| Challenge: | Named Entity Disambiguation (NED) systems perform well on news articles but quality drops when inputs span long time periods. |
| Approach: | They propose a time-aware method that resolves ambiguities even when mention contexts give only few cues. |
| Outcome: | The proposed method improves on a newly created diachronic corpus. |
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Temporally-Informed Analysis of Named Entity Recognition (2020.acl-main)
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| Challenge: | Existing methods to evaluate text data are rarely reported by taking the timestamp of the document into account. |
| Approach: | They propose methods that make better use of temporally-diverse training data with a focus on named entity recognition. |
| Outcome: | The proposed models make better use of temporally-diverse training data, with a focus on named entity recognition. |
Unsupervised Named Entity Disambiguation for Low Resource Domains (2024.emnlp-main)
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| Challenge: | Existing approaches to Named Entity Disambiguation (NED) are inefficient for domain specific tasks such as searching, question answering and information extraction. |
| Approach: | They propose a unsupervised approach leveraging the concept of Group Steiner Trees which can identify the most relevant candidate for entity disambiguation using contextual similarities across candidate entities for all the mentions present in a document. |
| Outcome: | The proposed approach outperforms the state-of-the-art methods by more than 40% in terms of Precision@1 and Hit@5 across various domain-specific datasets. |
When Time Makes Sense: A Historically-Aware Approach to Targeted Sense Disambiguation (2021.findings-acl)
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Kaspar Beelen, Federico Nanni, Mariona Coll Ardanuy, Kasra Hosseini, Giorgia Tolfo, Barbara McGillivray
| Challenge: | a new paper examines whether making NLP models sensitive to time improves their performance . timesensitive Sense Disambiguation is a variation on Word Sense disambiguation . authors present a task to determine whether a token in a text is related to a specific sense . |
| Approach: | They propose a task to determine whether a token in a text is related to a specific sense of a lemma. |
| Outcome: | The proposed model improves when time sensitive, rather than historically-aware, models . the proposed model is a variation on Word Sense Disambiguation (WSD) the proposed method is of more practical relevance to digital history and cultural analysis . |
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. |
Improving Few-Shot Domain Transfer for Named Entity Disambiguation with Pattern Exploitation (2022.findings-emnlp)
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| Challenge: | Named entity disambiguation is a critical subtask of entity linking . a model can be trained on a domain, but it needs to be adapted to the domain . |
| Approach: | They propose to reformulate named entity disambiguation as a masked language modeling problem. |
| Outcome: | The proposed model improves on a mental health news dataset without sacrifices in accuracy. |
Temporal Referential Consistency: Do LLMs Favor Sequences Over Absolute Time References? (2025.emnlp-main)
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| Challenge: | Existing efforts to ensure temporal consistency in large language models are lacking in time-sensitive fields . temporal reasoning is essential for time- sensitive fields such as finance and healthcare . a new benchmark aims to improve temporal referent consistency of LLMs . |
| Approach: | They propose a temporal referential consistency benchmark with a resource TEMP-ReCon to assess LLMs across temporal references. |
| Outcome: | The proposed model improves LLMs' temporal consistency by comparing them to baseline models. |
A Fast and Sound Tagging Method for Discontinuous Named-Entity Recognition (2024.emnlp-main)
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| Challenge: | Named-entity recognition (NER) is a fundamental natural language processing task . a tagging scheme for discontinuous named entities is proposed . |
| Approach: | They propose a tagging scheme for discontinuous named entity recognition based on an explicit description of the inner structure of discontinuous mentions. |
| Outcome: | The proposed method is comparable to state-of-the-art models on three English datasets in the biomedical domain. |
A Short Survey on Sense-Annotated Corpora (2020.lrec-1)
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| Challenge: | Word Sense Disambiguation (WSD) is a key task in Natural Language Understanding. |
| Approach: | They propose to use sense-annotated corpora for supervised Word Sense Disambiguation. |
| Outcome: | The proposed methods have been compared with knowledge-based approaches and have shown to be more efficient when they are available. |
Sense of the Day: Short Timeframe Temporal-Aware Word Sense Disambiguation (2024.lrec-main)
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| Challenge: | Existing models that consider the predominant sense of a lemma can be tailored to a specific timeframe and based on the timeframe of the text. |
| Approach: | They use Twitter to explore whether different senses are favoured within specific timeframes and how they are used to create short timeframe temporal-aware word sense disambiguation models. |
| Outcome: | The proposed model outperforms temporal agnostic models and author-aware models. |
Time-Out: Temporal Referencing for Robust Modeling of Lexical Semantic Change (P19-1)
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| Challenge: | State-of-the-art lexical semantic change detection models suffer from noise stemming from vector space alignment. |
| Approach: | They propose a method to simulate lexical semantic change and control for possible biases by avoiding alignment. |
| Outcome: | The proposed method outperforms state-of-the-art models on a synthetic task and a manual testset. |