Gianluca E. Lebani

Lexical Variability and Compositionality: Investigating Idiomaticity with Distributional Semantic Models

In this work we carried out an idiom type identification task on a set of 90 Italian V-NP and V-PP constructions comprising both idioms and non-idioms. Lexical variants were generated from these expressions by replacing their components with semantically related words extracted distributionally and from the Italian section of MultiWordNet. Idiomatic phrases turned out to be less similar to their lexical variants with respect to non-idiomatic ones in distributional semantic spaces. Different variant-based distributional measures of idiomaticity were tested. Our indices proved reliable in identifying also those idioms whose lexical variants are poorly or not at all attested in our corpus.

The Emotions of Abstract Words: A Distributional Semantic Analysis

Recent psycholinguistic and neuroscientific research has emphasized the crucial role of emotions for abstract words, which would be grounded by affective experience, instead of a sensorimotor one. The hypothesis of affective embodiment has been proposed as an alternative to the idea that abstract words are linguistically coded and that linguistic processing plays a key role in their acquisition and processing. In this paper, we use distributional semantic models to explore the complex interplay between linguistic and affective information in the representation of abstract words. Distributional analyses on Italian norming data show that abstract words have more affective content and tend to co-occur with contexts with higher emotive values, according to affective statistical indices estimated in terms of distributional similarity with a restricted number of seed words strongly associated with a set of basic emotions. Therefore, the strong affective content of abstract words might just be an indirect byproduct of co-occurrence statistics. This is consistent with a version of representational pluralism in which concepts that are fully embodied either at the sensorimotor or at the affective level live side-by-side with concepts only indirectly embodied via their linguistic associations with other embodied words.

A Distributional Model of Verb-Specific Semantic Roles Inferences

In a standard view, commonly adopted in psycholinguistics and computational linguistics, thematic roles are approached as primitive entities able to represent the roles played by the arguments of a predicate. In theoretical linguistics, however, the inability to reach a consensus on a primitive set of semantic roles led to the proposal of new approaches in which thematic roles are better described as a bundle of more primitive entities (e.g., Dowty, 1991; Van Valin, 1999) or as structural configurations (e.g., Jackendoff, 1987). In a complementary way, psycholinguistic evidence supports the idea that thematic roles and nominal concepts are represented in similar ways (McRae et al., 1997b; Ferretti et al., 2001), thus suggesting that the former can be accounted for as predicate-specific bundles of inferences activated by the semantics of the verb (e.g., the patient of kill is typically alive before the event and dead afterward). Such inferences can take the form of either presuppositions or entailment relations activated when a filler saturates a specific argument position for a given predicate. Our aim in this chapter is twofold. First, we report behavioral data collected to obtain a more fine-grained characterization of the thematic role properties activated by a subset of English verbs. To this end, we employed the modified version of the McRae et al. (1997b) elicitation paradigm proposed by Lebani et al. (2015) to describe which semantic properties of the participants are more relevant in each phase of the action described by the predicate. Next, we test the possibility to model such verb-specific inference patterns by exploiting corpus-based distributional data, thus proposing a novel approach to represent the same level of semantic knowledge that is currently described by means of a finite set of thematic roles.