Hits 1.061 – 1.080 of 1.080
1061 |
Dynamic Integration of Multiple Evidence Sources for Ontology Learning
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In: http://eprints.weblyzard.com//56/1/wohlgenannt2012a-dynamicIntegrationOfMultipleEvidenceSources.pdf
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1062 |
An Innovative Two-Stage WSD Unsupervised Method Un Innovador Método No Supervisado para Desambiguación de Sentidos de Palabras basado en dos etapas
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In: http://www.sepln.org/revistaSEPLN/revista/40/15p20.pdf
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1063 |
Extractive Summarisation of Legal Texts
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In: http://www.ltg.ed.ac.uk/np/publications/ltg/papers/Hachey2006Extractive.pdf
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1064 |
EDUCATING LIA: THE DEVELOPMENT OF A LINGUISTICALLY ACCURATE MEMORY-BASED LEMMATISER FOR AFRIKAANS
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In: http://dl.ifip.org/db/conf/ifip12/iip2006/Groenewald06.pdf
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1065 |
Distributed Representations for Semantic Matching in non-factoid Question Answering
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In: http://ceur-ws.org/Vol-1204/papers/paper_9.pdf
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1066 |
Tutor Dialogue Planning with Contextual Information and Discourse Structure
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In: http://www.cs.cmu.edu/afs/cs/user/rwfisher/www/Curriculum_Vitae_files/fisher_simmons_its14.pdf
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1067 |
ONTOLOGY SWITCHING FOR THE SOCIAL SCIENCES. METHODS FOR THE UNDERLYING CORPUS ANALYSIS
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In: http://www.stroetgen.de/Dokumente/rc33-2004.pdf
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Abstract:
Semantic heterogeneity can be defined as the use of different documentation languages or ontologies for different document sets in digital libraries or other integrated document collections. Automatic translation between terms of different ontologies (“ontology switching”) is one approach to solve this problem. The underlying statistical methods based on corpus analysis allow the automatic identification of semantic relations (or “cross-concordances”) between terms from different ontologies. These methods require parallel corpora with documents that are indexed with terms from different ontologies. For the social sciences suitable corpora are very rare. Therefore, special modifications of the ontology switching approach help to deal with this lack. One alteration is simulating a parallel corpus for un-indexed document collections using machine learning and linguistic methods. Another change is translating not between two ontologies but between an ontology and suitable free text terms.
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Keyword:
Digital Libraries; Key words. Semantic Heterogeneity; Machine Learning; Metadata 1 TREATMENT OF SEMANTIC HETEROGENEITY; Natural Language Processing
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URL: http://www.stroetgen.de/Dokumente/rc33-2004.pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.71.5432
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1068 |
A Proposed Architecture for Automated Assessment of Use Case Diagrams
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In: http://research.ijcaonline.org/volume108/number4/pxc3900193.pdf
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1069 |
THE EFFECT OF PARALLEL CORPUS QUALITY VS SIZE IN ENGLISH-TO- TURKISH SMT
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In: http://airccj.org/CSCP/vol4/csit42503.pdf
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1070 |
Experience
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In: http://www.desilinguist.org/pdf/madnani-cv.pdf
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1071 |
CALICO Journal, 26(3)
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In: http://www.research.att.com/people/Stent_Amanda_J/library/publications/napolitanostent_09.pdf
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1072 |
Taxonomy and Evaluation of Markers for Computational
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In: http://world-comp.org/p2011/ICA3763.pdf
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1073 |
Gradient for Structured Prediction
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In: http://www.cs.sfu.ca/%7Eanoop/students/porus_patell/poruspatell-mscProject.pdf
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1074 |
Social Network- An autonomous system designed for radio recommendation.
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In: http://www.cri.ensmp.fr/classement/doc/A-410.pdf
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1075 |
A Maximum Entropy (ME) Based Translation Model for Chinese Characters Conversion
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In: http://www.cicling.org/2009/RCS-41/267-276.pdf
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1076 |
Effective Sentiment Analysis of Social Media Datasets using Naive Bayesian Classification
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In: http://research.ijcaonline.org/volume99/number13/pxc3898274.pdf
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1077 |
Deep Learning with Constraints for Answer-Agnostic Question Generation in Legal Text Understanding
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1079 |
A Hybrid approach for biomedical relation extraction using finite state automata and random forest-weighted fusion
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1080 |
The LAMBADA dataset: word prediction requiring a broad discourse context
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