4,048 research outputs found

    A Transition-Based Directed Acyclic Graph Parser for UCCA

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    We present the first parser for UCCA, a cross-linguistically applicable framework for semantic representation, which builds on extensive typological work and supports rapid annotation. UCCA poses a challenge for existing parsing techniques, as it exhibits reentrancy (resulting in DAG structures), discontinuous structures and non-terminal nodes corresponding to complex semantic units. To our knowledge, the conjunction of these formal properties is not supported by any existing parser. Our transition-based parser, which uses a novel transition set and features based on bidirectional LSTMs, has value not just for UCCA parsing: its ability to handle more general graph structures can inform the development of parsers for other semantic DAG structures, and in languages that frequently use discontinuous structures.Comment: 16 pages; Accepted as long paper at ACL201

    Evolution of Neural Networks for Helicopter Control: Why Modularity Matters

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    The problem of the automatic development of controllers for vehicles for which the exact characteristics are not known is considered in the context of miniature helicopter flocking. A methodology is proposed in which neural network based controllers are evolved in a simulation using a dynamic model qualitatively similar to the physical helicopter. Several network architectures and evolutionary sequences are investigated, and two approaches are found that can evolve very competitive controllers. The division of the neural network into modules and of the task into incremental steps seems to be a precondition for success, and we analyse why this might be so

    Combining linguistic and statistical analysis to extract relations from web documents

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    Search engines, question answering systems and classification systems alike can greatly profit from formalized world knowledge. Unfortunately, manually compiled collections of world knowledge (such as WordNet or the Suggested Upper Merged Ontology SUMO) often suffer from low coverage, high assembling costs and fast aging. In contrast, the World Wide Web provides an endless source of knowledge, assembled by millions of people, updated constantly and available for free. In this paper, we propose a novel method for learning arbitrary binary relations from natural language Web documents, without human interaction. Our system, LEILA, combines linguistic analysis and machine learning techniques to find robust patterns in the text and to generalize them. For initialization, we only require a set of examples of the target relation and a set of counterexamples (e.g. from WordNet). The architecture consists of 3 stages: Finding patterns in the corpus based on the given examples, assessing the patterns based on probabilistic confidence, and applying the generalized patterns to propose pairs for the target relation. We prove the benefits and practical viability of our approach by extensive experiments, showing that LEILA achieves consistent improvements over existing comparable techniques (e.g. Snowball, TextToOnto)

    Grammatical treatment and specific language impairment: Neighbourhood density & third person singular –s

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    This is the author's accepted manuscript. The original publication is available at http://www.tandfonline.com/doi/full/10.3109/02699206.2013.789928The purpose of this study was to test the effect of manipulating verb neighbourhood density in treatment targeting the third person singular lexical affix. Using a single-subject experimental design, 6 pre-schoolers with Specific Language Impairment (SLI) were randomly assigned to one of two conditions: 1) treatment with sparse verbs or 2) treatment with dense verbs in 12 sessions. The third person singular lexical affix was targeted for 12 sessions of treatment in both conditions. Treatment gain and generalization were measured as the dependent variables. Third person singular % correct change from pre-treatment to post-treatment was measured using sentence production tasks with comparisons across the two treatment conditions. Treatment gain and generalization were greater for children enrolled in the sparse condition. Preliminary clinical recommendations are made and theoretical implications are discussed relative to neighbourhood density effects on lexical activation and storage in children with SLI

    The interface between neighborhood density and optional infinitives: Normal development and Specific Language Impairment

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    The effect of neighborhood density on optional infinitives was evaluated for typically developing (TD) children and children with Specific Language Impairment (SLI). Forty children, 20 in each group, completed two production tasks that assessed third person singular production. Half of the sentences in each task presented a dense verb, and half presented a sparse verb. Children's third person singular accuracy was compared across dense and sparse verbs. Results showed that the TD group was significantly less likely to use optional infinitives with dense, rather than sparse verbs. In contrast, the distribution of optional infinitives for the SLI group was independent of verb neighborhood density. Follow-up analyses showed that the lack of neighborhood density effect for the SLI group could not be attributed to heterogeneous neighborhood density effects or floor effects. Results were interpreted within the Optional Infinitive/Extended Optional Infinitive accounts for typical language development and SLI for English speaking children.National Institutes of Health DC00433, RR7031K, DC00076, DC001694 (PI: Gierut

    Grammatical treatment and specific language impairment: Neighborhood density & third person singular -s

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    The purpose of this study was to test the effect of manipulating verb neighbourhood density in treatment targeting the third person singular lexical affix. Using a single-subject experimental design, 6 pre-schoolers with Specific Language Impairment (SLI) were randomly assigned to one of two conditions: 1) treatment with sparse verbs or 2) treatment with dense verbs in 12 sessions. The third person singular lexical affix was targeted for 12 sessions of treatment in both conditions. Treatment gain and generalization were measured as the dependent variables. Third person singular % correct change from pre-treatment to post-treatment was measured using sentence production tasks with comparisons across the two treatment conditions. Treatment gain and generalization were greater for children enrolled in the sparse condition. Preliminary clinical recommendations are made and theoretical implications are discussed relative to neighbourhood density effects on lexical activation and storage in children with SLI.National Institutes of Health DC00433, RR7031K, DC00076, DC001694 (PI: Gierut)This is an Accepted Manuscript of an article published by Taylor & Francis in Clinical Linguistics & Phonetics on September 2013, available online: http://wwww.tandfonline.com/10.3109/02699206.2013.789928
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