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From Stance to Concern: Adaptation of Propositional Analysis to New Tasks and Domains ...
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Detecting Asks in SE attacks: Impact of Linguistic and Structural Knowledge ...
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Adaptation of a Lexical Organization for Social Engineering Detection and Response Generation ...
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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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Semantically-Informed Syntactic Machine Translation: A Tree-Grafting Approach ...
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Use of Modality and Negation in Semantically-Informed Syntactic MT
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In: DTIC (2012)
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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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Citation Handling: Processing Citation Texts in Scientific Documents
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Measuring Degrees of Semantic Opposition ...
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Abstract:
Knowing the degree of semantic contrast, or oppositeness, between words has widespread application in natural language processing, including machine translation, and information retrieval. Manually-created lexicons focus on strict opposites, such as antonyms, and have limited coverage. On the other hand, only a few automatic approaches have been proposed, and none have been comprehensively evaluated. Even though oppositeness may seem to be a simple and fairly intuitive idea at first glance, any deeper analysis quickly reveals that it is in fact a complex and heterogeneous phenomenon. In this paper we present a large crowdsourcing experiment to determine the amount of human agreement on the concept of oppositeness and its different kinds. In the process, we flesh out key features of different kinds of opposites and also determine their relative prevalence. We then present an automatic and empirical measure of lexical contrast that combines corpus statistics with the structure of a published thesaurus. Using ...
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Keyword:
affixes; antonymy; closest-to-opposite questions; crowdsourcing; distributional hypothesis; kinds of opposites; lexical contrast; thesaurus structure
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URL: https://nrc-publications.canada.ca/eng/view/object/?id=9994b07b-738c-4bcc-b884-98b4560e7566 https://dx.doi.org/10.4224/19040608
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Citation Handling for Improved Summarization of Scientific Documents
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Semantically-Informed Syntactic Machine Translation: A Tree-Grafting Approach ...
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The Circle of Meaning: From Translation to Paraphrasing and Back
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Semantically-Informed Syntactic Machine Translation: A Tree-Grafting Approach
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