Papers with computational pipeline
lingvis.io - A Linguistic Visual Analytics Framework (P19-3)
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Mennatallah El-Assady, Wolfgang Jentner, Fabian Sperrle, Rita Sevastjanova, Annette Hautli-Janisz, Miriam Butt, Daniel Keim
| Challenge: | Using a modular framework, linguistic visual analytics applications can be rapidly prototypized using a web-based framework. |
| Approach: | They propose a modular framework for rapid prototyping of linguistic, web-based, visual analytics applications. |
| Outcome: | The proposed framework supports rapid prototyping of linguistic, web-based, visual analytics applications. |
Annotating Research Infrastructure in Scientific Papers: An NLP-driven Approach (2023.acl-industry)
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Seyed Amin Tabatabaei, Georgios Cheirmpos, Marius Doornenbal, Alberto Zigoni, Veronique Moore, Georgios Tsatsaronis
| Challenge: | a pipeline is used to identify, extract and link research infrastructure used in scientific publications. |
| Approach: | They propose a natural language processing pipeline for the identification, extraction and linking of Research Infrastructure (RI) used in scientific publications. |
| Outcome: | The proposed pipeline can be used to identify, extract and link research infrastructure used in scientific publications. |
Social Meme-ing: Measuring Linguistic Variation in Memes (2024.naacl-long)
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| Challenge: | In this paper, we analyze memes as a form of language subject to the same kinds of sociolinguistic variation as other modalities, such as written language and speech. |
| Approach: | They propose a computational pipeline to cluster memes into templates and semantic variables, taking advantage of their multimodal structure to learn meme semantics from an unstructured dataset. |
| Outcome: | The proposed method uses 3.8M images from a reddit meme database to analyze linguistic variation in memes. |
Are Fairy Tales Fair? Analyzing Gender Bias in Temporal Narrative Event Chains of Children’s Fairy Tales (2023.acl-long)
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| Challenge: | Social biases and stereotypes are embedded in our culture through their presence in our stories. |
| Approach: | They propose a computational pipeline that automatically extracts a story’s temporal narrative verb-based event chain for each of its characters as well as character attributes such as gender. |
| Outcome: | The proposed framework extracts a story’s verb-based event chain for each of its characters as well as character attributes such as gender. |
The Automatic Extraction of Linguistic Biomarkers as a Viable Solution for the Early Diagnosis of Mental Disorders (2022.lrec-1)
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| Challenge: | Digital Linguistic Biomarkers extracted from spontaneous language productions proved to be very useful for the early detection of various mental disorders. |
| Approach: | They propose a computational pipeline for the automatic extraction of DLBs from speech samples and written texts. |
| Outcome: | The proposed pipeline is designed to extract DLBs from speech samples and written texts. |
The Noisy Path from Source to Citation: Measuring How Scholars Engage with Past Research (2025.acl-long)
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| Challenge: | Academic citations are widely used for evaluating research and tracing knowledge flows. |
| Approach: | They propose a computational pipeline to quantify citation fidelity at the sentence level by identifying citations in citing papers and corresponding claims in cited papers. |
| Outcome: | The proposed pipeline identifies citations in citing papers and the corresponding claims in cited papers and applies supervised models to measure fidelity at the sentence level. |