118,013 research outputs found
Symbolic, Distributed and Distributional Representations for Natural Language Processing in the Era of Deep Learning: a Survey
No full textNatural language is inherently a discrete symbolic representation of human knowledge. Recent advances in machine learning (ML) and in natural language processing (NLP) seem to contradict the above intuition: discrete symbols are fading away, erased by vectors or tensors called distributed and distributional representations. However, there is a strict link between distributed/distributional representations and discrete symbols, being the first an approximation of the second. A clearer understanding of the strict link between distributed/distributional representations and symbols may certainly lead to radically new deep learning networks. In this paper we make a survey that aims to renew the link between symbolic representations and distributed/distributional representations. This is the right time to revitalize the area of interpreting how discrete symbols are represented inside neural networks
Can we explain natural language inference decisions taken with neural networks? Inference rules in distributed representations
No full textNatural Language Inference (NLI) is a key, complex task where machine learning (ML) is playing an important role. However, ML has progressively obfuscated the role of linguistically-motivated inference rules, which should be the core of NLI systems. In this paper, we introduce distributed inference rules as a novel way to encode linguistically-motivated inference rules in learning interpretable NLI classifiers. We propose two encoders: the Distributed Partial Tree Encoder and the Distributed Smoothed Partial Tree Encoder. These encoders allow modeling syntactic and syntactic-semantic inference rules as distributed representations ready to be used in ML models over large datasets. Although far from the state-of-the-art of end-to-end deep learning systems on large datasets, our shallow networks positively exploit inference rules for NLI, improving over baseline systems. This is a first positive step towards interpretable and explainable end-to-end deep learning systems
Have you lost the thread? Discovering on-going conversations in scattered dialog blocks
No full textFinding threads in textual dialogs is emerging as a need to better organize stored knowledge. We capture this need
by introducing the novel task of discovering on-going conversations in scattered dialog blocks. Our aim in this paper is
twofold. First, we propose a publicly available testbed for the task by solving the insurmountable problem of privacy of Big
Personal Data. In fact, we showed that personal dialogs can be surrogated with theatrical plays. Second, we propose a suite
of computationally light learning models that can use syntactic and semantic features. With this suite, we showed that models
for this challenging task should include features capturing shifts in language use and, possibly, modeling underlying scripts
Preparation, characterization, and utilization of monoclonal antibodies to the gene products of the HLA-D region, with special emphasis on those to polymorphic determinants
No full textTowards Syntax-aware Compositional Distributional Semantic Models
No full textCompositional Distributional Semantics Models (CDSMs) are traditionally seen as an entire different
world with respect to Tree Kernels (TKs). In this paper, we show that under a suitable
regime these two approaches can be regarded as the same and, thus, structural information and
distributional semantics can successfully cooperate in CSDMs for NLP tasks. Leveraging on
distributed trees, we present a novel class of CDSMs that encode both structure and distributional
meaning: the distributed smoothed trees (DSTs). By using DSTs to compute the similarity
among sentences, we implicitly define the distributed smoothed tree kernels (DSTKs). Experiment
with our DSTs show that DSTKs approximate the corresponding smoothed tree kernels
(STKs). Thus, DSTs encode both structural and distributional semantics of text fragments as
STKs do. Experiments on RTE and STS show that distributional semantics encoded in DSTKs
increase performance over structure-only kernels
Linear Compositional Distributional Semantics and Structural Kernels
No full textIn this paper, we want to start the analysis of the models for compositional distributional semantics (CDS) with respect to the distributional similarity. We believe that this simple analysis of the properties of the similarity can help to better investigate new CDS models. We show that, looking at CDS models from this point of view, these models are strictly related with convolution kernels (Haussler, 1999), e.g.: tree kernels (Collins and Duffy, 2002).
We will then examine how the distributed tree kernels (Zanzotto and Dell’Arciprete, 2012) are an interesting result to draw a stronger link between CDS models and convolution kernels
Distributed Smoothed Tree Kernel
No full textIn this paper we explore
the possibility to merge the world of
Compositional Distributional Semantic
Models (CDSM) with Tree Kernels
(TK). In particular, we will introduce a
specific tree kernel (smoothed tree kernel,
or STK) and then show that is
possibile to approximate such kernel
with the dot product of two vectors
obtained compositionally from the sentences,
creating in such a way a new
CDSM
L' ACCOUNTABILITY E LE AZIENDE EDC: PROBLEMI E PROSPETTIVE
No full textIIl contributo di questo lavoro è quello di analizzare alcune dimensioni dell’accountability, ovvero di un processo di definizione e di comunicazione esterna delle risultanze quantitative e qualitative della responsabilità aziendale. In questo lavoro, in parte evolutivo, cercheremo di comprendere il ruolo che ha l’Economia di comunione (Edc) (Lubich, 2001; Argiolas, 2009a; Gold, 2010) nella diffusione e la promozione di una accountability “civile” o di comunione nell’ambito della dottrina aziendale. L’intento è quello di dar vita ad un primo tentativo di declinazione degli elementi teorici di una accountability di comunione. Per far ciò è necessario, brevemente, ripercorrere il quadro concettuale in cui nasce l’accountability e richiamare i principi dell’EdC sui cui si innesta tale aspetto
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