Generics are expressions used to communicate abstractions about categories. While conveying general truths (e.g., “Birds fly”), generics have the interesting property to admit exceptions (e.g., penguins do not fly). Statements of this type help us organizing our knowledge of the world, and form the basis of how we express it (Hampton, 2012; Leslie, 2014).This study investigates how Large Language Models (LLMs) interpret generics, drawing upon psycholinguistic experimental methodologies. Understanding how LLMs interpret generic statements serves not only as a measure of their ability to abstract but also arguably plays a role in their encoding of stereotypes. Given that generics interpretation necessitates a comparison with explicitly quantified sentences, we explored i.) whether LLMs can correctly associate a quantifier with the generic structure, and ii.) whether the presence of a generic sentence as context influences the outcomes of quantifiers. We evaluated LLMs using both Surprisal distributions and prompting techniques.The findings indicate that models do not exhibit a strong sensitivity to quantification. Nevertheless, they seem to encode a meaning linked with the generic structure, which leads them to adjust their answers accordingly when a generalization is provided as context.

Noun-noun compounds interpretation is the task where a model is given one of such constructions, and it is asked to provide a paraphrase, making the semantic relation between the nouns explicit, as in carrot cake is “a cake made of carrots.” Such a task requires the ability to understand the implicit structured representation of the compound meaning. In this paper, we test to what extent the recent Large Language Models can interpret the semantic relation between the constituents of lexicalized English compounds and whether they can abstract from such semantic knowledge to predict the semantic relation between the constituents of similar but novel compounds by relying on analogical comparisons (e.g., carrot dessert). We test both Surprisal metrics and prompt-based methods to see whether i.) they can correctly predict the relation between constituents, and ii.) the semantic representation of the relation is robust to paraphrasing. Using a dataset of lexicalized and annotated noun-noun compounds, we find that LLMs can infer some semantic relations better than others (with a preference for compounds involving concrete concepts). When challenged to perform abstractions and transfer their interpretations to semantically similar but novel compounds, LLMs show serious limitations.

Research on conceptual abstraction has investigated the differences in contextual distributions, or “contextual variability,” of abstract and concrete concept words (e.g., *love* vs. *cat*). Empirical studies on this topic show that abstract words tend to occur in diverse linguistic contexts, while concrete words are typically constrained within more homogeneous contexts. Nonetheless, these investigations have somewhat overlooked a factor that influences both abstract and concrete concepts: *Categorial Specificity*, which denotes the inclusiveness of a category (e.g., *ragdoll* vs. *mammal*). We argue that more specific words are tied to narrower domains, independently or whether they are concrete or abstract, thus resulting in a diminished degree of contextual variability when compared to generic terms. In this study, we used distributional models to investigate the interplay between contextual variability, concreteness, specificity, and their interaction. Analyzing 676 English nouns, we found that contextual variability is explained by both concreteness and specificity: more specific words have closer contexts, while generic words, whether abstract or concrete, exhibit less related contexts.

An open question in language comprehension studies is whether non-compositional multiword expressions like idioms and compositional-but-frequent word sequences are processed differently. Are the latter constructed online, or are instead directly retrieved from the lexicon, with a degree of entrenchment depending on their frequency? In this paper, we address this question with two different methodologies. First, we set up a self-paced reading experiment comparing human reading times for idioms and both highfrequency and low-frequency compositional word sequences. Then, we ran the same experiment using the Surprisal metrics computed with Neural Language Models (NLMs). Our results provide evidence that idiomatic and high-frequency compositional expressions are processed similarly by both humans and NLMs. Additional experiments were run to test the possible factors that could affect the NLMs’ performance.

A large amount of literature on conceptual abstraction has investigated the differences in contextual distribution (namely “contextual variability”) between abstract and concrete concept words (“joy” vs. “apple”), showing that abstract words tend to be used in a wide variety of linguistic contexts. In contrast, concrete words usually occur in a few very similar contexts. However, these studies do not take into account another process that affects both abstract and concrete concepts alike: “specificity, that is, how inclusive a category is (“ragdoll” vs. “mammal”). We argue that the more a word is specific, the more its usage is tied to specific domains, and therefore its contextual variability is more limited compared to generic words. In this work, we used distributional semantic models to model the interplay between contextual variability measures and i) concreteness, ii) specificity, and iii) the interaction between the two variables. Distributional analyses on 662 Italian nouns showed that contextual variability is mainly explainable in terms of specificity or by the interaction between concreteness and specificity. In particular, the more specific a word is, the more its contexts will be close to it. In contrast, generic words have less related contexts, regardless of whether they are concrete or abstract.

Usage-based constructionist approaches consider language a structured inventory of constructions, form-meaning pairings of different schematicity and complexity, and claim that the more a linguistic pattern is encountered, the more it becomes accessible to speakers. However, when an expression is unavailable, what processes underlie the interpretation? While traditional answers rely on the principle of compositionality, for which the meaning is built word-by-word and incrementally, usage-based theories argue that novel utterances are created based on previously experienced ones through analogy, mapping an existing structural pattern onto a novel instance. Starting from this theoretical perspective, we propose here a computational implementation of these assumptions. As the principle of compositionality has been used to generate distributional representations of phrases, we propose a neural network simulating the construction of phrasal embedding as an analogical process. Our framework, inspired by word2vec and computer vision techniques, was evaluated on tasks of generalization from existing vectors.

Prior research has explored the ability of computational models to predict a word semantic fit with a given predicate. While much work has been devoted to modeling the typicality relation between verbs and arguments in isolation, in this paper we take a broader perspective by assessing whether and to what extent computational approaches have access to the information about the typicality of entire events and situations described in language (Generalized Event Knowledge). Given the recent success of Transformers Language Models (TLMs), we decided to test them on a benchmark for the dynamic estimation of thematic fit. The evaluation of these models was performed in comparison with SDM, a framework specifically designed to integrate events in sentence meaning representations, and we conducted a detailed error analysis to investigate which factors affect their behavior. Our results show that TLMs can reach performances that are comparable to those achieved by SDM. However, additional analysis consistently suggests that TLMs do not capture important aspects of event knowledge, and their predictions often depend on surface linguistic features, such as frequent words, collocations and syntactic patterns, thereby showing sub-optimal generalization abilities.

In linguistics and cognitive science, Logical metonymies are defined as type clashes between an event-selecting verb and an entity-denoting noun (e.g. The editor finished the article), which are typically interpreted by inferring a hidden event (e.g. reading) on the basis of contextual cues. This paper tackles the problem of logical metonymy interpretation, that is, the retrieval of the covert event via computational methods. We compare different types of models, including the probabilistic and the distributional ones previously introduced in the literature on the topic. For the first time, we also tested on this task some of the recent Transformer-based models, such as BERT, RoBERTa, XLNet, and GPT-2. Our results show a complex scenario, in which the best Transformer-based models and some traditional distributional models perform very similarly. However, the low performance on some of the testing datasets suggests that logical metonymy is still a challenging phenomenon for computational modeling.

In this paper, we propose a new type of semantic representation of Construction Grammar that combines constructions with the vector representations used in Distributional Semantics. We introduce a new framework, Distributional Construction Grammar, where grammar and meaning are systematically modeled from language use, and finally, we discuss the kind of contributions that distributional models can provide to CxG representation from a linguistic and cognitive perspective.

In this paper, we describe ROOT 18, a classifier using the scores of several unsupervised distributional measures as features to discriminate between semantically related and unrelated words, and then to classify the related pairs according to their semantic relation (i.e. synonymy, antonymy, hypernymy, part-whole meronymy). Our classifier participated in the CogALex-V Shared Task, showing a solid performance on the first subtask, but a poor performance on the second subtask. The low scores reported on the second subtask suggest that distributional measures are not sufficient to discriminate between multiple semantic relations at once.

This paper introduces LexFr, a corpus-based French lexical resource built by adapting the framework LexIt, originally developed to describe the combinatorial potential of Italian predicates. As in the original framework, the behavior of a group of target predicates is characterized by a series of syntactic (i.e., subcategorization frames) and semantic (i.e., selectional preferences) statistical information (a.k.a. distributional profiles) whose extraction process is mostly unsupervised. The first release of LexFr includes information for 2,493 verbs, 7,939 nouns and 2,628 adjectives. In these pages we describe the adaptation process and evaluated the final resource by comparing the information collected for 20 test verbs against the information available in a gold standard dictionary. In the best performing setting, we obtained 0.74 precision, 0.66 recall and 0.70 F-measure.