10,040 research outputs found
Comparative Analysis of Word Embeddings for Capturing Word Similarities
Distributed language representation has become the most widely used technique
for language representation in various natural language processing tasks. Most
of the natural language processing models that are based on deep learning
techniques use already pre-trained distributed word representations, commonly
called word embeddings. Determining the most qualitative word embeddings is of
crucial importance for such models. However, selecting the appropriate word
embeddings is a perplexing task since the projected embedding space is not
intuitive to humans. In this paper, we explore different approaches for
creating distributed word representations. We perform an intrinsic evaluation
of several state-of-the-art word embedding methods. Their performance on
capturing word similarities is analysed with existing benchmark datasets for
word pairs similarities. The research in this paper conducts a correlation
analysis between ground truth word similarities and similarities obtained by
different word embedding methods.Comment: Part of the 6th International Conference on Natural Language
Processing (NATP 2020
On the universal structure of human lexical semantics
How universal is human conceptual structure? The way concepts are organized
in the human brain may reflect distinct features of cultural, historical, and
environmental background in addition to properties universal to human
cognition. Semantics, or meaning expressed through language, provides direct
access to the underlying conceptual structure, but meaning is notoriously
difficult to measure, let alone parameterize. Here we provide an empirical
measure of semantic proximity between concepts using cross-linguistic
dictionaries. Across languages carefully selected from a phylogenetically and
geographically stratified sample of genera, translations of words reveal cases
where a particular language uses a single polysemous word to express concepts
represented by distinct words in another. We use the frequency of polysemies
linking two concepts as a measure of their semantic proximity, and represent
the pattern of such linkages by a weighted network. This network is highly
uneven and fragmented: certain concepts are far more prone to polysemy than
others, and there emerge naturally interpretable clusters loosely connected to
each other. Statistical analysis shows such structural properties are
consistent across different language groups, largely independent of geography,
environment, and literacy. It is therefore possible to conclude the conceptual
structure connecting basic vocabulary studied is primarily due to universal
features of human cognition and language use.Comment: Press embargo in place until publicatio
Local Causal States and Discrete Coherent Structures
Coherent structures form spontaneously in nonlinear spatiotemporal systems
and are found at all spatial scales in natural phenomena from laboratory
hydrodynamic flows and chemical reactions to ocean, atmosphere, and planetary
climate dynamics. Phenomenologically, they appear as key components that
organize the macroscopic behaviors in such systems. Despite a century of
effort, they have eluded rigorous analysis and empirical prediction, with
progress being made only recently. As a step in this, we present a formal
theory of coherent structures in fully-discrete dynamical field theories. It
builds on the notion of structure introduced by computational mechanics,
generalizing it to a local spatiotemporal setting. The analysis' main tool
employs the \localstates, which are used to uncover a system's hidden
spatiotemporal symmetries and which identify coherent structures as
spatially-localized deviations from those symmetries. The approach is
behavior-driven in the sense that it does not rely on directly analyzing
spatiotemporal equations of motion, rather it considers only the spatiotemporal
fields a system generates. As such, it offers an unsupervised approach to
discover and describe coherent structures. We illustrate the approach by
analyzing coherent structures generated by elementary cellular automata,
comparing the results with an earlier, dynamic-invariant-set approach that
decomposes fields into domains, particles, and particle interactions.Comment: 27 pages, 10 figures;
http://csc.ucdavis.edu/~cmg/compmech/pubs/dcs.ht
Prosodic description: An introduction for fieldworkers
This article provides an introductory tutorial on prosodic features such as tone and accent for researchers working on little-known languages. It specifically addresses the needs of non-specialists and thus does not presuppose knowledge of the phonetics and phonology of prosodic features. Instead, it intends to introduce the uninitiated reader to a field often shied away from because of its (in part real, but in part also just imagined) complexities. It consists of a concise overview of the basic phonetic phenomena (section 2) and the major categories and problems of their functional and phonological analysis (sections 3 and 4). Section 5 gives practical advice for documenting and analyzing prosodic features in the field.National Foreign Language Resource Cente
The Origins of Computational Mechanics: A Brief Intellectual History and Several Clarifications
The principle goal of computational mechanics is to define pattern and
structure so that the organization of complex systems can be detected and
quantified. Computational mechanics developed from efforts in the 1970s and
early 1980s to identify strange attractors as the mechanism driving weak fluid
turbulence via the method of reconstructing attractor geometry from measurement
time series and in the mid-1980s to estimate equations of motion directly from
complex time series. In providing a mathematical and operational definition of
structure it addressed weaknesses of these early approaches to discovering
patterns in natural systems.
Since then, computational mechanics has led to a range of results from
theoretical physics and nonlinear mathematics to diverse applications---from
closed-form analysis of Markov and non-Markov stochastic processes that are
ergodic or nonergodic and their measures of information and intrinsic
computation to complex materials and deterministic chaos and intelligence in
Maxwellian demons to quantum compression of classical processes and the
evolution of computation and language.
This brief review clarifies several misunderstandings and addresses concerns
recently raised regarding early works in the field (1980s). We show that
misguided evaluations of the contributions of computational mechanics are
groundless and stem from a lack of familiarity with its basic goals and from a
failure to consider its historical context. For all practical purposes, its
modern methods and results largely supersede the early works. This not only
renders recent criticism moot and shows the solid ground on which computational
mechanics stands but, most importantly, shows the significant progress achieved
over three decades and points to the many intriguing and outstanding challenges
in understanding the computational nature of complex dynamic systems.Comment: 11 pages, 123 citations;
http://csc.ucdavis.edu/~cmg/compmech/pubs/cmr.ht
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