Abstract
Word meanings change over time and an automated procedure for extracting this information from text would be useful for historical exploratory studies, information retrieval or question answering. We present a dynamic Bayesian model of diachronic meaning change, which infers temporal word representations as a set of senses and their prevalence. Unlike previous work, we explicitly model language change as a smooth, gradual process. We experimentally show that this modeling decision is beneficial: our model performs competitively on meaning change detection tasks whilst inducing discernible word senses and their development over time. Application of our model to the SemEval-2015 temporal classification benchmark datasets further reveals that it performs on par with highly optimized task-specific systems.
Highlights
Language is a dynamic system, constantly evolving and adapting to the needs of its users and their environment (Aitchison, 2001)
Since the representations learnt by SCAN are influenced by neighboring representations, they overfit specific time intervals less which leads to better predictive performance
We report the correlation coefficient obtained in Gulordava and Baroni (2011) but emphasize that the scores are not directly comparable due to differences in training data: Gulordava and Baroni (2011) use the Google bigrams corpus
Summary
Language is a dynamic system, constantly evolving and adapting to the needs of its users and their environment (Aitchison, 2001). Today it mostly refers to objects or people perceived as attractive, pretty or sweet Another example is the word mouse which initially was only used in the rodent sense. We define this prior as an intrinsic Gaussian Markov Random Field (iGMRF; Rue and Held 2005), which allows us to model the change of adjacent parameters as drawn from a normal distribution, e.g.:. The precision parameter κ controls the extent of variation: how tightly coupled are the neighboring parameters? Or, φ1 φt−1 φt φT
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Transactions of the Association for Computational Linguistics
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
