China

Methods for lexical semantic change detection quantify changes in the meaning of words over time. Prior research produced many methods that excel when evaluated on established benchmarks that consist of annotated sets of pre-selected \emph{target} words; the targets are chosen ahead of time due to the prohibitively large cost of manually annotating all words. However, performance measured on small curated sets of words cannot reveal how well these methods would perform when applied to \emph{discovering} semantic changes among the \emph{full} corpus vocabulary, which is the actual end goal for many applications. In this paper, we implement a top-k set-up to evaluate semantic change discovery (SCDisc) despite lacking complete annotations (although a contribution of our efforts is a 50% extension of the annotations in the commonly used LiverpoolFC benchmark). We use our evaluation setup to investigate a battery of semantic change detection methods that utilize three different kinds of contextualized representations, as well as recent techniques specifically designed to reduce false-discovery rates, such as statistical significance tests and metric scaling. We find that despite great performance on pre-existing benchmarks, \emph{all} methods of semantic change detection struggle at ranking known changes higher compared to other words in the vocabulary when presented with a \emph{natural} distribution of instances. Finally, we verify that the majority of words with high detected change scores do not in fact experience meaning changes. In fact, for most of the methods less than a third of highest ranked changes were annotated to have changed in meaning. Given the large performance discrepancies between semantic-change scoring and discovery ``in the wild&#39;&#39;, we recommend that researchers direct more attention to semantic change discovery and include it in their suite of evaluations when proposing new methods.

EMNLP 2025

Current Semantic-change Quantification Methods Struggle with Semantic Change Discovery in the Wild

lexical semantic change

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ACL-IJCNLP 2021

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

Methods for lexical semantic change detection quantify changes in the meaning of words over time. Prior research produced many methods that excel when evaluated on established benchmarks that consist of annotated sets of pre-selected \emph{target} words; the targets are chosen ahead of time due to the prohibitively large cost of manually annotating all words. However, performance measured on small curated sets of words cannot reveal how well these methods would perform when applied to \emph{discovering} semantic changes among the \emph{full} corpus vocabulary, which is the actual end goal for many applications. In this paper, we implement a top-k set-up to evaluate semantic change discovery (SCDisc) despite lacking complete annotations (although a contribution of our efforts is a 50% extension of the annotations in the commonly used LiverpoolFC benchmark). We use our evaluation setup to investigate a battery of semantic change detection methods that utilize three different kinds of contextualized representations, as well as recent techniques specifically designed to reduce false-discovery rates, such as statistical significance tests and metric scaling. We find that despite great performance on pre-existing benchmarks, \emph{all} methods of semantic change detection struggle at ranking known changes higher compared to other words in the vocabulary when presented with a \emph{natural} distribution of instances. Finally, we verify that the majority of words with high detected change scores do not in fact experience meaning changes. In fact, for most of the methods less than a third of highest ranked changes were annotated to have changed in meaning. Given the large performance discrepancies between semantic-change scoring and discovery ``in the wild'', we recommend that researchers direct more attention to semantic change discovery and include it in their suite of evaluations when proposing new methods.

poster

## Welcome!
"I am excited to welcome you to this year’s edition of the Conference on Empirical Methods in Natural Language Processing! Importantly, it marks the 30th edition of EMNLP. With over 8,000 submissions, more than 3,000 accepted papers, and thousands of attendees, we have come a long way from that first
workshop, which had 14 accepted papers. As the field looks ahead, Suzhou is the fitting location for celebrating this milestone: rooted in a long literary tradition, yet modern and forward-looking, and home to a large share of the NLP community."<br>

*Message from the General Chair, Dirk Hovy*





<br>

Celebrate 30 Years of EMNLP! 
EMNLP 2025 will be held in Suzhou, China from November 5th to November 9th, 2025.

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The Frame Representation Hypothesis: Multi-Token LLM Interpretability and Concept-Guided Text Generation

Detecting content generated by large language models (LLMs) is crucial for preventing misuse and building trustworthy AI systems. Although existing detection methods perform well, their robustness in out-of-distribution (OOD) scenarios is still lacking. In this paper, we hypothesize that, compared to features used by existing detection methods, the internal representations of LLMs contain more comprehensive and raw features that can more effectively capture and distinguish the statistical pattern differences between LLM-generated texts (LGT) and human-written texts (HWT). We validated this hypothesis across different LLMs and observed significant differences in neural activation patterns when processing these two types of texts. Based on this, we propose RepreGuard, an efficient statistics-based detection method. Specifically, we first employ a surrogate model to collect representation of LGT and HWT, and extract the distinct activation feature that can better identify LGT. We can classify the text by calculating the projection score of the text representations along this feature direction and comparing with a precomputed threshold. Experimental results show that RepreGuard outperforms all baselines with average 94.92% AUROC on both in-distribution (ID) and OOD scenarios, while also demonstrating robust resilience to various text sizes and mainstream attacks.

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Lexical Semantic Change Discovery