Analyzing learner language: the case of the Hebrew essay corpus-Reference-Cited by-同舟云学术

Analyzing learner language: the case of the Hebrew essay corpus

Published:2023-01-16 Issue: Volume: Page:
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Author:

Gafni Chen¹,Sheinfux Livnat Herzig¹,Klunover Hadar¹,Prior Anat¹,Wintner Shuly¹

Affiliation:

1. University of Haifa

Abstract

Abstract We present the Hebrew Essay Corpus: an annotated corpus of Hebrew language argumentative essays authored by prospective higher-education students. The corpus includes both essays by native speakers, written as part of the psychometric exam that is used to assess their future success in academic studies; and essays authored by non-native speakers, with three different native languages, that were written as part of a language aptitude test. The corpus is uniformly encoded and stored. The non-native essays were annotated with target hypotheses whose main goal is to make the texts amenable to automatic processing (morphological and syntactic analysis). The corpus is available for research purposes upon request. We describe the corpus and the error correction and annotation schemes used in its analysis. In addition to introducing this new resource, we discuss the challenges of identifying and analyzing non-native language use in general, and propose various ways for dealing with these challenges.

Publisher

Research Square Platform LLC

Reference42 articles.

1. Abu Baker, R. (2016). Hashpa’at leshon ha-em ha-’aravit al diburam ve-al ktivatam shel studentim arvim be-mixlala dovert aravit.Ivrit be-Kavana T’hila,63–69.

2. Orthographic Representation and Phonemic Segmentation in Skilled Readers: A Cross-Language Comparison;Ben-Dror I;Psychological Science,1995

3. Bender, E. M., & Friedman, B. (2018). Toward Mitigating System Bias and Enabling Better Science. Transactions of the Association for Computational Linguistics, 6, 587–604. https://doi.org/10.1162/tacl_a_00041. Data Statements for Natural Language Processing:.

4. Processing lexical ambiguity and visual word recognition in a deep orthography;Bentin S;Memory & Cognition,1987

5. Bergsma, S., Post, M., & Yarowsky, D. (2012). Stylometric analysis of scientific articles. Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference, 327–337.