Papers with Psycholinguistics
Modeling Referential Gaze in Task-oriented Settings of Varying Referential Complexity (2022.findings-aacl)
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| Challenge: | Referential gaze is a fundamental phenomenon for psycholinguistics and human-human communication. |
| Approach: | They propose a multimodal NLP task to predict when the gaze is referential . they train a sequential attention-based LSTM model and a transformer encoder architecture to model referential gaze and transfer gaze features to unseen situated settings . |
| Outcome: | The proposed model can be applied to situations with different referential complexities . the proposed model is based on an attention-based LSTM model and a multivariate transformer encoder architecture . |
Using the RUPEX Multichannel Corpus in a Pilot fMRI Study on Speech Disfluencies (2020.lrec-1)
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Katerina Smirnova, Nikolay Korotaev, Yana Panikratova, Irina Lebedeva, Ekaterina Pechenkova, Olga Fedorova
| Challenge: | Numerous classifications of disfluencies have been proposed and/or implemented in annotating speech corpora. |
| Approach: | They propose to use Russian multichannel corpus RUPEX to create fragments of speech disfluencies and their clusters. |
| Outcome: | The proposed method allows to create fragments in terms of requirements for the fMRI BOLD temporal resolution. |
A Case Study of Analysis of Construals in Language on Social Media Surrounding a Crisis Event (2021.acl-srw)
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| Challenge: | construal level theory (CLT) uses concreteness as covariate to analyze language around political import events. |
| Approach: | They propose to include psycholinguistic measures of concreteness as covariates in topic models to analyze the language around an event of political import. |
| Outcome: | The proposed model incorporates measures of concreteness as covariates to inform the analysis of language around the 2017 rally. |
Deconvolutional Time Series Regression: A Technique for Modeling Temporally Diffuse Effects (D18-1)
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| Challenge: | a confound exists in time series data that violates assumptions of linear models . time series may violate assumptions through temporal diffusion . |
| Approach: | They propose a statistical model that borrows from digital signal processing to fit latent impulse response functions of arbitrary shape. |
| Outcome: | The proposed model recovers true latent IRFs and improves prediction quality . it is based on a new technique that borrows from digital signal processing . |
Cifu: a Frequency Lexicon of Hong Kong Cantonese (2020.lrec-1)
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| Challenge: | lexical database for Hong Kong Cantonese offers phonological and orthographic information, frequency measures, and lexically neighborhood information for lexicals in HKC. |
| Approach: | They introduce a lexical database for Hong Kong Cantonese that offers phonological and orthographic information, frequency measures, and lexically neighborhood information for lexicals in HKC. |
| Outcome: | The proposed lexical database for Hong Kong Cantonese offers phonological and orthographic information, frequency measures, and lexically neighborhood information. |
The CLARIN Knowledge Centre for Atypical Communication Expertise (2020.lrec-1)
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| Challenge: | ACE is a new knowledge center for Atypical communication experts . it is located at the Centre for Language and Speech Technology (CLST) at Radboud University . |
| Approach: | They introduce a new CLARIN Knowledge Center called the K-Centre for Atypical Communication Expertise (ACE) ACE closely collaborates with The Language Archive at the Max Planck Institute for Psycholinguistics to safeguard GDPR-compliant data storage and access. |
| Outcome: | The new CLARIN Knowledge Center is the K-Centre for Atypical Communication Expertise (ACE) ACE closely collaborates with The Language Archive (TLA) at the Max Planck Institute for Psycholinguistics in order to safeguard GDPR-compliant data storage and access. |
Dedicated Language Resources for Interdisciplinary Research on Multiword Expressions: Best Thing since Sliced Bread (2020.lrec-1)
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| Challenge: | Multiword expressions are challenging for disciplines like NLP, psycholinguistics and second language acquisition due to their more or less fixed character. |
| Approach: | They propose to develop tools and language resources that are crucial for multifaceted research. |
| Outcome: | The proposed tools and language resources are crucial for this kind of multifaceted research. |
A Survey of Automatic Personality Detection from Texts (2020.coling-main)
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| Challenge: | Personality profiling has long been used in psychology to predict life outcomes. |
| Approach: | They present the trajectory of automatic personality detection from purely psychology approaches to the latest purely natural language processing approaches on large social media datasets. |
| Outcome: | The proposed models have been compared with the most recent approaches on large social media datasets. |
Detecting Subtle Differences between Human and Model Languages Using Spectrum of Relative Likelihood (2024.emnlp-main)
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| Challenge: | Existing methods for detecting modelgenerated texts from human texts are limited by the fact that absolute likelihood values of texts are bound to certain linguistic and cognitive constraints. |
| Approach: | They propose to use relative likelihood values instead of absolute ones to extract useful features from the spectrum-view of likelihood for the human-model text detection task. |
| Outcome: | The proposed method can reveal subtle differences between human and model languages, which find theoretical roots in psycholinguistics studies. |
Is In-Context Learning a Type of Error-Driven Learning? Evidence from the Inverse Frequency Effect in Structural Priming (2025.naacl-long)
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| Challenge: | Recent pre-trained large language models have shown the capacity to perform in-context learning (ICL) this capability could provide a way to bridge the divide between language models and humans. |
| Approach: | They propose a new way of diagnosing whether ICL is error-driven learning . they simulated structural priming with ICL and found the effect was stronger . |
| Outcome: | The proposed method is based on the inverse frequency effect (IFE) phenomenon is similar to error-driven learning in large language models . |