| Challenge: | a demonstration system visualizes news trend of key roles based on natural language processing techniques . semantic role labelling and word embeddings can help users understand news topics . |
| Approach: | They propose a system that visualizes the news trend of key roles based on natural language processing techniques. |
| Outcome: | The proposed system analyzes the news trend of key roles using semantic role labelling . it also analyzes how similarities between key roles and news topics change over time . |
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| Challenge: | Using hierarchical Dirichlet processes, we characterize news articles associated with key events from news streams. |
| Approach: | They propose a generic framework for news stream clustering that analyzes the temporal trend of news articles to automatically extract the underlying key news events that draw significant media attention. |
| Outcome: | The proposed framework produces more coherent clusters based on event summaries . the proposed framework is a first step in a new field of news analysis . |
Diachronic word embeddings and semantic shifts: a survey (C18-1)
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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. |
| Approach: | They propose several axes along which these methods can be compared and propose a framework for comparison. |
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Entity Framing and Role Portrayal in the News (2025.findings-acl)
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Tarek Mahmoud, Zhuohan Xie, Dimitar Iliyanov Dimitrov, Nikolaos Nikolaidis, Purificação Silvano, Roman Yangarber, Shivam Sharma, Elisa Sartori, Nicolas Stefanovitch, Giovanni Da San Martino, Jakub Piskorski, Preslav Nakov
| Challenge: | a dataset of news articles containing 22 fine-grained characters is annotated for entity framing and role portrayal . the dataset includes 1,378 recent news articles in five languages focusing on the Ukraine-Russia War and climate change . |
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Through the Lens of History: Methods for Analyzing Temporal Variation in Content and Framing of State-run Chinese Newspapers (2025.naacl-long)
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| Challenge: | State-run newspapers are believed to strategically select and frame news articles to align with the shifting political tides of the country. |
| Approach: | They analyze more than 50 years of articles from the People's Daily and Reference News to quantify differences in content and framing 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 . |
| Outcome: | The tutorial will synthesize recent techniques for handling and modeling temporal data, such as dynamic word embeddings, and identify useful tools for social scientists and digital humanities scholars. |
Sudden Semantic Shifts in Swedish NATO discourse (2023.acl-srw)
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| Challenge: | Using word embeddings, we study sudden semantic shifts that occur when a sudden event radically changes public opinion on a topic. |
| Approach: | They use word embeddings to study how Twitter associations evolve . they find domain knowledge and data selection are of prime importance when using word embeds to understand semantic shifts. |
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Syntax-driven Approach for Semantic Role Labeling (2022.lrec-1)
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| Challenge: | Existing studies focus on auto-generated syntactic knowledge to enhance semantic role labeling . experimental results show that map memories can enhance SRL . |
| Approach: | They propose to map memories to enhance semantic role labeling by encoding auto-generated syntactic knowledge from off-the-shelf toolkits. |
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Room to Glo: A Systematic Comparison of Semantic Change Detection Approaches with Word Embeddings (D19-1)
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| Challenge: | Word embeddings are increasingly used for automatic detection of semantic change, but a robust evaluation and systematic comparison of the choices involved has been lacking. |
| Approach: | They propose a new evaluation framework for semantic change detection using whole time series and a Twitter dataset spanning 5.5 years. |
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No Word Embedding Model Is Perfect: Evaluating the Representation Accuracy for Social Bias in the Media (2022.findings-emnlp)
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| Challenge: | Recent work has relied on word embedding bias measures, such as WEAT, but these methods can be inaccurate due to several representation issues, such low-resource settings and token frequency differences. |
| Approach: | They propose to use WEAT to quantify social bias in US online news articles and embed embedding algorithms to account for the aforementioned issues. |
| Outcome: | The proposed algorithms do not match the literature, but they reduce the gap. |
Exploring Word Usage Change with Continuously Evolving Embeddings (2021.acl-demo)
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| Challenge: | a new method to track word usage changes is proposed for text datasets that are collected over a longer period of time. |
| Approach: | They propose a way to track word usage changes via continuously evolving embeddings . they demonstrate an interactive web app that can explore semantic shifts with interactive plots a text . |
| Outcome: | The proposed method can be used to analyze word usage changes with interactive plots. |