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

In distributional semantic accounts of the meaning of noun-noun compounds
(e.g. starfish, bank account, houseboat) the important role of constituent
polysemy remains largely unaddressed(cf. the meaning of star in starfish vs.
star cluster vs. star athlete). Instead of semantic vectors that average over
the different meanings of a constituent, disambiguated vectors of the
constituents would be needed in order to see what these more specific
constituent meanings contribute to the meaning of the compound as a whole. This
paper presents a novel approach to this specific problem of word sense
disambiguation: set expansion. We build on the approach developed by Mahabal et
al. (2018) which was originally designed to solve the analogy problem. We
modified their method in such a way that it can address the problem of sense
disambiguation of compound constituents. The results of experiments with a data
set of almost 9000 compounds (LADEC, Gagné et al. 2019) suggest that this
approach is successful, yet the success is sensitive to the frequency with
which the compounds are attested.

本文介绍了一种新颖的 Word Sense Disambiguation 方法，基于 set expansion 的思想，旨在解决复合词中多义词汇的歧义问题，实验表明该方法具有一定的成功率，但对复合词出现频率敏感。