| 41 |
IRRA at TREC 2009: Index Term Weighting based on Divergence From Independence Model
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In: DTIC (2009)
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| 42 |
POSTECH at TREC 2009 Blog Track: Top Stories Identification
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In: DTIC (2009)
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| 43 |
PRIS at 2009 Relevance Feedback track: Experiments in Language Model for Relevance Feedback
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In: DTIC (2009)
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| 44 |
Novel Topic Impact on Authorship Attribution
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In: DTIC (2009)
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| 45 |
Experiments on Related Entity Finding Track at TREC 2009
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In: DTIC (2009)
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| 46 |
Facet Classification of Blogs: Know-Center at the TREC 2009 Blog Distillation Task
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In: DTIC (2009)
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| 47 |
Integrating Language and Cultural Knowledge into the Army Officer Corps
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In: DTIC (2008)
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| 48 |
Linking Semantic and Knowledge Representations in a Multi-Domain Dialogue System
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In: DTIC (2007)
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| 49 |
Comparing Evaluation Metrics for Sentence Boundary Detection
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In: DTIC (2007)
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| 50 |
Techniques for Automatically Generating Biographical Summaries from News Articles
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In: DTIC (2007)
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| 51 |
The Effect of Bilingual Term List Size on Dictionary-Based Cross-Language Information Retrieval
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In: DTIC (2006)
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| 52 |
Effectively Using Syntax for Recognizing False Entailment
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In: DTIC (2006)
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| 53 |
Efficacy in Automated Language Translators
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In: DTIC (2006)
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Abstract:
This paper suggests an improved measure for evaluating the usefulness of automated machine language translators. With the Global War on Terror (GWOT), the Army has increasing interest and need for accurate language translation more than ever. Today, there are approximately 20,000 linguists with language training in either the Active Duty or Reserve components of the U.S. Army. Coalition operations and U.S. presence in Iraq, Kuwait, and other areas in the Middle East require Arabic translation. Unfortunately, the Army has never been able to maintain the number of linguists it needs, particularly in the hard-to-fill, low-density languages. Previous evaluations of machine translations usually rely on word error rate. Machine translation systems should be rated not in terms of their word error rate but in terms of human comprehension and usefulness, which is some function of word translation, syntax translation, and semantic interpretation. This study introduces a new method of evaluating human comprehension in the context of machine translation using a language translation program known as the Forward Area Language Converter (FALCon). A study was conducted where participants received seven translated articles in a random order. For each of the seven articles, the participants received a set of corresponding comprehension questions. The goal of the questions was to gear the reader toward intelligence gathering and to see if he could grasp main concepts and details. The results of this study suggest that word error rate is not an effective measure of the usefulness of a machine language translator. Comprehension tests perform better at evaluating a human's understanding of a translated document. This study further indicates strengths and weaknesses in each translator. ; See also ADM002075. Presented at the Army Science Conference (25th) held in Orlando, FL on 27-30 November 2006.
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Keyword:
*COMPREHENSION; *MACHINE TRANSLATION; *SYNTAX; COMPARISON; COMPONENT REPORTS; FALCON(FORWARD AREA LANGUAGE CONVERTER); Linguistics; SYMPOSIA; TEST AND EVALUATION; TRANSLATIONS
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URL: http://www.dtic.mil/docs/citations/ADA481410
http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA481410
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| 55 |
A Methodology for End-to-End Evaluation of Arabic Document Image Processing Software
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In: DTIC (2006)
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| 56 |
Measuring Translation Quality by Testing English Speakers with a New Defense Language Proficiency Test for Arabic
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In: DTIC (2005)
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| 57 |
Measuring Human Readability of Machine Generated Text: Three Case Studies in Speech Recognition and Machine Translation
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In: DTIC (2005)
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| 58 |
Criteria for Appraising Computer-Based Simulations for Teaching Arabic as a Foreign Language
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In: DTIC AND NTIS (2005)
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| 59 |
Syntactic Simplification for Improving Content Selection in Multi-Document Summarization
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In: DTIC (2004)
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| 60 |
Minimum Bayes-Risk Decoding for Statistical Machine Translation
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In: DTIC (2004)
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