| 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. |
| Outcome: | The proposed framework decomposes concept representations into interpretable features and tracks their activation dynamics over time and across sources. |
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Identifying Emerging Concepts in Large Corpora (2025.naacl-long)
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| 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. |
A Wind of Change: Detecting and Evaluating Lexical Semantic Change across Times and Domains (P19-1)
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| Challenge: | Existing models for diachronic and synchronic detection of lexical semantic divergences are superficial and lack of comparison. |
| Approach: | They propose to extend benchmark models on a common state-of-the-art evaluation task . they also demonstrate that the same evaluation task and modelling approaches can be utilised for synchronic detection of domain-specific sense divergences in the field of term extraction. |
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Neural Temporality Adaptation for Document Classification: Diachronic Word Embeddings and Domain Adaptation Models (P19-1)
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| Challenge: | Recent studies show that document classifiers can become more stable over time when trained in ways that account for temporal variations. |
| Approach: | They propose a method for embedding diachronic word embedds into document classification models . they propose 'time-driven neural classification model' that accounts for temporal variations . |
| Outcome: | The proposed model can be trained on six corpora and make it more robust over time. |
Inspecting the concept knowledge graph encoded by modern language models (2021.findings-acl)
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| Challenge: | Pre-trained language models are used to solve tasks such as summarization and information retrieval. |
| Approach: | They propose to use word embeddings, text generators, context encoders to extract underlying knowledge graphs of nine influential language models. |
| Outcome: | The proposed model is able to encode word embeddings, text generators, and context encoders, but suffers from several inaccuracies. |
Will This Idea Spread Beyond Academia? Understanding Knowledge Transfer of Scientific Concepts across Text Corpora (2020.findings-emnlp)
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| Challenge: | Existing research on knowledge transfer focuses on documents as unit of analysis and follow their transfer into practice for a specific scientific domain. |
| Approach: | They analyze scientific concepts from corpora and use them to predict knowledge transfer . they find that only a small proportion of these ideas will be used in inventions . |
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The DReaM Corpus: A Multilingual Annotated Corpus of Grammars for the World’s Languages (2020.lrec-1)
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| Challenge: | Until recently, language descriptions were available in paper form only, with indexes as the only search aid. |
| Approach: | They propose to digitize a multilingual corpus of language descriptions and annotate it with various meta, word, and text attributes to make searching and analysis easier and more useful. |
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A Multidimensional Framework for Evaluating Lexical Semantic Change with Social Science Applications (2024.acl-long)
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| Challenge: | Historical linguists have identified multiple forms of lexical semantic change. |
| Approach: | They propose a framework for integrating and evaluating lexical semantic changes in historical linguists and a unified computational methodology for evaluating them concurrently. |
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Analyzing Encoded Concepts in Transformer Language Models (2022.naacl-main)
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| Challenge: | a new framework to analyze how latent concepts are encoded in representations learned in pre-trained lan-guage models is proposed . conceptX uses clustering to discover the encoded concepts and align them with a large set of human-defined concepts. |
| Approach: | They propose a framework to analyze how latent concepts are encoded in representations learned within pre-trained lan-guage models. |
| Outcome: | The proposed framework explains encoded concepts by aligning with human-defined concepts. |
A Diachronic Corpus for Literary Style Analysis (L18-1)
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| Challenge: | Temporal style analysis is not widely taken into account, says aaron daelemans . he says it is important to consider the possibility of an author's style frequently changing over time . daelemens: synchronic style analysis requires accurate time-stamped data . |
| Approach: | They propose a resource for diachronic style analysis in particular the analysis of literary authors over time. |
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Measuring and Modeling Language Change (N19-5)
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| Challenge: | This tutorial will help researchers answer questions fundamental to the social sciences and humanities . |
| Approach: | This tutorial is designed to help researchers answer questions in the social sciences and humanities . it synthesizes recent computational techniques for handling and modeling temporal data . |
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