# Patterns of Closeness and Abstractness in Colexifications:
The Case of Indigneous Languages in the Americas

Yiyi Chen
Department of Computer Science
Aalborg University
Copenhagen, Denmark
<EMAIL_ADDRESS>
&Johannes Bjerva
Department of Computer Science
Aalborg University
Copenhagen, Denmark
<EMAIL_ADDRESS>This work is supported by the Carlsberg Foundation under a
Semper Ardens: Accelerate career grant held by JB, entitled “Multilingual
Modelling for Resource-Poor Languages”, grant code CF21- 0454. This work is
accepted to SIGMORPHON 2023.

Colexification refers to linguistic phenomena where multiple concepts
(meanings) are expressed by the same lexical form, such as polysemy or
homophony. Semantic typology studies this type of cross-lingual semantic
categorization (Evans et al., 2010). The term “colexification" was first
formalized in semantic typology by François (2008) to create semantic maps.
Colexifications have been found to be pervasive across languages and cultures.
The investigation of cross-lingual colexifications has provided insights
across different fields, such as psycholinguistics (Jackson et al., 2019),
cognitive science (Gibson et al., 2019) and linguistic typology (Schapper and
Koptjevskaja-Tamm, 2022), but remain relatively unexplored in NLP (see, e.g.,
Harvill et al. (2022), Chen et al. (2023)). The Database of Cross-Linguistic
Colexifications (CLICS3) (Rzymski et al., 2020) was created to include 4,228
colexification patterns across 3,156 languages, to facilitate research in
colexifications.

Certain colexifications are more prevalent than others, for example, moon and
month colexify in more than 300 languages while cat and dog only colexify
across 4 languages in CLICS 3111https://clics.clld.org/graphs/subgraph_1313.
Previous works have attempted to shed light on the patterns of
colexifications, e.g., why certain concepts or meanings colexify more
frequently than others, and what patterns colexifications reveal. Studies
suggest that related concepts are more likely to colexify (Karjus et al.,
2021; Xu et al., 2020). Xu et al. (2020) argues that concepts requiring less
cognitive effort to relate are more likely to colexify across languages, by
investigating 246 languages and 1310 meanings using the Intercontinental
Dictionary Series (Borin et al., 2013). The Goldilocks principle suggests that
both relatedness and accurate information transfer play roles in
colexifications using CLICS 3 (Brochhagen and Boleda, 2022). For example,
strongly related concepts left and right are expected to be less likely to
colexify because they are hard to distinguish in context.

Figure 1: Colexification Subgraph sun with concreteness distance on the edge
of each pair of colexified concepts. Each concept is annotated with
concreteness.

The problem of concreteness/abstractness of concepts is interdisciplinary,
studied from a cognitive standpoint in linguistics, psychology,
psycholinguistics, neurophysiology, etc (Solovyev, 2021). Concrete concepts
are those that are perceived by the senses, such as cat and mountain, while
abstract concepts are not perceived by the senses, such as relationship and
understanding. Brysbaert et al. (2014) curates concreteness ratings for 37,058
English words and 2,896 two-word expressions from over 4,000 participants,
which has provided insights across various linguistic disciplines. The
concreteness ratings scales from 1 (abstract) to 5 (concrete).

Table 1: Correlation between #Colexifications and the Concreteness Distance between the Colexified Concepts (a) across 1989 languages in CLICS and (b) 170 Indigenous American Languages, respectively.  | a |  | b |
---|---|---|---|---
| Correlation Coefficient | p-value | Correlation Coefficient | p-value
#Colex. | -0.3843 | 4.2965e-26* | -0.3585 | 2.0013e-19*
Colex. Patterns | -0.3500 | 1.2436e-21* | -0.3829 | 3.8299e-22*
#Languages | -0.3537 | 4.3349e-22* | -0.3603 | 1.2804e-19*
Figure 2: Histogram of Number of Colexifications against Concreteness Distance
between the Colexified Concepts across 1989 languages in terms of all
colexifications (left), unique collexification patterns (middle), and number
of languages where colexification patterns occur (right), respectively.

Previous studies show that abstract concepts are often understood by reference
to more concrete concepts (Lakoff and Johnson, 2008), and words that first
arise with concrete meanings often later gain an abstract one (Xu et al.,
2017). Xu et al. (2020) leans on these findings to show that concepts more
dissimilar in concreteness and affective valence are more likely to colexify.
However, using CLICS3, Di Natale et al. (2021) find that colexification
patterns capture similar affective meanings, i.e., the concepts that are
closer in affectiveness are more likely to colexify. Moreover, studies show
that abstract words are often combined with abstract ones, and vice versa for
concrete words (Frassinelli et al., 2017; Naumann et al., 2018).

We have a markedly differing hypothesis from previous work. Unlike Xu et al.
(2020), we hypothesize that concepts that are closer in
concreteness/abstractness are more likely to colexify. We use CLICS3 to map
concepts with the concreteness list from (Brysbaert et al., 2014), comparing
all languages available, with indigenous languages in the Americas. We then
construct graphs of colexifications, with nodes being the concepts and
annotated with concreteness, and edges weighted by languages. Fig. 1 shows an
example of part of the subgraph sun, and the edge weights are proportional to
the number of language where the respective concepts colexify. The
concreteness distance for each colexification is calculated simply by
extracting the absolute distance from one concept to another, since the
colexification graph is undirected. As shown in Fig. 1, concreteness distances
of concepts are annotated on the edges, except for the edge connected to the
concept day (24 hours) with unknown concreteness. By our hypothesis, the
colexification (year, sun) with the most distant concretenesses compared to
other colexifications in the graph, i.e., (moon, sun), (clock, sun) and (day
(24 hours), sun), occurs less frequently across languages. Fig. 2 shows the
histograms of the count of a) all the colexifications, b) unique
colexification patterns, and c) the languages where the colexifications occur
against the concreteness distances, across all 1989 languages in CLICS3.

To test our hypothesis, we calculate the Pearson correlation calculated for
the X-axis and Y-axis presented in Fig. 2. As shown in Table 1 there is a
statistically significant negative correlation between colexifications and the
concreteness distances of the colexified concepts. This verifies our
hypothesis, showing that it is indeed more likely for a pair of concepts to
colexify when they have similar levels of concreteness.

Our preliminary findings challenge previous theories and findings on the
correlation between colexification and metaphoricity, corroborated by data
with a broader range of concepts and languages. We will conduct analyses to
evaluate a stronger hypothesis, i.e., that colexifications encode closeness in
concreteness, by leveraging language weights between concepts. This line of
research will provide insights into the study of human conceptualization, and
more specifically assist in bootstrapping concreteness for large-scale lexical
databases to facilitate further interdisciplinary research, such as
psycholinguistics and multilingual NLP.

## Limitations

A limitation of this study is the fact that the concreteness ratings of
Brysbaert et al. (2014) are curated solely from self-identified U.S.
residents. As such, there is a risk of an anglocentric bias in that dataset.
However, as our findings are consistent both across English, languages spoken
in the global West, but also generalise to, e.g., indigenous languages in the
Americas, we argue that the findings of this study are robust.

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