Volume 8 Issue 1 (2010)
Note: Linguistic Discovery uses Unicode characters
to represent phonetic symbols. Please see Optimizing Display
for requirements to accurately reproduce this page.
The Dynamic Potential of Probabilistic Semantic Maps
Comment on ‘Similarity Semantics and Building Probabilistic
Semantic Maps from Parallel Texts’ by Bernhard Wälchli
Probabilistic semantic maps (also known as
‘statistical’ or ‘second-generation’ semantic maps)
represent semantic space as a “probability space which can be modeled by
statistical methods which need concrete databases as input” (Wälchli
2010: section 5). They are neither robust nor comprehensive, but
i.e. they assume different shapes depending on the data-set considered.
According to Wälchli (2010: section 1), this dynamicity is consistent with
the “dynamicity of psychological similarity based on perception of
situations”, and with the idea that categories are formed in the
speaker’s mind on the basis of similarity among exemplars.
Critics of probabilistic semantic maps (see e.g. van der Auwera 2008)
often point to a major disadvantage of such maps, namely, that it is impossible
to incorporate diachronic information into them. Knowledge of the relevant
diachronic processes which lead to the polysemy patterns of grammatical elements
at the synchronic level can be added to traditional semantic maps, making them
more enlightening and more informative. By contrast, there is no principled way
to do so on a probabilistic semantic map. In other words, traditional semantic
maps, based on the “usual
va et vient between armchair hypothesis
building and empirical validation” (van der Auwera 2008:44) are better
because they are inherently diachronic in nature and are also truly semantic,
i.e. based on “strong semantic links that are historical” (van der
Auwera 2008:43, adapted) rather than on weak links that are not. Unlike
probabilistic semantic maps, traditional maps based on large samples of
languages are also predictive to some extent, i.e. they are able to
represent—in a more or less explicit way—which diachronic changes
are more likely and which are disfavored or even impossible.
Such a criticism targeted at probabilistic semantic maps is possibly
well-founded. However, the way in which semantic space is represented in this
kind of maps can turn out to be useful also when addressing diachronic issues.
The purpose of this short commentary is to elaborate on some characteristics of
probabilistic semantic maps which are not considered by Wälchli in all
and to suggest
that probabilistic maps have the inherent potential to be used as tools for
interpreting diachronic tendencies, although the ways to exploit this potential
might not be obvious at first sight.
This potential is directly connected to the dynamic character of
probabilistic semantic maps. As Wälchli correctly observes, “semantic
space is not universal, not even language-specific, but different for every
changing over time” (Wälchli 2010: section 2,
my emphasis). Probabilistic semantic maps are a kind of average over individual
semantic spaces in a given population, and their shape changes with every new
exemplar presented. They are therefore highly unstable configurations and
comprise “constellations of exemplars with varying degrees of
activation” (Wälchli 2010: section 4). The meaning of a category in
such an approach is not an abstract concept but simply amounts to the range of
individual meanings of exemplars of that category.
Now, many attested cases of semantic change involve reorganization of
the meaning of a category triggered by the extension of a marker encoding that
category to new situations, or by the increase in number of peripheral members
of that category, which may eventually affect the speaker’s perception of
the category itself. Other changes may involve progressive loss of peripheral
exemplars of a category and mutual reinforcement of their nuclear or
prototypical members, resulting in a contraction of the range of meanings of the
category (see Sansò to appear for such a case in the history of Italian).
Dealing with such instances of semantic change often requires us to adopt a
similarity-based view of meaning which is also the (implicit) theoretical
foundation of probabilistic semantic maps according to Wälchli. Such maps
may therefore become suitable tools for modelling the interaction between
different exemplars within the same semantic space and its effects over
Adapting probabilistic semantic maps to this task is not at all trivial,
and I do not have many concrete suggestions in this respect. A possible road-map
might look as follows: given two or more populations of exemplars from different
diachronic stages of the same language, it is possible to draw two or more
probabilistic maps representing their semantic space. These maps will then
exhibit differences that can be interpreted as the visual correlate of different
stages in the processes of interaction among exemplars within that semantic
space (such as, for instance, the emergence of prototypes, or the extension of a
grammatical marker to new contexts over time). This road-map might be taken to
be nothing but wishful thinking, given that diachronic datasets are very
difficult to obtain for many languages. The construction of comparable datasets
for different diachronic stages, however, is not an impossible task for a number
of better-investigated languages: the insights gained from the comparison of
maps for different stages of these languages can provide a robust statistical
basis for a general theory of semantic change, and can subsequently be used as a
key for interpreting similar changes in other languages, based on the
uniformitarian principle that what is known can be used as a key for
interpreting what is unknown. Furthermore, possible suggestions on how this goal
can be achieved may come from relatively “distant” fields of
linguistic theory, in which the problem of interaction between different
entities within the same space has long been debated. One of these sources of
inspiration (Croft 2007) could be the theory of the best exemplar, as applied,
for instance, to phonology by Pierrehumbert (2001). In this theory, each
phonological category “is represented in memory by a large cloud of
remembered tokens” (Pierrehumbert 2001:140) of that category. As a result,
the prototypical instances of a phoneme are represented by numerous tokens,
while infrequent, less prototypical instances are represented by less numerous
tokens. The difference in token count is the main ingredient in explaining the
various frequency effects attested in historical phonological processes and the
dynamics of interaction between phonemes over time.
Croft, W. 2007. Exemplar semantics. Unpublished manuscript,
University of New Mexico.
Pierrehumbert, J. 2001. Exemplar dynamics: Word frequency, lenition
and contrast. Frequency and the emergence of linguistic structure, ed. by J.
Bybee, P.J. Hopper, 137-157. Amsterdam: Benjamins.
Sansò, A. to appear. Grammaticalization paths or prototype
effects? A history of the agentive reflexive passive in Italian. Language
Sciences special issue ‘Prototypes and grammaticalization –
Grammaticalization as prototype?’, ed. by T. Mortelmans.
van der Auwera, J. 2008. In defense of classical semantic maps.
Theoretical Linguistics 34/1. 39-46. doi:10.1515/thli.2008.002
Wälchli, B. 2010. Similarity semantics and building
probabilistic semantic maps from parallel texts. Linguistic Discovery, this
Author’s contact information:
Scienze della Mediazione Interlinguistica e Interculturale Facoltà di Giurisprudenza
Università dell’Insubria Via S. Abbondio 9 I-22100
This is by no means to
be intended as a flaw in Wälchli’s argumentation, which is primarily
concerned with the theoretical bases of probabilistic semantic