13,423 research outputs found
The Structure of Phonological Networks Across Multiple Languages
The network characteristics based on the phonological similarities in the
lexicons of several languages were examined. These languages differed widely in
their history and linguistic structure, but commonalities in the network
characteristics were observed. These networks were also found to be different
from other networks studied in the literature. The properties of these networks
suggest explanations for various aspects of linguistic processing and hint at
deeper organization within human language.Comment: 5 pages, 3 figures, 2 tables, submitted to Phys. Rev.
Multilayer Network of Language: a Unified Framework for Structural Analysis of Linguistic Subsystems
Recently, the focus of complex networks research has shifted from the
analysis of isolated properties of a system toward a more realistic modeling of
multiple phenomena - multilayer networks. Motivated by the prosperity of
multilayer approach in social, transport or trade systems, we propose the
introduction of multilayer networks for language. The multilayer network of
language is a unified framework for modeling linguistic subsystems and their
structural properties enabling the exploration of their mutual interactions.
Various aspects of natural language systems can be represented as complex
networks, whose vertices depict linguistic units, while links model their
relations. The multilayer network of language is defined by three aspects: the
network construction principle, the linguistic subsystem and the language of
interest. More precisely, we construct a word-level (syntax, co-occurrence and
its shuffled counterpart) and a subword level (syllables and graphemes) network
layers, from five variations of original text (in the modeled language). The
obtained results suggest that there are substantial differences between the
networks structures of different language subsystems, which are hidden during
the exploration of an isolated layer. The word-level layers share structural
properties regardless of the language (e.g. Croatian or English), while the
syllabic subword level expresses more language dependent structural properties.
The preserved weighted overlap quantifies the similarity of word-level layers
in weighted and directed networks. Moreover, the analysis of motifs reveals a
close topological structure of the syntactic and syllabic layers for both
languages. The findings corroborate that the multilayer network framework is a
powerful, consistent and systematic approach to model several linguistic
subsystems simultaneously and hence to provide a more unified view on language
Transfer in a Connectionist Model of the Acquisition of Morphology
The morphological systems of natural languages are replete with examples of
the same devices used for multiple purposes: (1) the same type of morphological
process (for example, suffixation for both noun case and verb tense) and (2)
identical morphemes (for example, the same suffix for English noun plural and
possessive). These sorts of similarity would be expected to convey advantages
on language learners in the form of transfer from one morphological category to
another. Connectionist models of morphology acquisition have been faulted for
their supposed inability to represent phonological similarity across
morphological categories and hence to facilitate transfer. This paper describes
a connectionist model of the acquisition of morphology which is shown to
exhibit transfer of this type. The model treats the morphology acquisition
problem as one of learning to map forms onto meanings and vice versa. As the
network learns these mappings, it makes phonological generalizations which are
embedded in connection weights. Since these weights are shared by different
morphological categories, transfer is enabled. In a set of experiments with
artificial stimuli, networks were trained first on one morphological task
(e.g., tense) and then on a second (e.g., number). It is shown that in the
context of suffixation, prefixation, and template rules, the second task is
facilitated when the second category either makes use of the same forms or the
same general process type (e.g., prefixation) as the first.Comment: 21 pages, uuencoded compressed Postscrip
In search of isoglosses: continuous and discrete language embeddings in Slavic historical phonology
This paper investigates the ability of neural network architectures to
effectively learn diachronic phonological generalizations in a multilingual
setting. We employ models using three different types of language embedding
(dense, sigmoid, and straight-through). We find that the Straight-Through model
outperforms the other two in terms of accuracy, but the Sigmoid model's
language embeddings show the strongest agreement with the traditional
subgrouping of the Slavic languages. We find that the Straight-Through model
has learned coherent, semi-interpretable information about sound change, and
outline directions for future research
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