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Hits 121 – 140 of 922

121
Temporally-Informed Analysis of Named Entity Recognition ...
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122
Extracting Problem Linkages to Improve Knowledge Exchange between Science and Technology Domains using an Attention-based Language Model ...
Sasaki, H.; Yamamoto, S.; Agchbayar, A.. - : Zenodo, 2020
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123
Extracting Problem Linkages to Improve Knowledge Exchange between Science and Technology Domains using an Attention-based Language Model ...
Sasaki, H.; Yamamoto, S.; Agchbayar, A.. - : Zenodo, 2020
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124
Towards Olfactory Information Extraction from Text: A Case Study on Detecting Smell Experiences in Novels ...
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125
Towards Olfactory Information Extraction from Text: A Case Study on Detecting Smell Experiences in Novels ...
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126
Unsupervised Extraction of Workplace Rights and Duties from Collective Bargaining Agreements ...
Ash, Elliott; Jacobs, Jeff; MacLeod, Bentley. - : ETH Zurich, 2020
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127
Prerequisites for Extracting Entity Relations from Swedish Texts
Lenas, Erik. - : KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020
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128
Enhancing the Performance of Telugu Named Entity Recognition Using Gazetteer Features
In: Information ; Volume 11 ; Issue 2 (2020)
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129
A Review of Geospatial Semantic Information Modeling and Elicitation Approaches
In: ISPRS International Journal of Geo-Information ; Volume 9 ; Issue 3 (2020)
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130
Unsupervised Extraction of Workplace Rights and Duties from Collective Bargaining Agreements
In: 2020 International Conference on Data Mining Workshops (ICDMW) (2020)
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131
NAT: Noise-Aware Training for Robust Neural Sequence Labeling
In: Fraunhofer IAIS (2020)
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132
Using Probabilistic Soft Logic to Improve Information Extraction in the Legal Domain
In: Fraunhofer IAIS (2020)
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133
Domain-Independent Extraction of Scientific Concepts from Research Articles ...
Brack, Arthur; D'Souza, Jennifer; Hoppe, Anett. - : Cham : Springer, 2020
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134
Focus Particles and Extraction – An Experimental Investigation of German and English Focus Particles in Constructions with Leftward Association ...
Jäger, Marion. - : Universität Tübingen, 2020
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135
COMBINING TEXT EMBEDDING WITH ADDITIONAL KNOWLEDGE FOR INFORMATION EXTRACTION ...
Roy, Arpita. - : Maryland Shared Open Access Repository, 2020
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136
Evaluating semantic textual similarity in clinical sentences using deep learning and sentence embeddings
Antunes, Rui; Silva, João Figueira; Matos, Sérgio. - : Association for Computing Machinery, 2020
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137
Cold-start universal information extraction
Huang, Lifu. - 2020
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138
Receptive field transformations of the optimal HSNN predicts transformations observed along the ascending auditory pathway.
Fatemeh Khatami (4446235); Monty A. Escabí (5105276). - 2020
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139
Focus Particles and Extraction – An Experimental Investigation of German and English Focus Particles in Constructions with Leftward Association
Jäger, Marion. - : Universität Tübingen, 2020
Abstract: In my dissertation on leftward association of English and German focus particles, I investigate the following overall research questions: How strong is the c-command relation between focus particles and their associated focus? Is their relation fixed or are there factors which can license leftward association? In my study, I focus on the English particles "only" and "even" and the corresponding German particles "nur" and "sogar". These particles preferably c-command their associated focus constituent (as in "Only SAM will eat chocolate). It is controversial in the literature how strong this c-command relation is and whether leftward association of these particles is acceptable (as in "SAM will only eat chocolate"). To my knowledge, my study provides the first experimental investigation dealing with this phenomenon. In the analysis of examples from everyday language and in various acceptability judgment studies, I identified the following factors which license leftward association of the German particles under consideration: (i) prosody, (ii) speaker evaluation, and (iii) special emphasis. I conclude that the c-command relation between focus particles and their associated focus is strong but not fixed in such a way that leftward association is impossible, as there are factors which improve and license this construction. Moreover, German examples I collected from spontaneous speech provide evidence that leftward association of the German particles under consideration occurs in spoken language. I base my explanations of the data on theories dealing with emphatic syntactic constructions and on theories dealing with salience and cognitive prominence. I propose an account which combines information structure, pragmatics, and processing.
Keyword: 400; 420; 430; acceptability judgment studies; Akzeptabilitätsstudien; Alltagssprache; Ambiguität; ambiguity; cognitive prominence; context; Deutsch; Emphase; emphasis; Englisch; English; even; everyday language; Experimentelle Linguistik; extraction; focus; focus particles; Fokus; German; Gradpartikel; information structure; Informationsstruktur; Kontext; leftward association; linguistics; Linguistik; Linksassoziierung; nur; only; pragmatics; Pragmatik; Prosodie; prosody; sogar; speaker evaluation; Sprecherevaluation; Syntax
URL: http://hdl.handle.net/10900/97239
http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-972396
https://doi.org/10.15496/publikation-38622
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140
Deep Neural Architectures for End-to-End Relation Extraction
In: Theses and Dissertations--Computer Science (2020)
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