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Transforming Warrant Officer Career College Instructor Assessment for the Army Learning Model
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In: DTIC (2014)
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| 63 |
Overview of the ShARe/CLEF eHealth Evaluation Lab 2014
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In: Information Access Evaluation : Multilinguality, Multimodality, and Interaction ; 5th International Conference of the CLEF Initiative, CLEF 2014, Sheffield, UK, September 15-18, 2014. Proceedings / Evangelos Kanoulas . (Hrsg.). - Springer International Publishing, 2014. - (Lecture Notes in Computer Science ; 8685). - S. 172-191. - ISBN 978-3-319-11381-4 (2014)
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| 64 |
ParTes. Test Suite for Parsing Evaluation ; ParTes: Test suite para evaluación de analizadores sintácticos
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| 65 |
Secreção na orelha média em lactentes ocorrência, recorrência e aspectos relacionados
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| 66 |
Phonological remediation program in students with developmental dyslexia
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| 67 |
Avaliação do reconhecimento de palavras em alunos com dificuldades de aprendizagem da leitura
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| 68 |
Validation d'un test prédictif et d'une batterie d'évaluation du bredouillement
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| 69 |
SWETO: Large-Scale Semantic Web Test-bed
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In: Amit P. Sheth (2014)
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| 71 |
Les tests de langues : une nécessité ?
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In: ISSN: 0994-6632 ; Le Français dans le monde. Recherches et applications ; https://hal.archives-ouvertes.fr/hal-01423819 ; Le Français dans le monde. Recherches et applications, CLE International / Français dans le monde, 2013, L’évaluation en didactique des langues/cultures. Continuités, tensions, ruptures, pp.67-79 (2013)
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| 72 |
Semantic title evaluation and recommendation based on topic models
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Abstract:
To digest tremendous documents efficiently, people often resort to their titles, which normally provide a concise and semantic representation of main text. Some titles however are misleading due to lexical ambiguity or eye-catching intention. The requirement of reference summaries hampers using traditional lexical summarisation evaluation techniques for title evaluation. In this paper we develop semantic title evaluation techniques by comparing a title with other sentences in terms of topic-based similarity with regard to the whole document. We further give a statistical hypothesis test to check whether a title is favourable without any reference summary. As a byproduct, the top similar sentence can be recommended as a candidate for title. Experiments on patents, scientific papers and DUC'04 benchmarks show our Semantic Title Evaluation and Recommendation technique based on a recent Segmented Topic Model (STERSTM), performs substantially better than that based on the canonical model Latent Dirichlet Allocation (STERLDA). It can also recommend titles with quality comparable with the winners of DUC'04 in terms of summarising documents into very short summaries. ; 12 page(s)
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Keyword:
080100 Artificial Intelligence and Image Processing; Evaluation; Hypothesis test; Semantic; Topic models
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URL: http://hdl.handle.net/1959.14/281052
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| 73 |
How Autism Affects Speech Understanding in Multitalker Environments
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In: DTIC (2013)
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| 74 |
Lexical Link Analysis Application: Improving Web Service to Acquisition Visibility Portal
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In: DTIC (2013)
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| 75 |
SPCR2 High Risk Suicidal Behavior in Veterans - Assessment of Predictors and Efficacy of Dialectical Behavioral Therapy
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In: DTIC (2013)
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| 76 |
Is there a strong match between the construct of general proficiency in the minds of native speaker non-teachers and native speaker teachers? An exploratory study
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In: 93 (2013)
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| 77 |
Collaborative testing: evidence of learning in a controlled in-class study of undergraduate students, or "more learning while testing, less time only testing"
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In: The Western Conference on Science Education (2013)
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| 80 |
The Relationship Between Text Message Volume and Formal Writing Performance Among Upper Level High School Students and College Freshmen
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In: Doctoral Dissertations and Projects (2012)
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