Detecting Future-related Contexts of Entity Mentions
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866909503548555264 |
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| author | Prashar, Puneet Shukla, Krishna Mohan Jatowt, Adam |
| author_facet | Prashar, Puneet Shukla, Krishna Mohan Jatowt, Adam |
| contents | The ability to automatically identify whether an entity is referenced in a future context can have multiple applications including decision making, planning and trend forecasting. This paper focuses on detecting implicit future references in entity-centric texts, addressing the growing need for automated temporal analysis in information processing. We first present a novel dataset of 19,540 sentences built around popular entities sourced from Wikipedia, which consists of future-related and non-future-related contexts in which those entities appear. As a second contribution, we evaluate the performance of several Language Models including also Large Language Models (LLMs) on the task of distinguishing future-oriented content in the absence of explicit temporal references. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_15332 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Detecting Future-related Contexts of Entity Mentions Prashar, Puneet Shukla, Krishna Mohan Jatowt, Adam Computation and Language Information Retrieval The ability to automatically identify whether an entity is referenced in a future context can have multiple applications including decision making, planning and trend forecasting. This paper focuses on detecting implicit future references in entity-centric texts, addressing the growing need for automated temporal analysis in information processing. We first present a novel dataset of 19,540 sentences built around popular entities sourced from Wikipedia, which consists of future-related and non-future-related contexts in which those entities appear. As a second contribution, we evaluate the performance of several Language Models including also Large Language Models (LLMs) on the task of distinguishing future-oriented content in the absence of explicit temporal references. |
| title | Detecting Future-related Contexts of Entity Mentions |
| topic | Computation and Language Information Retrieval |
| url | https://arxiv.org/abs/2502.15332 |