Challenge: Using domain independent models, we date documents based only on neologism usage patterns . nasa models use only 200 input features, compared to state of the art models using 200K features.
Approach: They propose domain independent models to date documents based only on neologism usage patterns.
Outcome: The proposed models can generalize to various domains like News, Fiction, and Non-Fiction with competitive performance.

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Challenge: EMNLP 2017 is a workshop on enhancing natural language processing's ability to produce concise, fluent summaries.
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Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts (2025.emnlp-tutorials)

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Challenge: EMNLP 2025 tutorials will cover seven cutting-edge topics . the process of soliciting, reviewing and selecting tutorials was a collaborative effort .
Approach: EMNLP 2025 will feature tutorials on seven cutting-edge topics . the process of soliciting, reviewing and selecting tutorials was a collaborative effort .
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Challenge: Existing methods for tracing time-related semantic shifts with word embedding models lack the cohesion, common terminology and shared practices of more established areas of natural language processing.
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Challenge: EMNLP 2024 will feature tutorials on six exciting topics . the process of selecting tutorials was a collaborative effort .
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Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts (2023.emnlp-tutorial)

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Challenge: EMNLP 2023 tutorials session is organized to give conference attendees a comprehensive introduction by expert researchers to a variety of topics of importance drawn from our rapidly growing and changing research field.
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Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13) (D19-53)

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Challenge: TextGraphs is a workshop on graph-based methods for natural language processing . the workshop is being organized in conjunction with the 9th International Joint Conference on Natural Language Processing .
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Findings of the Association for Computational Linguistics: NAACL 2022 (2022.findings-naacl)

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Challenge: . - (EN)
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