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- 75 creator erhard-hinrichs.
- 75 creator monica-lau.
- 75 type InProceedings.
- 75 label "In Contrast - A Complex Discourse Connective".
- 75 sameAs 75.
- 75 abstract "This paper presents a corpus-based study of the discourse connective in contrast . The corpus data are drawn from the British National Corpus (BNC) and are analyzed at the levels of syntax, discourse structure, and compositional semantics. Following Webber et al. (2003), the paper argues that in contrast crucially involves discourse anaphora and, thus, resembles other discourse adverbials such as then , otherwise , and nevertheless . The compositional semantics proposed for other discourse connectives, however, does not straightforwardly generalize to in contrast , for which the notions of contrast pairs and contrast properties are essential.".
- 75 hasAuthorList authorList.
- 75 hasTopic Linguistics.
- 75 isPartOf proceedings.
- 75 keyword "Corpus (creation, annotation, etc.)".
- 75 keyword "Discourse".
- 75 keyword "Semantics".
- 75 title "In Contrast - A Complex Discourse Connective".
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- 750 creator arnaud-delhay.
- 750 creator jonathan-chevelu.
- 750 creator nelly-barbot.
- 750 creator olivier-boeffard.
- 750 type InProceedings.
- 750 label "Comparing Set-Covering Strategies for Optimal Corpus Design".
- 750 sameAs 750.
- 750 abstract "This article is interested in the problem of the linguistic content of a speech corpus. Depending on the target task, the phonological and linguistic content of the corpus is controlled by collecting a set of sentences which covers a preset description of phonological attributes under the constraint of an overall duration as small as possible. This goal is classically achieved by greedy algorithms which however do not guarantee the optimality of the desired cover. In recent works, a lagrangian-based algorithm, called LamSCP, has been used to extract coverings of diphonemes from a large corpus in French, giving better results than a greedy algorithm. We propose to keep comparing both algorithms in terms of the shortest duration, stability and robustness by achieving multi-represented diphoneme or triphoneme covering. These coverings correspond to very large scale optimization problems, from a corpus in English. For each experiment, LamSCP improves the greedy results from 3.9 to 9.7 percent.".
- 750 hasAuthorList authorList.
- 750 hasTopic Linguistics.
- 750 isPartOf proceedings.
- 750 keyword "Acquisition, Machine Learning".
- 750 keyword "Corpus (creation, annotation, etc.)".
- 750 keyword "Tools, systems, applications".
- 750 title "Comparing Set-Covering Strategies for Optimal Corpus Design".
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- 751 creator haejoong-lee.
- 751 creator lauren-friedman.
- 751 creator meghan-lammie-glenn.
- 751 creator shawn-medero.
- 751 creator stephanie-strassel.
- 751 type InProceedings.
- 751 label "Management of Large Annotation Projects Involving Multiple Human Judges: a Case Study of GALE Machine Translation Post-editing".
- 751 sameAs 751.
- 751 abstract "Managing large groups of human judges to perform any annotation task is a challenge. Linguistic Data Consortium coordinated the creation of manual machine translation post-editing results for the DARPA Global Autonomous Language Exploration Program. Machine translation is one of three core technology components for GALE, which includes an annual MT evaluation administered by National Institute of Standards and Technology. Among the training and test data LDC creates for the GALE program are gold standard translations for system evaluation. The GALE machine translation system evaluation metric is edit distance, measured by HTER (human translation edit rate), which calculates the minimum number of changes required for highly-trained human editors to correct MT output so that it has the same meaning as the reference translation. LDC has been responsible for overseeing the post-editing process for GALE. We describe some of the accomplishments and challenges of completing the post-editing effort, including developing a new web-based annotation workflow system, and recruiting and training human judges for the task. In addition, we suggest that the workflow system developed for post-editing could be ported efficiently to other annotation efforts.".
- 751 hasAuthorList authorList.
- 751 hasTopic Linguistics.
- 751 isPartOf proceedings.
- 751 keyword "Corpus (creation, annotation, etc.)".
- 751 keyword "Machine Translation, SpeechToSpeech Translation".
- 751 keyword "Other".
- 751 title "Management of Large Annotation Projects Involving Multiple Human Judges: a Case Study of GALE Machine Translation Post-editing".
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