Search Results for author: Aless Lenci

Found 30 papers, 0 papers with code

Are Word Embeddings Really a Bad Fit for the Estimation of Thematic Fit?

no code implementations LREC 2020 Emmanuele Chersoni, Ludovica Pannitto, Enrico Santus, Aless Lenci, ro, Chu-Ren Huang

While neural embeddings represent a popular choice for word representation in a wide variety of NLP tasks, their usage for thematic fit modeling has been limited, as they have been reported to lag behind syntax-based count models.

Word Embeddings

Distributional Semantics Meets Construction Grammar. towards a Unified Usage-Based Model of Grammar and Meaning

no code implementations WS 2019 Giulia Rambelli, Emmanuele Chersoni, Philippe Blache, Chu-Ren Huang, Aless Lenci, ro

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.

Modeling Violations of Selectional Restrictions with Distributional Semantics

no code implementations WS 2018 Emmanuele Chersoni, Adri{\`a} Torrens Urrutia, Philippe Blache, Aless Lenci, ro

Distributional Semantic Models have been successfully used for modeling selectional preferences in a variety of scenarios, since distributional similarity naturally provides an estimate of the degree to which an argument satisfies the requirement of a given predicate.

Logical Metonymy in a Distributional Model of Sentence Comprehension

no code implementations SEMEVAL 2017 Emmanuele Chersoni, Aless Lenci, ro, Philippe Blache

In theoretical linguistics, logical metonymy is defined as the combination of an event-subcategorizing verb with an entity-denoting direct object (e. g., The author began the book), so that the interpretation of the VP requires the retrieval of a covert event (e. g., writing).

Retrieval Sentence

Towards a Distributional Model of Semantic Complexity

no code implementations WS 2016 Emmanuele Chersoni, Philippe Blache, Aless Lenci, ro

The composition cost of a sentence depends on the semantic coherence of the event being constructed and on the activation degree of the linguistic constructions.

Sentence

The CogALex-V Shared Task on the Corpus-Based Identification of Semantic Relations

no code implementations WS 2016 Enrico Santus, Anna Gladkova, Stefan Evert, Aless Lenci, ro

The task is split into two subtasks: (i) identification of related word pairs vs. unrelated ones; (ii) classification of the word pairs according to their semantic relation.

Language Acquisition Paraphrase Generation

Antonymy and Canonicity: Experimental and Distributional Evidence

no code implementations WS 2016 Andreana Pastena, Aless Lenci, ro

Previous studies have showed that some pairs of antonyms are perceived to be better examples of opposition than others, and are so considered representative of the whole category (e. g., Deese, 1964; Murphy, 2003; Paradis et al., 2009).

``Beware the Jabberwock, dear reader!'' Testing the distributional reality of construction semantics

no code implementations WS 2016 Gianluca Lebani, Aless Lenci, ro

Notwithstanding the success of the notion of construction, the computational tradition still lacks a way to represent the semantic content of these linguistic entities.

LexFr: Adapting the LexIt Framework to Build a Corpus-based French Subcategorization Lexicon

no code implementations LREC 2016 Giulia Rambelli, Gianluca Lebani, Laurent Pr{\'e}vot, Aless Lenci, ro

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.

Evaluating Context Selection Strategies to Build Emotive Vector Space Models

no code implementations LREC 2016 Lucia C. Passaro, Aless Lenci, ro

In this paper we compare different context selection approaches to improve the creation of Emotive Vector Space Models (VSMs).

Italian VerbNet: A Construction-based Approach to Italian Verb Classification

no code implementations LREC 2016 Lucia Busso, Aless Lenci, ro

This paper proposes a new method for Italian verb classification -and a preliminary example of resulting classes- inspired by Levin (1993) and VerbNet (Kipper-Schuler, 2005), yet partially independent from these resources; we achieved such a result by integrating Levin and VerbNet{'}s models of classification with other theoretic frameworks and resources.

Classification General Classification

Crowdsourcing for the identification of event nominals: an experiment

no code implementations LREC 2014 Rachele Sprugnoli, Aless Lenci, ro

This paper presents the design and results of a crowdsourcing experiment on the recognition of Italian event nominals.

Question Answering

Choosing which to use? A study of distributional models for nominal lexical semantic classification

no code implementations LREC 2014 Lauren Romeo, Gianluca Lebani, N{\'u}ria Bel, Aless Lenci, ro

This paper empirically evaluates the performances of different state-of-the-art distributional models in a nominal lexical semantic classification task.

General Classification Machine Translation +1

Bootstrapping an Italian VerbNet: data-driven analysis of verb alternations

no code implementations LREC 2014 Gianluca Lebani, Veronica Viola, Aless Lenci, ro

The goal of this paper is to propose a classification of the syntactic alternations admitted by the most frequent Italian verbs.

Classification General Classification +2

LexIt: A Computational Resource on Italian Argument Structure

no code implementations LREC 2012 Aless Lenci, ro, Gabriella Lapesa, Giulia Bonansinga

The aim of this paper is to introduce LexIt, a computational framework for the automatic acquisition and exploration of distributional information about Italian verbs, nouns and adjectives, freely available through a web interface at the address http://sesia. humnet. unipi. it/lexit.

Enriching the ISST-TANL Corpus with Semantic Frames

no code implementations LREC 2012 Aless Lenci, ro, Simonetta Montemagni, Giulia Venturi, Maria Grazia Cutrull{\`a}

The paper describes the design and the results of a manual annotation methodology devoted to enrich the ISST--TANL Corpus, derived from the Italian Syntactic--Semantic Treebank (ISST), with Semantic Frames information.

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