Papers with computational approach

17 papers
Computational Discovery of Chiasmus in Ancient Religious Text (2025.naacl-short)

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Challenge: chiasmus, or chiastic units, is a debated literary device in biblical texts . a computational approach to detect chiastes is shown to be efficient, but not efficient .
Approach: They propose a computational approach to detect chiasmus within Biblical passages . they leverage neural embeddings to capture lexical and semantic patterns associated with chiastics - using annotators to review a subset of the detected patterns.
Outcome: The proposed method achieves high inter-annotator agreement and system accuracy of 0.80 at verse level and 0.60 at half-verse level.
A Computational Approach to Quantifying Grammaticization of English Deverbal Prepositions (2024.lrec-main)

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Challenge: Linguistic studies have revealed important aspects of grammaticization of deverbal prepositions.
Approach: They propose a computational approach to measure the degree of grammaticization of deverbal prepositions based on corpus data.
Outcome: The proposed method correlates well with human judgements and supports previous findings in linguistics.
Understanding and Countering Stereotypes: A Computational Approach to the Stereotype Content Model (2021.acl-long)

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Challenge: Stereotypical language expresses widely-held beliefs about different social categories.
Approach: They propose a computational approach to interpreting stereotypes in text through the Stereotype Content Model (SCM), a comprehensive causal theory from social psychology.
Outcome: The proposed model compares favourably with survey-based studies in the psychological literature on stereotypes and shows that it is realistic and effective.
Casting Light on Invisible Cities: Computationally Engaging with Literary Criticism (N19-1)

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Challenge: Literary critics often attempt to uncover meaning in a single work of literature through careful reading and analysis.
Approach: They propose to use a literary theory to analyze Italo Calvino's novel Invisible Cities to leverage contextualized representations to embed each city's description and use unsupervised methods to cluster embeddings.
Outcome: The proposed method can be applied to Italo Calvino’s novel Invisible Cities . authors compare results to similarity judgments generated by human readers .
Is Nike female? Exploring the role of sound symbolism in predicting brand name gender (D18-1)

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Challenge: Existing research suggests that the sound of a person’s first name is associated with the person’ s gender, but no research has attempted to assess the gender of brand names.
Approach: They propose a machine-learning method that uses sound symbolism to assess the gender of brand names.
Outcome: The proposed method can predict gender of human first names with high accuracy . it uses linguistic features of name endings to predict gender .
Is the Red Square Big? MALeViC: Modeling Adjectives Leveraging Visual Contexts (D19-1)

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Challenge: gradable adjectives of size are relative, i.e., determined by the context.
Approach: They propose to model how the meaning of gradable adjectives of size can be learned from visually-grounded contexts by using four tasks to determine whether an object is ‘big’ or ‘small’.
Outcome: The proposed model can learn subtending the meaning of size adjectives, but their performance decreases while moving from simple to more complex tasks.
Singlish Message Paraphrasing: A Joint Task of Creole Translation and Text Normalization (2022.coling-1)

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Challenge: Existing computational approaches to translate languages or creoles back to standard English are challenging . lexical level normalization, syntactic level editing, and semantic level rewriting are key to a successful translation task.
Approach: They propose a computational task to parse Singlish into English using its dialects . they propose to use a dataset to normalize and edit the text to improve translation .
Outcome: The proposed model can improve translation performance and improve stance detection.
A Ship of Theseus: Curious Cases of Paraphrasing in LLM-Generated Texts (2024.acl-long)

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Challenge: Using a computational approach, we discover that diminishing performance in text classification models is closely associated with the extent of deviation from the original author’s style.
Approach: They propose to use large language models to determine whether a text retains original authorship when it undergoes numerous paraphrasing iterations.
Outcome: The results suggest that authorship should be task-dependent .
A Computational Exploration of Exaggeration (D18-1)

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Challenge: a new computational approach to exaggeration detection is needed for non-literal phenomena . a corpus of overstatements (or hyperboles) is used to detect exaggrements .
Approach: They propose a computational approach to detect exaggerated sentences using crowdsourcing data . they build a corpus containing overstatements and then evaluate models trained on HYPO .
Outcome: The proposed approach can detect exaggerated sentences using a crowdsourced dataset.
When People are Floods: Analyzing Dehumanizing Metaphors in Immigration Discourse with Large Language Models (2025.acl-long)

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Challenge: a computational approach to measure metaphorical language is based on immigration discourse on social media.
Approach: They propose a computational approach that leverages word-level and document-level signals to measure metaphor with respect to immigration discourse on social media.
Outcome: The proposed method measures metaphorical language in immigration discourse on social media.
BERT-based Classical Arabic Poetry Authorship Attribution (2025.coling-main)

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Challenge: AA in Arabic poetry has been a significant issue since the 9th century due to the loss of pre-Islamic poetry and the misattribution of post-Islamical works to earlier poets.
Approach: They propose a computational approach to authorship attribution in Arabic poetry using the entire Classical Arabic Poetry corpus for the first time.
Outcome: The proposed model achieves F1 scores ranging from 0.97 to 1.0 and was applied to four pre-Islamic misattribution cases.
A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support (2020.emnlp-main)

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Challenge: Empathy measurement has predominantly occurred in synchronous, face-to-face settings, and may not translate to asynchronous, text-based contexts.
Approach: They propose a computational approach to understanding how empathy is expressed in online mental health platforms.
Outcome: The proposed model can identify empathic conversations and extract rationales from them.
A Mapudüngun FST Morphological Analyser and its Web Interface (2022.lrec-1)

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Challenge: a computational tool for Mapudüngun language is developed using finite state technology . it is the first of its kind for the language and is available as a web service for free .
Approach: They propose to develop a morphological and phonological machine for Mapudüngun using finite state technology.
Outcome: The proposed system is the first of its kind for the Mapuche language and is available for public use through a web interface.
A Deeper (Autoregressive) Approach to Non-Convergent Discourse Parsing (2023.emnlp-main)

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Challenge: Existing frameworks for dialogic discourse parsing are not suitable for contentious discussions . authors propose a model for non-convergent discourse paring that does not require label collocation .
Approach: They propose a multi-label scheme for contentious dialog parsing that uses multiple labels . they propose combining embeddings of the utterance, context and the labels through GRN layers .
Outcome: The proposed model achieves comparable results with SOTA without label collocation and without training a unique architecture/model for each label.
Decoding Susceptibility: Modeling Misbelief to Misinformation Through a Computational Approach (2024.emnlp-main)

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Challenge: Existing studies on susceptibility to misinformation rely on self-reported beliefs, which can be subject to bias, expensive to collect, and challenging to scale for downstream applications.
Approach: They propose a computational approach to efficiently model users’ latent susceptibility levels by using demographic factors and political ideology as inputs.
Outcome: The proposed model shows that political leanings and other psychological factors exhibit varying degrees of association with susceptibility to COVID-19 misinformation.
Pater Incertus? There Is a Solution: Automatic Discrimination between Cognates and Borrowings for Romance Languages (2024.lrec-main)

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Challenge: Existing methods for discriminating between cognates and borrowings are difficult, but they provide a deeper insight into the history of a language and allow for a better characterization of language relatedness.
Approach: They propose a computational approach for discriminating between cognates and borrowings based on a comprehensive database of Romance cognates.
Outcome: The proposed approach is the most comprehensive in terms of covered languages.
Computational Analysis of Character Development in Holocaust Testimonies (2025.emnlp-main)

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Challenge: This work examines character development along the narrative timeline by analyzing changes in the protagonist’s views and behavior and the interplay between them.
Approach: They propose to analyze character development along the narrative timeline using a transcript of Holocaust survivor testimonies as a test case.
Outcome: The proposed approach characterizes changes in the protagonist’s views and behavior and the interplay between them.

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