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Using Global Constraints and Reranking to Improve Cognates Detection ...
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Acquisition of Translation Lexicons for Historically Unwritten Languages via Bridging Loanwords ...
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Data Cleaning for XML Electronic Dictionaries via Statistical Anomaly Detection ...
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Data Cleaning for XML Electronic Dictionaries via Statistical Anomaly Detection
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Statistical modality tagging from rule-based annotations and crowdsourcing ...
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Use of Modality and Negation in Semantically-Informed Syntactic MT ...
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Annotating Cognates and Etymological Origin in Turkic Languages ...
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Correcting Errors in Digital Lexicographic Resources Using a Dictionary Manipulation Language ...
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A random forest system combination approach for error detection in digital dictionaries ...
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Bucking the Trend: Large-Scale Cost-Focused Active Learning for Statistical Machine Translation ...
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Taking into Account the Differences between Actively and Passively Acquired Data: The Case of Active Learning with Support Vector Machines for Imbalanced Datasets ...
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Rapid Adaptation of POS Tagging for Domain Specific Uses ...
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Detecting Structural Irregularity in Electronic Dictionaries Using Language Modeling ...
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A Modality Lexicon and its use in Automatic Tagging ...
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Abstract:
This paper describes our resource-building results for an eight-week JHU Human Language Technology Center of Excellence Summer Camp for Applied Language Exploration (SCALE-2009) on Semantically-Informed Machine Translation. Specifically, we describe the construction of a modality annotation scheme, a modality lexicon, and two automated modality taggers that were built using the lexicon and annotation scheme. Our annotation scheme is based on identifying three components of modality: a trigger, a target and a holder. We describe how our modality lexicon was produced semi-automatically, expanding from an initial hand-selected list of modality trigger words and phrases. The resulting expanded modality lexicon is being made publicly available. We demonstrate that one tagger---a structure-based tagger---results in precision around 86% (depending on genre) for tagging of a standard LDC data set. In a machine translation application, using the structure-based tagger to annotate English modalities on an English-Urdu ... : 6 pages, 5 figures; appeared in Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10), May 2010 ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences; I.2.7
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URL: https://dx.doi.org/10.48550/arxiv.1410.4868 https://arxiv.org/abs/1410.4868
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Semantically-Informed Syntactic Machine Translation: A Tree-Grafting Approach ...
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