. This study introduces a tool to automatically process morphological complexity in texts. Spoken and written English-learner corpora were analyzed using the tool to explore the relationship between morphological complexity and discourse types.

Keyphrases: Natural Language Processing, learner corpora, morphology

@booklet{EasyChair:3912,
  author    = {Rurik Tywoniw and Scott Crossley},
  title     = {Using Automatic Measurements of Morphological Features to Distinguish Spoken and Written Discourse.},
  howpublished = {EasyChair Preprint 3912},
  year      = {EasyChair, 2020}}