Understanding semantic change of words over centuries

Abstract

In this paper, we propose to model and analyze changes that occur to an entity in terms of changes in the words that co-occur with the entity over time. We propose to do an in-depth analysis of how this co-occurrence changes over time, how the change influences the state (semantic, role) of the entity, and how the change may correspond to events occurring in the same period of time. We propose to identify clusters of topics surrounding the entity over time using Topics-Over-Time (TOT) and k-means clustering. We conduct this analysis on Google Books Ngram dataset. We show how clustering words that co-occur with an entity of interest in 5-grams can shed some lights to the nature of change that occurs to the entity and identify the period for which the change occurs. We find that the period identified by our model precisely coincides with events in the same period that correspond to the change that occurs.

Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science

“…We note that for cell, the identified period of change ) coincides with the introduction-and subsequent adoption-of the cell phone by the general public. 3 Likewise, the period of change for gay agrees with the gay movement which began around the 1970s (Wijaya and Yeniterzi, 2011).…”

Finding IndividualWord Sense Changes and their Delay in Appearance

“…Topic-based models (where topics are interpreted as senses) have been used to detect novel senses in one collection compared to another by identifying new topics in the later corpus ((Lau et al, 2012;Cook et al, 2014)), or to cluster top- ics over time (Wijaya and Yeniterzi, 2011). A dynamic topic model that builds topics with respect to information from the previous time point is proposed by Frermann and Lapata (2016) and again sense novelty is evaluated.…”

Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)

“…This includes mainly (i), semantic similarity models assuming one sense for each word and then measuring its spatial displacement by a similarity metric (such as cosine) in a semantic vector space (Gulordava and Baroni, 2011;Xu and Kemp, 2015;Eger and Mehler, 2016;Hellrich and Hahn, 2016;Hamilton et al, 2016a,b) and (ii), word sense induction models (WSI) inferring for each word a probability distribution over different word senses (or topics) in turn modeled as a distribution over words (Wang and Mccallum, 2006;Bamman and Crane, 2011;Wijaya and Yeniterzi, 2011;Lau et al, 2012;Mihalcea and Nastase, 2012;Frermann and Lapata, 2016).…”