@inproceedings{roland-king-2025-exploring,
title = "Exploring Language in Different Daily Time Segments Through Text Prediction and Language Modeling",
author = "Roland, Kennedy and
King, Milton",
editor = "Arachchige, Isuri Nanomi and
Frontini, Francesca and
Mitkov, Ruslan and
Rayson, Paul",
booktitle = "Proceedings of the First on Natural Language Processing and Language Models for Digital Humanities",
month = sep,
year = "2025",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd., Shoumen, Bulgaria",
url = "https://aclanthology.org/2025.lm4dh-1.11/",
pages = "120--125",
abstract = "Temporal-aware language models have proved to be effective over longer time periods as language and its use changes, but little research has looked at how language use can change at different times of the day. We hypothesize that a person{'}s usage of language varies at different times of day. We explore this concept by evaluating if models for language modeling and next word prediction improve their performance when considering the time of day. Specifically, we explore personalized temporal-aware models for next-word prediction and language modeling and compare them against baseline models, including non-temporal-aware personalized models. Specifically, our proposed model considers which of the 8, 3-hr daily time segments that a text snippet was written during for a given author. We found that our temporal-aware models tend to outperform temporal-agnostic models with respect to accuracy and perplexity."
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<abstract>Temporal-aware language models have proved to be effective over longer time periods as language and its use changes, but little research has looked at how language use can change at different times of the day. We hypothesize that a person’s usage of language varies at different times of day. We explore this concept by evaluating if models for language modeling and next word prediction improve their performance when considering the time of day. Specifically, we explore personalized temporal-aware models for next-word prediction and language modeling and compare them against baseline models, including non-temporal-aware personalized models. Specifically, our proposed model considers which of the 8, 3-hr daily time segments that a text snippet was written during for a given author. We found that our temporal-aware models tend to outperform temporal-agnostic models with respect to accuracy and perplexity.</abstract>
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%0 Conference Proceedings
%T Exploring Language in Different Daily Time Segments Through Text Prediction and Language Modeling
%A Roland, Kennedy
%A King, Milton
%Y Arachchige, Isuri Nanomi
%Y Frontini, Francesca
%Y Mitkov, Ruslan
%Y Rayson, Paul
%S Proceedings of the First on Natural Language Processing and Language Models for Digital Humanities
%D 2025
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F roland-king-2025-exploring
%X Temporal-aware language models have proved to be effective over longer time periods as language and its use changes, but little research has looked at how language use can change at different times of the day. We hypothesize that a person’s usage of language varies at different times of day. We explore this concept by evaluating if models for language modeling and next word prediction improve their performance when considering the time of day. Specifically, we explore personalized temporal-aware models for next-word prediction and language modeling and compare them against baseline models, including non-temporal-aware personalized models. Specifically, our proposed model considers which of the 8, 3-hr daily time segments that a text snippet was written during for a given author. We found that our temporal-aware models tend to outperform temporal-agnostic models with respect to accuracy and perplexity.
%U https://aclanthology.org/2025.lm4dh-1.11/
%P 120-125
Markdown (Informal)
[Exploring Language in Different Daily Time Segments Through Text Prediction and Language Modeling](https://aclanthology.org/2025.lm4dh-1.11/) (Roland & King, LM4DH 2025)
ACL