CALaMo: a Constructionist Assessment of Language Models

This paper presents a novel framework for evaluating Neural Language Models' linguistic abilities using a constructionist approach. Not only is the usage-based model in line with the un- derlying stochastic philosophy of neural architectures, but it also allows the linguist to keep meaning as a determinant factor in the analysis. We outline the framework and present two possible scenarios for its application

FA3L at SemEval-2017 Task 3: A ThRee Embeddings Recurrent Neural Network for Question Answering

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AHyDA: Automatic Hypernym Detection with feature Augmentation

Several unsupervised methods for hypernym detection have been investigated in distributional semantics. Here we present a new approach based on a smoothed version of the distributional inclusion hypothesis. The new method is able to improve hypernym detection after testing on the BLESS dataset

Refining the Distributional Inclusion Hypothesis for Unsupervised Hypernym Identification

Several unsupervised methods for hypernym detection have been investigated in distributional semantics. Here we present a new approach based on a smoothed version of the distributional inclusion hypothesis. The new method is able to improve hypernym detection after testing on the BLESS dataset

Modelling Italian construction flexibility with distributional semantics: are constructions enough?

The present study combines psycholinguistic evidence on Italian valency coercion and a distributional analysis. The paper suggests that distributional properties can provide useful insights on how general abstract constructions influence the resolution of coercion effects. However, complete understanding of the processing and recognition of coercion requires to take into consideration the complex intertwining of lexical verb and abstract constructions

Event Knowledge in Compositional Distributional Semantics

The great majority of compositional models in distributional semantics present methods to compose vectors or tensors in a representation of the sentence. Here we propose to enrich one of the best performing methods (vector addition, which we take as a baseline) with distributional knowledge about events. The resulting model is able to outperform our baseline

Teaching NLP with Bracelets and Restaurant Menus:An Interactive Workshop for Italian Students

Although Natural Language Processing is at the core of many tools young people use in their everyday life, high school curricula (in Italy) do not include any computational linguistics education. This lack of exposure makes the use of such tools less responsible than it could be, and makes choosing computational linguistics as a university degree unlikely. To raise awareness, curiosity, and longer-term interest in young people, we have developed an interactive workshop designed to illustrate the basic principles of NLP and computational linguistics to high school Italian students aged between 13 and 18 years. The workshop takes the form of a game in which participants play the role of machines needing to solve some of the most common problems a computer faces in understanding language: from voice recognition to Markov chains to syntactic parsing. Participants are guided through the workshop with the help of instructors, who present the activities and explain core concepts from computational linguistics. The workshop was presented at numerous outlets in Italy between 2019 and 2020, both face-to-face and online

Proceedings of the Fifth Italian Conference on Computational Linguistics CLiC-it 2018

On behalf of the Program Committee, a very warm welcome to the Fifth Italian Conference on Computational Linguistics (CLiC-­‐it 2018). This edition of the conference is held in Torino. The conference is locally organised by the University of Torino and hosted into its prestigious main lecture hall “Cavallerizza Reale”. The CLiC-­‐it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after five years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges

Event Knowledge in Compositional Distributional Semantics

The great majority of compositional models in distributional semantics presents methods to compose distributional vectors or tensors in a representation of the sentence. Here we propose a linguistically motivated and cognitively inspired framework, in order to enrich the composition process with distributional knowledge about events and their typical participants