3,619 research outputs found

    Modeling Graph Languages with Grammars Extracted via Tree Decompositions

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    Work on probabilistic models of natural language tends to focus on strings and trees, but there is increasing interest in more general graph-shaped structures since they seem to be better suited for representing natural language semantics, ontologies, or other varieties of knowledge structures. However, while there are relatively simple approaches to defining generative models over strings and trees, it has proven more challenging for more general graphs. This paper describes a natural generalization of the n-gram to graphs, making use of Hyperedge Replacement Grammars to define generative models of graph languages.9 page(s

    The Prosody of Uncertainty for Spoken Dialogue Intelligent Tutoring Systems

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    The speech medium is more than an audio conveyance of word strings. It contains meta information about the content of the speech. The prosody of speech, pauses and intonation, adds an extra dimension of diagnostic information about the quality of a speaker\u27s answers, suggesting an important avenue of research for spoken dialogue tutoring systems. Tutoring systems that are sensitive to such cues may employ different tutoring strategies based on detected student uncertainty, and they may be able to perform more precise assessment of the area of student difficulty. However, properly identifying the cues can be challenging, typically requiring thousands of hand labeled utterances for training in machine learning. This study proposes and explores means of exploiting alternate automatically generated information, utterance correctness and the amount of practice a student has had, as indicators of student uncertainty. It finds correlations with various prosodic features and these automatic indicators and compares the result with a small set of annotated utterances, and finally demonstrates a Bayesian classifier based on correctness scores as class labels

    Psychoeducational interventions in adolescent depression: A systematic review

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    Background: Adolescent depression is common and leads to distress and impairment for individuals/families. Treatment/prevention guidelines stress the need for good information and evidence-based psychosocial interventions. There has been growing interest in psychoeducational interventions (PIs), which broadly deliver accurate information about health issues and self-management. Objective, methods: Systematic search of targeted PIs as part of prevention/management approaches for adolescent depression. Searches were undertaken independently in PubMed, PsycINFO, EMBASE, guidelines, reviews (including Cochrane), and reference lists. Key authors were contacted. No restrictions regarding publishing dates. Results: Fifteen studies were included: seven targeted adolescents with depression/depressive symptoms, eight targeted adolescents ‘at risk' e.g. with a family history of depression. Most involved family/group programmes; others included individual, school-based and online approaches. PIs may affect understanding of depression, identification of symptoms, communication, engagement, and mental health outcomes. Conclusion, practice implications: PIs can have a role in preventing/managing adolescent depression, as a first-line or adjunctive approach. The limited number of studies, heterogeneity in formats and evaluation, and inconsistent approach to defining PI, make it difficult to compare programmes and measure overall effectiveness. Further work needs to establish an agreed definition of PI, develop/evaluate PIs in line with frameworks for complex interventions, and analyse their active components

    Semantic Parsing with Bayesian Tree Transducers

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    Many semantic parsing models use tree transformations to map between natural language and meaning representation. However, while tree transformations are central to several state-of-the-art approaches, little use has been made of the rich literature on tree automata. This paper makes the connection concrete with a tree transducer based semantic parsing model and suggests that other models can be interpreted in a similar framework, increasing the generality of their contributions. In particular, this paper further introduces a variational Bayesian inference algorithm that is applicable to a wide class of tree transducers, producing state-of-the-art semantic parsing results while remaining applicable to any domain employing probabilistic tree transducers.9 page(s

    Taxonomy of the spring dwelling amphipod Synurella ambulans (Crustacea: Crangonyctidae) in West Russia: with notes on its distribution and ecology

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    This study deals with taxonomic problems of the semi-subterranean crangonyctid amphipod Synurella ambulans (F. MĂŒller, 1846), well-known from various freshwater habitats in Europe. The taxonomy of the species S. ambulans and the generic diagnosis for the genus Synurella are revised. A new synonymy is proposed: Synurella ambulans (F. MĂŒller, 1846) = Synurella ambulans meschtscherica Borutzky, 1929, syn. nov. The affinity with the related groups, distribution and ecology of the species are examined

    Incompatibility of long-period neutron star precession with creeping neutron vortices

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    Aims: To determine whether ``vortex creep'' in neutron stars, the slow motion of neutron vortices with respect to pinning sites in the core or inner crust, is consistent with observations of long-period precession. Methods: Using the concept of vortex drag, I discuss the precession dynamics of a star with imperfectly-pinned (i.e., "creeping'') vortices. Results: The precession frequency is far too high to be consistent with observations, indicating that the standard picture of the outer core (superfluid neutrons in co-existence with type II, superconducting protons) should be reconsidered. There is a slow precession mode, but it is highly over-damped and cannot complete even a single cycle. Moreover, the vortices of the inner crust must be able to move with little dissipation with respect to the solid.Comment: 4 pages, v3. Missing reference adde

    Learning words and syntactic cues in highly ambiguous contexts

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    The cross-situational word learning paradigm argues that word meanings can be approximated by word-object associations, computed from co-occurrence statistics between words and entities in the world. Lexicon acquisition involves simultaneously guessing (1) which objects are being talked about (the ”meaning”) and (2) which words relate to those objects. However, most modeling work focuses on acquiring meanings for isolated words, largely neglecting relationships between words or physical entities, which can play an important role in learning. Semantic parsing, on the other hand, aims to learn a mapping between entire utterances and compositional meaning representations where such relations are central. The focus is the mapping between meaning and words, while utterance meanings are treated as observed quantities. Here, we extend the joint inference problem of word learning to account for compositional meanings by incorporating a semantic parsing model for relating utterances to non-linguistic context. Integrating semantic parsing and word learning permits us to explore the impact of word-word and concept-concept relations. The result is a joint-inference problem inherited from the word learning setting where we must simultaneously learn utterance-level and individual word meanings, only now we also contend with the many possible relationships between concepts in the meaning and words in the sentence. To simplify design, we factorize the model into separate modules, one for each of the world, the meaning, and the words, and merge them into a single synchronous grammar for joint inference. There are three main contributions. First, we introduce a novel word learning model and accompanying semantic parser. Second, we produce a corpus which allows us to demonstrate the importance of structure in word learning. Finally, we also present a number of technical innovations required for implementing such a model

    A PHOTOGRAMMETRIC WORKFLOW FOR RAPID SITE DOCUMENTATION AT STOBI, REPUBLIC OF NORTH MACEDONIA

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    The so-called “Theodosian Palace” is one of the most significant Late Antique structures at the site of Stobi, in the Republic of North Macedonia. Popularly thought to be a stopping-place of Theodosius I on his way through the province of Macedonia Secunda according to the evidence of the Codex Theodosianus, the structure is in dire need of conservation with many of the stone and mortar walls threatening to collapse onto the mosaic floors below. Any conservation effort in the Republic of North Macedonia must produce rigorous documentation before any physical work can take place. The most important and time consuming component of the project preparation are section and elevation drawings documenting each of the walls stone-by-stone, with elevations and scales indicated in a format prescribed by the state. These drawings are usually done manually on graph paper in the field, with the assistance of time-honoured manual tools – the plum-bob and tape-measure –, but this method is enormously time consuming and has considerable of room for error. The present project, begun in 2016 and the subject of this paper, endeavoured to show that new, photogrammetric methods could not only improve the accuracy of these drawings, but also the speed with which they are made. Our results demonstrate an increase in accuracy by an order magnitude, from 3 cm to 3 mm, and an improvement in the time to deliver the final product from an estimated 8 months to 2 months
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