@inproceedings{rosin-etal-2017-learning,
title = "Learning Word Relatedness over Time",
author = "Rosin, Guy D. and
Adar, Eytan and
Radinsky, Kira",
editor = "Palmer, Martha and
Hwa, Rebecca and
Riedel, Sebastian",
booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D17-1121",
doi = "10.18653/v1/D17-1121",
pages = "1168--1178",
abstract = "Search systems are often focused on providing relevant results for the {``}now{''}, assuming both corpora and user needs that focus on the present. However, many corpora today reflect significant longitudinal collections ranging from 20 years of the Web to hundreds of years of digitized newspapers and books. Understanding the temporal intent of the user and retrieving the most relevant historical content has become a significant challenge. Common search features, such as query expansion, leverage the relationship between terms but cannot function well across all times when relationships vary temporally. In this work, we introduce a temporal relationship model that is extracted from longitudinal data collections. The model supports the task of identifying, given two words, when they relate to each other. We present an algorithmic framework for this task and show its application for the task of query expansion, achieving high gain.",
}
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%0 Conference Proceedings
%T Learning Word Relatedness over Time
%A Rosin, Guy D.
%A Adar, Eytan
%A Radinsky, Kira
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F rosin-etal-2017-learning
%X Search systems are often focused on providing relevant results for the “now”, assuming both corpora and user needs that focus on the present. However, many corpora today reflect significant longitudinal collections ranging from 20 years of the Web to hundreds of years of digitized newspapers and books. Understanding the temporal intent of the user and retrieving the most relevant historical content has become a significant challenge. Common search features, such as query expansion, leverage the relationship between terms but cannot function well across all times when relationships vary temporally. In this work, we introduce a temporal relationship model that is extracted from longitudinal data collections. The model supports the task of identifying, given two words, when they relate to each other. We present an algorithmic framework for this task and show its application for the task of query expansion, achieving high gain.
%R 10.18653/v1/D17-1121
%U https://aclanthology.org/D17-1121
%U https://doi.org/10.18653/v1/D17-1121
%P 1168-1178
Markdown (Informal)
[Learning Word Relatedness over Time](https://aclanthology.org/D17-1121) (Rosin et al., EMNLP 2017)
ACL
- Guy D. Rosin, Eytan Adar, and Kira Radinsky. 2017. Learning Word Relatedness over Time. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1168–1178, Copenhagen, Denmark. Association for Computational Linguistics.