3,293 research outputs found
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
An improved neural network model for joint POS tagging and dependency parsing
We propose a novel neural network model for joint part-of-speech (POS)
tagging and dependency parsing. Our model extends the well-known BIST
graph-based dependency parser (Kiperwasser and Goldberg, 2016) by incorporating
a BiLSTM-based tagging component to produce automatically predicted POS tags
for the parser. On the benchmark English Penn treebank, our model obtains
strong UAS and LAS scores at 94.51% and 92.87%, respectively, producing 1.5+%
absolute improvements to the BIST graph-based parser, and also obtaining a
state-of-the-art POS tagging accuracy at 97.97%. Furthermore, experimental
results on parsing 61 "big" Universal Dependencies treebanks from raw texts
show that our model outperforms the baseline UDPipe (Straka and Strakov\'a,
2017) with 0.8% higher average POS tagging score and 3.6% higher average LAS
score. In addition, with our model, we also obtain state-of-the-art downstream
task scores for biomedical event extraction and opinion analysis applications.
Our code is available together with all pre-trained models at:
https://github.com/datquocnguyen/jPTDPComment: 11 pages; In Proceedings of the CoNLL 2018 Shared Task: Multilingual
Parsing from Raw Text to Universal Dependencies, to appea
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