| Challenge: | Existing methods for text analysis are not specifically designed for identifying emergent concepts, instead applying general-purpose techniques that do not account for distinct temporal patterns associated with conceptual emergence. |
| Approach: | They propose a method to identify emerging concepts in large text corpora by analyzing changes in the heatmaps of the underlying embedding space. |
| Outcome: | The proposed method outperforms existing methods by analyzing speeches in the U.S. Senate from 1941 to 2015. |
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On the Distribution of Deep Clausal Embeddings: A Large Cross-linguistic Study (P19-1)
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| Challenge: | Empirical evidence on the prevalence and limits of embeddings has been based on either laboratory setups or corpus data of relatively limited size. |
| Approach: | They use large, dependency-parsed corpora to capture clausal embedding through dependency graphs and assess their distribution. |
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A Brief Survey of Textual Dialogue Corpora (2022.lrec-1)
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| Challenge: | Several dialogue corpora are available for research purposes, but they do not cover all the necessities of real-world applications. |
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Findings of the Association for Computational Linguistics: EMNLP 2021 (2021.findings-emnlp)
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| Challenge: | . - (EN) |
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Pragmatics in the Era of Large Language Models: A Survey on Datasets, Evaluation, Opportunities and Challenges (2025.acl-long)
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Bolei Ma, Yuting Li, Wei Zhou, Ziwei Gong, Yang Janet Liu, Katja Jasinskaja, Annemarie Friedrich, Julia Hirschberg, Frauke Kreuter, Barbara Plank
| Challenge: | linguistics studies how context influences meaning of language and how people use it to convey implied meanings, emotions, and intentions. |
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An Analysis of Negation in Natural Language Understanding Corpora (2022.acl-short)
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| Challenge: | Using annotator-generated examples, one can evaluate systems with synthetic language that is not representative of language in the wild. |
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How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances (2023.emnlp-main)
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| Challenge: | Large language models (LLMs) are impressive in solving tasks, but they can quickly be outdated after deployment. |
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Findings of the Association for Computational Linguistics: EMNLP 2025 (2025.findings-emnlp)
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Findings of the Association for Computational Linguistics: EMNLP 2022 (2022.findings-emnlp)
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HistLens: Mapping Idea Change across Concepts and Corpora (2026.acl-long)
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| Challenge: | Existing approaches to diachronic semantics and discourse analysis focus on a single concept or corpus, argues a new paper. |
| Approach: | They propose a framework for multi-concept, multi-corpus conceptual-history analysis that decomposes concept representations into interpretable features and tracks activation dynamics over time and across sources. |
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