A Grounded Typology of Word Classes (2025)

Abstract: In this work, we propose a grounded approach to meaning in language typology. Using images captioned across languages, we can treat the images as an empirical language agnostic representation of meaning, allowing the quantification of language function and semantics. Using principles from information theory, we define “groundedness”, an empirical measure of contextual semantic contentfulness which can be computed using multilingual (vision-and-)language models. As an initial application, we apply this measure to the typology of word classes. We find our measure captures the contentfulness asymmetry between functional (grammatical) and lexical (content) classes across languages, but contradicts the view that functional classes do not convey content. We release a dataset of groundedness scores for 30 languages. Our results suggest that the grounded typology approach can provide quantitative evidence about semantic function in language.

## introduction within linguistics, typology is the subfield focused on the study of patterns and variation across the world's languages @xcite . to identify such patterns, linguists must carefully identify phenomena of interest within languages, and then align them with one another. for example, vowels exist in a continuous acoustic and perceptual space, without clear boundaries between them. to define vowel categories and align systems across languages, linguists rely largely on acoustic properties of the speech signal-reducing the problem to a physically grounded, empirical one @xcite . figure 1 : mean and standard deviation of per-language mutual information estimates between word class and image. across 30 languages, we see clear and consistent tendencies about which parts of speech are more "grounded", corresponding to a distinction between lexical and functional classes. while empirically grounding language form (surface structure like vowels) is typically straightforward, language is not just a formal system, but also a functional one. many questions within typology relate to the relationship between form and meaning, especially in domains like morphology and syntax. typically, typologists manually identify semantic/functional roles such as "subject", and "causative" and study their expression across languages @xcite . unlike with many definitions based on form, definitions based on meaning are left up to subjective discretion, leading to debates which reduce to the definition of particular terms cross-linguistically @xcite @xcite . instead, we propose a "grounded" approach to typology, which (under certain assumptions), allows the quantification and cross-linguistic comparison of language function and semantics across languages. by looking at sentences produced as captions of the same image across languages, we can use the image as an evidence-based, languageagnostic representation of the shared semantics underlying these utterances, similar to the evidencebased acoustic signal in the study of vowel spaces. in this work, we specifically focus on semantic contentfulness-how semantically informative a given word token is. we introduce a way to empirically quantify contentfulness, groundedness, which relies on vision-and-language models. groundedness measures how much less surprising a word is when we know the perceptual stimuli (i.e., the image) it describes. this surprisal difference between the surprisal of the word token in an image captioning model versus its surprisal in a language model is an estimate of the pointwise mutual information: the greater this difference (@xmath0, the more grounded the word is in that context. as a case study, we apply this measure to the study of the typology of word classes ("parts of speech"). literature from cognitive, pyscho-and neurolinguistics all point to contentfulness being an organizing factor in word class processing and even formation and structure: low-content (functional) word classes have many different properties from high-content (lexical) classes @xcite @xcite ). yet, there has been no cross-linguistic study of the relationship between contentfulness and word class. using our groundedness measure to quantify semantic contentfulness, we can estimate the mutual information of a word class with a caption's meaning (image). we find our measure largely rediscovers the distinction between lexical and functional word classes across 30 languages. further, though it correlates only weakly with psycholinguistic norms for imageability and concreteness in english, it provides an intuitive ranking (@xmath1. on the other hand, it contradicts the view of adpositions as a "semi-lexical" class @xcite and suggests grammatical word classes do carry some semantic content. these results thus partly validate and partly falsify received wisdom about word class contentfulness. they suggest the utility of this measure as a general tool for studying contentfulness in linguistics, and of taking a grounded approach to typological problems. we release the model used to estimate our measure and a dataset of groundedness values in 30 languages. 1 2 background an excellent example of the relevance of the relationship between semantic function and linguistic form to typology is word classes. within a particular language, there are typically groups of words 1 https://osf.io/bdhna/ unified by the (formal) contexts in which they can appear. further, this distribution of words is not arbitrary, but unified by a particular semantic prototype. for example, in english, nouns are a class of words which prototypically denote physical objects or things and can follow words like "the", "this", and "that". however, not all languages have words like "the", and so an equivalent formal-structural criterion cannot be given @xcite . on the other hand, semantic criteria are not sufficient to describe these classes: most languages can express prototypical verb or adjective meanings with the syntactic distribution of a noun. the elusiveness of a cross-linguistic definition for word classes leads to many debates about particular languages "having" or "not having" a distinction between (e.g.) nouns and verbs on the basis of a mix of formal and semantic criteria (cf. @xcite @xcite @xcite . here, we investigate word classes as operationalized in a framework where there is a fixed set of universally applicable word classes, as set out in the universal dependencies project @xcite .while this is problematic in general, our aim is not to claim that the assignment of word classes is precisely correct, but rather to empirically and quantitatively investigate the functional/semantic dimension of this common operationalisation of word class. in future work, we aim to investigate the relationship between these measures and non-prototypical parts of speech. ## method in this section, we define a token's groundedness, and show how we can use this to estimate the mutual information between parts of speech and representations of meaning. let the set of word types in a language be w. we assume a model of the data generation process where given a meaning m, a sentence is constructed by iteratively sampling a word w @xmath2. as mentioned previously, the groundedness of a token is given by its pointwise mutual information (pmi) with the meaning. as we cannot access the true meaning m, we must approximate it with a proxy. a good proxy for m should be language-neutral, and will make estimating the probabilities in equation 1 straightforward across languages. in this work, we focus on images as a language-neutral representation of meaning. images capture rich, language-independent information about the world state described by an image, and have proved useful as a method for aligning meanings across languages @xcite @xcite . further, a major strength of images as a meaning representation is that estimating both quantities in equation 1 becomes straightforward with neural models: p ϕ (w t | m, @xmath3, while p θ (w t |@xmath4. using images as a representation of meaning does have some implications for our approach. for instance, verbs, which usually denote events and are more temporally unstable @xcite than other parts of speech, may be less grounded than with a different meaning representation, such as videos. further, the language of image captions is somewhat restricted in terms of grammatical structure and lexical items, making the analysis of longtail phenomena or highly abstract language challenging @xcite . future work could use our framework to explore other meaning representations, such as symbolic models or videos (though doing so involves overcoming further dataset and modeling challenges). still, the language-neutral nature and rich information content of images allows us to study groundedness for a wide range of words, languages, and linguistic contexts. noting that a model's surprisal is negative log probability, we can view groundedness as a difference in surprisal, corresponding to how much more expected the token is under the grounded model than under the textual model. as such, the pmi should rarely take on negative values-because the captioning model has more information (both image and text) than the language model (text only). however, some tokens, such as those that are highly grammatical or structural, should be close to 0. in this work, we study the groundedness of word classes. drawing inspiration from functionalist typology, we treat a word class c i as a label selected by a linguist for a word in its context. we make an assumption that this label is independent of our meaning representation given a word's context, allowing us to define the following joint distribution: we can then formulate the mutual information between a word class and meaning as the expected value of the pmi between each token labeled with that class, and the token's associated image: given our factorization of the joint, we can perform a monte carlo estimation of the expectation by simply averaging groundedness over all the tokens tagged with c i in the data d: ## experimental setup captioning model p ϕ (w t | @xmath5, m) as our image captioning model, we use the recently released paligemma model @xcite . this model is by far the state-of-the-art among publicly available multilingual image captioning models. paligemma consists of an image encoder, initialized from the siglip-so400m model @xcite , and a transformer decoder language model, initialized from the gemma-2b language model (gemma, 2024). a linear projection maps from the image encoder space to a sequence of 256 tokens in the language model's embedding space. the whole system is then trained on a mix of vision-andlanguage datasets, including the unreleased webli dataset with 10 billion image-caption pairs in 109 languages @xcite , and the cc3m-35l dataset consisting of 3 million image-caption pairs in each of 35 languages @xcite . while paligemma is a general-purpose visionand-language model, it is designed to be fine-tuned on and applied to individual tasks. as such, we use the open-source paligemma-3b-ft-coco35-224 checkpoint for multilingual captioning, which has been fine-tuned on coco-35l. language model p θ (w t | @xmath6, m) as possible. this is critical to getting good (p)mi estimates, which relies on estimating a difference in surprisal between the two models. if the language model is not adapted to the image captioning domain, it may under-estimate the probability of particular words, leading to an over-estimation of mutual information. we therefore aim to match the training data between the language model and image captioning model, such that they see the same set of captions. to do so, we initialize our language model with the weights from the pretrained paligemma model paligemma-3b-pt-224. however, out of the box, the decoder behaves degenerately when no image is provided, so we need to adapt the model to not expect image information and to match the training data of the captioning model. to do so, we finetune the language model on the captions only from the coco-35l dataset. in this way, we ensure the models have observed the same data during training and are adapted to the same domain, and are thus maximally comparable. ## results the following sections quantitatively investigate the trends in our groundedness measure across languages and word classes. we begin by examining which word classes exhibit significant groundedness (section 5.1), followed by an analysis of crosslinguistic trends and their consistency (5.2 and 5.3). finally, we relate our findings to contentfulnessrelated psycholinguistic norms (5.4). ## discussion and conclusion we have proposed a grounded approach to typology, using images as a proxy for sentence meaning. using information theory and neural models, we define groundedness, a measure of a token's association with the meaning expressed in a sentence our results demonstrate that word classes display consistent patterns in terms of their groundedness across a typologically diverse sample of languages. we find these patterns can be described as a continuous cline which generalizes the traditionally dichotomous distinction between lexical and functional word classes into a gradient one. however, our results suggest grammatical word classes still carry semantic content. we find that @xmath7, in line with a view of these classes as a continuum; yet, our results contradict claims that adpositions are more lexical than other functional classes. our measure is related to surprisal, but diverges from it, particularly for concrete words. while this work has focused on word classes, groundedness enables the exploration of other aspects of how languages express function through form. future work could investigate in detail under what conditions "functional" items have higher groundedness. for example, do more spatial adpositions and determiners have higher groundedness than less spatial ones? humans tend to have difficulty scoring highly abstract and grammaticalized words, and getting contextual scores is difficult with existing psycholinguistic approaches: groundedness opens new ways to address these questions. our approach is also suitable for studying nonprototypical word class organizations, such as languages which do not clearly distinguish between adjectives and verbs (korean; @xcite , or languages that split individual word classes into distinct sub-classes (japanese adjectives; @xcite . future work should look at both formal and semantic sub-classes of parts of speechsuch as gerunds, participles, and different semantic classes of verbs (as in verbnet; @xcite -investigating their groundedness and how it aligns with or varies from existing metrics. in particular, we conjecture that boundary classes (e.g. gerunds) may display intermediate groundedness (between nouns and verbs) compared to prototypical members of those classes. groundedness makes it possible to test this conjecture with reference to the contexts in which words appear, which is needed for distinguishing syncretic forms. our approach can also cover any classes which can be defined over linguistic units, such as morphemes, phrases, or semantic classes. for instance, future work could explore the claim that inflections are more "grammatical" than derivations @xcite . similarly, our measure could be used to study the lexicalization or grammaticalization of constructions (as a decrease in groundedness over time). to support such work, we release our groundedness scores online. 8 going beyond the details of the approach here, our work generally suggests a role for multimodal models in computational typology similar to the one played by language models in the past decade (e.g. @xcite @xcite . while language coverage remains more limited than text models, the latest multimodal models and datasets cover enough typologically and culturally diverse languages to make them worth studying-and we anticipate coverage will only improve. further, the ability of multimodal models to provide an empirically grounded (if imperfect) representation of meaning makes them uniquely valuable for quantitatively addressing questions about the relation between form and function in language. our work provides the first study of this kind, and we hope that by demonstrating the utility of this approach and releasing our groundedness scores we will inspire other researchers to follow suit.