Kristopher Kyle


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Span Identification of Epistemic Stance-Taking in Academic Written English
Masaki Eguchi | Kristopher Kyle
Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)

Responding to the increasing need for automated writing evaluation (AWE) systems to assess language use beyond lexis and grammar (Burstein et al., 2016), we introduce a new approach to identify rhetorical features of stance in academic English writing. Drawing on the discourse-analytic framework of engagement in the Appraisal analysis (Martin & White, 2005), we manually annotated 4,688 sentences (126,411 tokens) for eight rhetorical stance categories (e.g., PROCLAIM, ATTRIBUTION) and additional discourse elements. We then report an experiment to train machine learning models to identify and categorize the spans of these stance expressions. The best-performing model (RoBERTa + LSTM) achieved macro-averaged F1 of .7208 in the span identification of stance-taking expressions, slightly outperforming the intercoder reliability estimates before adjudication (F1 = .6629).

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An Argument Structure Construction Treebank
Kristopher Kyle | Hakyung Sung
Proceedings of the First International Workshop on Construction Grammars and NLP (CxGs+NLP, GURT/SyntaxFest 2023)

In this paper we introduce a freely available treebank that includes argument structure construction (ASC) annotation. We then use the treebank to train probabilistic annotation models that rely on verb lemmas and/ or syntactic frames. We also use the treebank data to train a highly accurate transformer-based annotation model (F1 = 91.8%). Future directions for the development of the treebank and annotation models are discussed.


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A Dependency Treebank of Spoken Second Language English
Kristopher Kyle | Masaki Eguchi | Aaron Miller | Theodore Sither
Proceedings of the 17th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2022)

In this paper, we introduce a dependency treebank of spoken second language (L2) English that is annotated with part of speech (Penn POS) tags and syntactic dependencies (Universal Dependencies). We then evaluate the degree to which the use of this treebank as training data affects POS and UD annotation accuracy for L1 web texts, L2 written texts, and L2 spoken texts as compared to models trained on L1 texts only.


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Native Language Identification: A Key N-gram Category Approach
Kristopher Kyle | Scott Crossley | Jianmin Dai | Danielle McNamara
Proceedings of the Eighth Workshop on Innovative Use of NLP for Building Educational Applications