2,550 research outputs found
Complex Networks Measures for Differentiation between Normal and Shuffled Croatian Texts
This paper studies the properties of the Croatian texts via complex networks.
We present network properties of normal and shuffled Croatian texts for
different shuffling principles: on the sentence level and on the text level. In
both experiments we preserved the vocabulary size, word and sentence frequency
distributions. Additionally, in the first shuffling approach we preserved the
sentence structure of the text and the number of words per sentence. Obtained
results showed that degree rank distributions exhibit no substantial deviation
in shuffled networks, and strength rank distributions are preserved due to the
same word frequencies. Therefore, standard approach to study the structure of
linguistic co-occurrence networks showed no clear difference among the
topologies of normal and shuffled texts. Finally, we showed that the in- and
out- selectivity values from shuffled texts are constantly below selectivity
values calculated from normal texts. Our results corroborate that the node
selectivity measure can capture structural differences between original and
shuffled Croatian texts
Comparison of the language networks from literature and blogs
In this paper we present the comparison of the linguistic networks from
literature and blog texts. The linguistic networks are constructed from texts
as directed and weighted co-occurrence networks of words. Words are nodes and
links are established between two nodes if they are directly co-occurring
within the sentence. The comparison of the networks structure is performed at
global level (network) in terms of: average node degree, average shortest path
length, diameter, clustering coefficient, density and number of components.
Furthermore, we perform analysis on the local level (node) by comparing the
rank plots of in and out degree, strength and selectivity. The
selectivity-based results point out that there are differences between the
structure of the networks constructed from literature and blogs
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
Uvid u automatsko izluÄivanje metaforiÄkih kolokacija
Collocations have been the subject of much scientific research over the years. The focus of this research is on a subset of collocations, namely metaphorical collocations. In metaphorical collocations, a semantic shift has taken place in one of the components, i.e., one of the components takes on a transferred meaning. The main goal of this paper is to review the existing literature and provide a systematic overview of the existing research on collocation extraction, as well as the overview of existing methods, measures, and resources. The existing research is classified according to the approach (statistical, hybrid, and distributional semantics) and presented in three separate sections. The insights gained from existing research serve as a first step in exploring the possibility of developing a method for automatic extraction of metaphorical collocations. The methods, tools, and resources that may prove useful for future work are highlighted.Kolokacije su veÄ dugi niz godina tema mnogih znanstvenih istraživanja. U fokusu ovoga istraživanja podskupina je kolokacija koju Äine metaforiÄke kolokacije. Kod metaforiÄkih je kolokacija kod jedne od sastavnica doÅ”lo do semantiÄkoga pomaka, tj. jedna od sastavnica poprima preneseno znaÄenje. Glavni su ciljevi ovoga rada istražiti postojeÄu literaturu te dati sustavan pregled postojeÄih istraživanja na temu izluÄivanja kolokacija i postojeÄih metoda, mjera i resursa. PostojeÄa istraživanja opisana su i klasificirana prema razliÄitim pristupima (statistiÄki, hibridni i zasnovani na distribucijskoj semantici). TakoÄer su opisane razliÄite asocijativne mjere i postojeÄi naÄini procjene rezultata automatskoga izluÄivanja kolokacija. Metode, alati i resursi koji su koriÅ”teni u prethodnim istraživanjima, a mogli bi biti korisni za naÅ” buduÄi rad posebno su istaknuti. SteÄeni uvidi u postojeÄa istraživanja Äine prvi korak u razmatranju moguÄnosti razvijanja postupka za automatsko izluÄivanje metaforiÄkih kolokacija
Acquiring and Harnessing Verb Knowledge for Multilingual Natural Language Processing
Advances in representation learning have enabled natural language processing models to derive non-negligible linguistic information directly from text corpora in an unsupervised fashion. However, this signal is underused in downstream tasks, where they tend to fall back on superficial cues and heuristics to solve the problem at hand. Further progress relies on identifying and filling the gaps in linguistic knowledge captured in their parameters. The objective of this thesis is to address these challenges focusing on the issues of resource scarcity, interpretability, and lexical knowledge injection, with an emphasis on the category of verbs.
To this end, I propose a novel paradigm for efficient acquisition of lexical knowledge leveraging native speakersā intuitions about verb meaning to support development and downstream performance of NLP models across languages. First, I investigate the potential of acquiring semantic verb classes from non-experts through manual clustering. This subsequently informs the development of a two-phase semantic dataset creation methodology, which combines semantic clustering with fine-grained semantic similarity judgments collected through spatial arrangements of lexical stimuli. The method is tested on English and then applied to a typologically diverse sample of languages to produce the first large-scale multilingual verb dataset of this kind. I demonstrate its utility as a diagnostic tool by carrying out a comprehensive evaluation of state-of-the-art NLP models, probing representation quality across languages and domains of verb meaning, and shedding light on their deficiencies. Subsequently, I directly address these shortcomings by injecting lexical knowledge into large pretrained language models. I demonstrate that external manually curated information about verbsā lexical properties can support data-driven models in tasks where accurate verb processing is key. Moreover, I examine the potential of extending these benefits from resource-rich to resource-poor languages through translation-based transfer. The results emphasise the usefulness of human-generated lexical knowledge in supporting NLP models and suggest that time-efficient construction of lexicons similar to those developed in this work, especially in under-resourced languages, can play an important role in boosting their linguistic capacity.ESRC Doctoral Fellowship [ES/J500033/1], ERC Consolidator Grant LEXICAL [648909
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