In this work, we propose a Distributional Semantic resource enriched with
linguistic and lexical information extracted from electronic dictionaries,
designed to address the challenge of bridging the gap between the continuous
semantic values represented by distributional vectors and the discrete
descriptions offered by general semantics theory. Recently, many researchers
have concentrated on the nexus between embeddings and a comprehensive theory of
semantics and meaning. This often involves decoding the representation of word
meanings in Distributional Models into a set of discrete, manually constructed
properties such as semantic primitives or features, using neural decoding
techniques. Our approach introduces an alternative strategy grounded in
linguistic data. We have developed a collection of domain-specific
co-occurrence matrices, derived from two sources: a classification of Italian
nouns categorized into 4 semantic traits and 20 concrete noun sub-categories,
and a list of Italian verbs classified according to their semantic classes. In
these matrices, the co-occurrence values for each word are calculated
exclusively with a defined set of words pertinent to a particular lexical
domain. The resource comprises 21 domain-specific matrices, one comprehensive
matrix, and a Graphical User Interface. Our model facilitates the generation of
reasoned semantic descriptions of concepts by selecting matrices directly
associated with concrete conceptual knowledge, such as a matrix based on
location nouns and the concept of animal habitats. We assessed the utility of
the resource through two experiments, achieving promising outcomes in both: the
automatic classification of animal nouns and the extraction of animal features.

提出了一种分布语义资源，该资源包含从电子词典提取的语言和词汇信息，通过领域特定的共现矩阵来桥接分布向量和一般语义理论之间的差距，并通过两个实验验证了该资源的有效性，实现了动物名词的自动分类和特征提取。

用于生成意大利语复杂概念描述的领域嵌入

Domain Embeddings for Generating Complex Descriptions of Concepts in  Italian Language

The advent of Generative Adversarial Networks (GANs) has brought about
completely novel ways of transforming and manipulating pixels in digital
images. GAN based techniques such as Image-to-Image translations, DeepFakes,
and other automated methods have become increasingly popular in creating fake
images. In this paper, we propose a novel approach to detect GAN generated fake
images using a combination of co-occurrence matrices and deep learning. We
extract co-occurrence matrices on three color channels in the pixel domain and
train a model using a deep convolutional neural network (CNN) framework.
Experimental results on two diverse and challenging GAN datasets comprising
more than 56,000 images based on unpaired image-to-image translations (cycleGAN
[1]) and facial attributes/expressions (StarGAN [2]) show that our approach is
promising and achieves more than 99% classification accuracy in both datasets.
Further, our approach also generalizes well and achieves good results when
trained on one dataset and tested on the other.

本文提出了一种新的方法来检测使用生成对抗网络生成的假图像，采用共生矩阵和深度学习的组合进行训练，结果表明在两个大型数据集上的分类准确度均超过 99％。