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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 8 of 8 1 Event Structure In Vision And Language Hafri, Alon In: Publicly Accessible Penn Dissertations (2019) BASE Show details 2 The Dynamic Role of Subphonemic Cues in Speech Perception: Investigating Coarticulatory Processing Across Sound Classes Arbour, Jessica. - 2012 BASE Show details 3 Receptive Vocabulary Knowledge in Low-Functioning Autism as Assessed by Eye Movements, Pupillary Dilation, and Event-Related Potentials Gordon, Barry In: DTIC (2011) BASE Show details 4 Improving Information Extraction and Translation Using Component Interactions Ji, Heng In: DTIC (2008) BASE Show details 5 Entropy Based Classifier Combination for Sentence Segmentation Magimai-Doss, M.; Hakkani-Tur, D.; Cetin, O.... In: DTIC (2007) BASE Show details 6 The ICSI+ Multilingual Sentence Segmentation System Zimmerman, M.; Hakkani-Tuer, D.; Fung, J.... In: DTIC (2006) BASE Show details 7 Not every pseudoword disrupts word recognition: an ERP study Friedrich, Claudia K.; Eulitz, Carsten; Lahiri, Aditi. - 2006 BASE Show details 8 Discriminative Slot Detection Using Kernel Methods Zhao, Shubin; Meyers, Adam; Grishman, Ralph In: DTIC (2004) Abstract: Most traditional information extraction approaches are generative models that assume events exist in text in certain patterns and these patterns can be regenerated in various ways. These assumptions limited the syntactic clues being considered for finding an event and confined these approaches to a particular syntactic level. This paper presents a discriminative framework based on kernel SVMs that takes into account different levels of syntactic information and automatically identifies the appropriate clues. Kernels are used to represent certain levels of syntactic structure and can be combined in principled ways as input for an SVM. We will show that by combining a low level sequence kernel with a high level kernel on a GLARF dependency graph, the new approach outperformed a good rule-based system on slot filler detection for MUC-6. ; Presented at the International Conference on Computational Linguistics (20th), held in Geneva Switzerland on 23-27 August, 2004. Pub. in the Proceedings of the International Conference on Computational Linguistics (20th), p757-763, 2004. Sponsored in part by DARPA and NSF grant ITS-0325657. Keyword: *DISCRIMINANT ANALYSIS; *DISCRIMINATIVE SLOT DETECTION; *INFORMATION RETRIEVAL; *KERNEL FUNCTIONS; *MATHEMATICAL MODELS; *RULE BASED SYSTEMS; Cybernetics; DISCRIMINATIVE CLASSIFIERS; EOD(EVENT OCCURRENCE DETECTION); GLARF(GRAMMATICAL AND LOGICAL ARGUMENT REGULARIZATION FRAMEWORK); GRAPHS; IE(INFORMATION EXTRACTION); Information Science; Linguistics; MESSAGE UNDERSTANDING; NES(NAMED ENTITIES); PATTERN RECOGNITION; SEQUENCES; SFD(SLOT FILLER DETECTION); SVM(SUPPORT VECTOR MACHINE); SYMPOSIA; SYNTAX; TEXT PROCESSING URL: http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA460647 http://www.dtic.mil/docs/citations/ADA460647 BASE Hide details

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