From a Lexical to a Semantic Distributional Hypothesis
Authors: Luigi Di Caro, Guido Boella, Alice Ruggeri, Loredana Cupi, Adebayo Kolawole, Livio Robaldo (Università di Torino, University of Luxembourg, Università di Bologna, 2015)
Topic: Distributional Semantics

Abstract
Distributional Semantics is based on the idea of extracting semantic information from lexical information in (multilingual) corpora using statistical algorithms. This paper presents the challenging aim of the SemBurst research project which applies distributional methods not only to words, but to sets of semantic information taken from existing semantic resources and associated with words in syntactic contexts. The idea is to inject semantics into vector space models to find correlations between statements (rather than between words). The proposal may have strong impact on key applications such as Word Sense Disambiguation, Textual Entailment, and others.

Introduction
One of the main current research frontiers in Computational Linguistics is represented by studies and techniques usually associated with the label “Distributional Semantics” (DS), which are focused on the exploitation of distributional analyses of words in syntactic compositions. Their importance is demonstrated by recent ERC projects (COMPOSES and DisCoTex) and by a growing research interest in the scientific community. The proposal presented in this paper is about going far beyond this state of the art. DS uses traditional Data Mining (DM) techniques on text, considering language as a grammar-based type of data, instead of simple unstructured sequences of tokens. It quantifies semantic (in truth lexical) similarities between linguistically-refined tokens (words, lemmas, parts-of-speech, etc.), based on their distributional properties in large corpora. DM relies on Vector Space Models (VSMs), a representation of textual information as vectors of numeric values AUTHOR. DM techniques such as Latent Semantic Analysis (LSA) have been successfully applied to text for information indexing and extraction tasks, using matrix decompositions such as Singular Value Decomposition (SVD) to reconstruct the latent structure behind the distributional hypothesis AUTHOR. It usually works by evaluating the relatedness of different terms, forming word clusters sharing similar contexts. Explicit Semantic Analysis (ESA) AUTHOR and Salient Semantic Analysis (SSA) AUTHOR revisits these methods in the way they define the conceptual layer. With LSA a word's hidden concept is based on its surrounding words, with ESA it is based on Wikipedia entries, and with SSA it is based on hyperlinked words in Wikipedia entries. These approaches represent only a partial step towards the use of semantic information as input for Distributional Analysis. While distributional representations excel at modelling lexical semantic phenomena such as word similarity and categorization (conceptual aspect), Formal Semantics in Computational Linguistics focuses on the representation of the meaning in a set theoretic way (functional aspect), providing a systematic treatment of compositionality and reasoning. Recent interest in the combination of Formal Semantics and Distributional Semantics have been proposed AUTHOR AUTHOR AUTHOR, that employ approaches based on the lexical level. However, 1) the problem of compositionality of lexical distributional vectors is still open and the proposed solutions are limited to combination of vectors, 2) reasoning on classic distributional representations is not possible, since they are VSMs at the lexical level only, 3) the connection of DS with traditional Formal Semantics is not straightforward AUTHOR AUTHOR since DS is limited to a semantics of similarity which is able to support retrieval but not other aspects such as reasoning; and 4) DS does not scale up to phrases and sentences due to data sparseness and growth in model size AUTHOR, restraining the use of tensors. Distributional Hypothesis

Conclusion
This paper presents a recently-funded project on a research frontier in Computational Linguistic. It includes a brief survey on the topic and the essential keys of the proposal with its impact.