172,486 research outputs found
Crosslingual Transfer Learning for Low-Resource Languages Based on Multilingual Colexification Graphs
In comparative linguistics, colexification refers to the phenomenon of a
lexical form conveying two or more distinct meanings. Existing work on
colexification patterns relies on annotated word lists, limiting scalability
and usefulness in NLP. In contrast, we identify colexification patterns of more
than 2,000 concepts across 1,335 languages directly from an unannotated
parallel corpus. We then propose simple and effective methods to build
multilingual graphs from the colexification patterns: ColexNet and ColexNet+.
ColexNet's nodes are concepts and its edges are colexifications. In ColexNet+,
concept nodes are additionally linked through intermediate nodes, each
representing an ngram in one of 1,334 languages. We use ColexNet+ to train
\overrightarrow{\mbox{ColexNet+}}, high-quality multilingual embeddings that
are well-suited for transfer learning. In our experiments, we first show that
ColexNet achieves high recall on CLICS, a dataset of crosslingual
colexifications. We then evaluate \overrightarrow{\mbox{ColexNet+}} on
roundtrip translation, sentence retrieval and sentence classification and show
that our embeddings surpass several transfer learning baselines. This
demonstrates the benefits of using colexification as a source of information in
multilingual NLP.Comment: EMNLP 2023 Finding
BOOL-AN: A method for comparative sequence analysis and phylogenetic reconstruction
A novel discrete mathematical approach is proposed as an additional tool for molecular systematics which does not require prior statistical assumptions concerning the evolutionary process. The method is based on algorithms generating mathematical representations directly from DNA/RNA or protein sequences, followed by the output of numerical (scalar or vector) and visual characteristics (graphs). The binary encoded sequence information is transformed into a compact analytical form, called the Iterative Canonical Form (or ICF) of Boolean functions, which can then be used as a generalized molecular descriptor. The method provides raw vector data for calculating different distance matrices, which in turn can be analyzed by neighbor-joining or UPGMA to derive a phylogenetic tree, or by principal coordinates analysis to get an ordination scattergram. The new method and the associated software for inferring phylogenetic trees are called the Boolean analysis or BOOL-AN
Disentangling causal webs in the brain using functional Magnetic Resonance Imaging: A review of current approaches
In the past two decades, functional Magnetic Resonance Imaging has been used
to relate neuronal network activity to cognitive processing and behaviour.
Recently this approach has been augmented by algorithms that allow us to infer
causal links between component populations of neuronal networks. Multiple
inference procedures have been proposed to approach this research question but
so far, each method has limitations when it comes to establishing whole-brain
connectivity patterns. In this work, we discuss eight ways to infer causality
in fMRI research: Bayesian Nets, Dynamical Causal Modelling, Granger Causality,
Likelihood Ratios, LiNGAM, Patel's Tau, Structural Equation Modelling, and
Transfer Entropy. We finish with formulating some recommendations for the
future directions in this area
A topological approach to computer-aided sensitivity analysis
Sensitivities of any arbitrary system are calculated using general purpose digital computer with available software packages for transfer function analysis. Sensitivity shows how element variation within system affects system performance. Signal flow graph illustrates topological system behavior and relationship among parameters in system
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