Papers with psycholinguistic analysis

4 papers
Prefix Parsing is Just Parsing (2026.acl-short)

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Challenge: Existing prefix parsers are typically tied to particular parsing algorithms.
Approach: They propose a prefix grammar transformation that reduces prefix parsing to ordinary parsers . they propose enabling prediction of the next token by computing the next-token weight vector .
Outcome: The proposed method reduces prefix parsing to ordinary parsers without modification . the transformed grammar is only a small factor larger than the input .
TONY: an open-source TOolkit for Nlp in psYchology (2026.acl-demo)

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Challenge: Existing NLP tools are fragmented, closed-source, or difficult to use . a single sentence can convey emotion, social dynamics, cognitive states, and implicit attitudes .
Approach: They propose an open-source python TOolkit for NLP in clinical psychology.
Outcome: The TOolkit bridges traditional psycholinguistic analysis and modern NLP . it integrates interpretable lexical features with state-of-the-art lightweight transformer models . the toolkit is released under an open-source license and is evaluated through multiple MH–related datasets.
Psycholinguistic Tripartite Graph Network for Personality Detection (2021.acl-long)

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Challenge: Existing work on personality detection from online posts adopts multifarious deep neural networks to represent the posts and builds predictive models in a data-driven manner without the exploitation of psycholinguistic knowledge.
Approach: They propose a psycholinguistic knowledge-based tripartite graph network, TrigNet, which consists of a tripartitic graph network and a BERT-based graph initializer.
Outcome: The proposed graph network outperforms the existing state-of-the-art model by 3.47 and 2.10 points in average F1 on two datasets.
ScanDL: A Diffusion Model for Generating Synthetic Scanpaths on Texts (2023.emnlp-main)

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Challenge: Eye movements in reading are a key part of psycholinguistic research, but the lack of eye movement data and its unavailability at application time pose a major challenge for this line of research.
Approach: They propose a novel sequence-to-sequence diffusion model that generates synthetic scanpaths on texts by leveraging pre-trained word representations and jointly embedding both the stimulus text and the fixation sequence.
Outcome: The proposed model outperforms state-of-the-art models in psycholinguistic analysis and is able to exhibit human-like reading behavior.

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