Blog | Anna Di Natale: “Affective meaning in language”


Jun 4, 2021

Anna Di Natale by (c) Verena Ahne

Anna Di Natale:

 

“Does language encode affective meaning?”

 

Our answer is yes!

 

In the paper “Colexification networks encode affective meaning,” which I wrote together with Max Pellert and my supervisor David Garcia and that just appeared in the journal Affective Science, we analyzed a word network, the one determined by colexification, and find that words cluster on the basis of their valence, arousal and dominance.

 

This discovery proves that colexification links carry affective meaning and could be deployed to expand pre-existing affective lexica.

Network of matching nodes in OmegaWiki colored on the basis of their valence: The reference affective network is NRC VAD and the network is the largest connected component of the OmegaWiki network (42% of the matching nodes).

Colexification is a linguistic idea and happens when two different concepts are conveyed with the same word in one language, as for example the concepts for “breath” and “air.” These instances have been collected in a project led by Johann-Mattis List and subsequently expanded.

 

We also deployed online dictionaries to automatically build two additional networks on the basis of an approximation of the concept of colexification.

Construction of a colexification network: Nodes represent concepts (words in the case of identical translation) and links represent the existence of a colexification or identical translation pattern between the nodes it connects. Links are weighted by the number of languages and the number of families of languages that present the same connection. We usually apply a threshold on the weights of the links we consider.

We considered two affective lexica, WKB (Warriner et al., 2013) and NRC VAD (Mohammad, 2018) and interpolate the affective values of words in the colexification network on the basis of the ground truth ratings of their neighbors. We did so by computing the mean of the ratings of the neighbors of each word, as for example with the word “air.”

We compared the performance of our algorithm on a 75/25 split cross validation with machine learning techniques by Mandera et al. and find that the two methods have comparable results, and that the colexification based one achieves higher coverage.

Valence ratings in the NRC VAD lexicon versus estimated ratings in the OmegaWiki network: The language weights are considered for the computation of the estimated ratings as weighted mean. Only nodes with at least 5 neighbors are plotted. The line shows a linear regression fit (ρ = 0.839, c.i. = [.82,.856], p < 10− 5). Labels indicate the most extreme outliers.

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