4 research outputs found
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Application of value of information of tank waste characterization: A new paradigm for defining tank waste characterization requirements
This report presents the rationale for adopting a recommended characterization strategy that uses a risk-based decision-making framework for managing the Tank Waste Characterization program at Hanford. The risk-management/value-of-information (VOI) strategy that is illustrated explicitly links each information-gathering activity to its cost and provides a mechanism to ensure that characterization funds are spent where they can produce the largest reduction in risk. The approach was developed by tailoring well-known decision analysis techniques to specific tank waste characterization applications. This report illustrates how VOI calculations are performed and demonstrates that the VOI approach can definitely be used for real Tank Waste Remediation System (TWRS) characterization problems
Delexicalized Word Embeddings for Cross-lingual Dependency Parsing
International audienceThis paper presents a new approach to the problem of cross-lingual dependency parsing, aiming at leveraging training data from different source languages to learn a parser in a target language. Specifically , this approach first constructs word vector representations that exploit structural (i.e., dependency-based) contexts but only considering the morpho-syntactic information associated with each word and its contexts. These delexicalized word em-beddings, which can be trained on any set of languages and capture features shared across languages, are then used in combination with standard language-specific features to train a lexicalized parser in the target language. We evaluate our approach through experiments on a set of eight different languages that are part the Universal Dependencies Project. Our main results show that using such delexicalized embeddings, either trained in a monolin-gual or multilingual fashion, achieves significant improvements over monolingual baselines