Papers with Thematic analysis

2 papers
Assessing the Efficacy of Grammar Error Correction: A Human Evaluation Approach in the Japanese Context (2024.lrec-main)

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Challenge: Using an automatic annotation toolkit, we evaluated the performance of the sequence tagging grammar error detection and correction model (SeqTagger) using Japanese university students’ writing samples.
Approach: They evaluated the performance of the state-of-the-art sequence tagging grammar error detection and correction model using Japanese university students’ writing samples.
Outcome: The proposed model shows a high precision but conservativeness in error detection and correction.
LLM-in-the-loop: Leveraging Large Language Model for Thematic Analysis (2023.findings-emnlp)

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Challenge: Recent research shows that large language models can replicate human-like behavior in various tasks.
Approach: They propose a framework for human-LLM collaboration to conduct TA with in-context learning (ICL) they propose to use survey data to frame discussions with an LLM to generate a final codebook for TA.
Outcome: The proposed framework outperforms crowd workers on text-annotation tasks and yields similar coding quality to that of human coders but reduces TA’s labor and time demands.

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