Words are Malleable: Computing Semantic Shifts in Political and Media Discourse

by   Hosein Azarbonyad, et al.

Recently, researchers started to pay attention to the detection of temporal shifts in the meaning of words. However, most (if not all) of these approaches restricted their efforts to uncovering change over time, thus neglecting other valuable dimensions such as social or political variability. We propose an approach for detecting semantic shifts between different viewpoints--broadly defined as a set of texts that share a specific metadata feature, which can be a time-period, but also a social entity such as a political party. For each viewpoint, we learn a semantic space in which each word is represented as a low dimensional neural embedded vector. The challenge is to compare the meaning of a word in one space to its meaning in another space and measure the size of the semantic shifts. We compare the effectiveness of a measure based on optimal transformations between the two spaces with a measure based on the similarity of the neighbors of the word in the respective spaces. Our experiments demonstrate that the combination of these two performs best. We show that the semantic shifts not only occur over time, but also along different viewpoints in a short period of time. For evaluation, we demonstrate how this approach captures meaningful semantic shifts and can help improve other tasks such as the contrastive viewpoint summarization and ideology detection (measured as classification accuracy) in political texts. We also show that the two laws of semantic change which were empirically shown to hold for temporal shifts also hold for shifts across viewpoints. These laws state that frequent words are less likely to shift meaning while words with many senses are more likely to do so.


page 1

page 2

page 3

page 4


Visualizing Linguistic Shift

Neural network based models are a very powerful tool for creating word e...

On the Linearity of Semantic Change: Investigating Meaning Variation via Dynamic Graph Models

We consider two graph models of semantic change. The first is a time-ser...

A Greek Parliament Proceedings Dataset for Computational Linguistics and Political Analysis

Large, diachronic datasets of political discourse are hard to come acros...

Slangvolution: A Causal Analysis of Semantic Change and Frequency Dynamics in Slang

Languages are continuously undergoing changes, and the mechanisms that u...

Cultural Shift or Linguistic Drift? Comparing Two Computational Measures of Semantic Change

Words shift in meaning for many reasons, including cultural factors like...

A Survey on Contextualised Semantic Shift Detection

Semantic Shift Detection (SSD) is the task of identifying, interpreting,...

Unsupervised detection of diachronic word sense evolution

Most words have several senses and connotations which evolve in time due...

Please sign up or login with your details

Forgot password? Click here to reset