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1
Can we predict new facts with open knowledge graph embeddings? A benchmark for open link prediction
Gashteovski, Kiril; Gemulla, Rainer; Wang, Yanjie. - : Association for Computational Linguistics, 2020
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2
LibKGE – A knowledge graph embedding library for reproducible research
Broscheit, Samuel; Ruffinelli, Daniel; Kochsiek, Adrian. - : Association for Computational Linguistics (ACL), 2020
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3
On aligning OpenIE extractions with Knowledge Bases: A case study
Gashteovski, Kiril; Gemulla, Rainer; Kotnis, Bhushan. - : Association for Computational Linguistics (ACL), 2020
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4
OPIEC: An open information extraction corpus
Gashteovski, Kiril [Verfasser]; Wanner, Sebastian [Verfasser]; Hertling, Sven [Verfasser]. - Mannheim : Universitätsbibliothek Mannheim, 2019
DNB Subject Category Language
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5
OPIEC: An open information extraction corpus
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6
On evaluating embedding models for knowledge base completion
Gemulla, Rainer; Wang, Yanjie; Broscheit, Samuel. - : Association for Computational Linguistics, 2019
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7
A neural autoencoder approach for document ranking and query refinement in pharmacogenomic information retrieval
Broscheit, Samuel; Pfeiffer, Jonas; Gemulla, Rainer. - : Association for Computational Linguistics, 2018
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8
Learning distributional token representations from visual features
Gemulla, Rainer; Broscheit, Samuel; Keuper, Margret. - : Association for Computational Linguistics, 2018
Abstract: In this study, we compare token representations constructed from visual features (i.e., pixels) with standard lookup-based embeddings. Our goal is to gain insight about the challenges of encoding a text representation from low-level features, e.g. from characters or pixels. We focus on Chinese, which—as a logographic language—has properties that make a representation via visual features challenging and interesting. To train and evaluate different models for the token representation, we chose the task of character-based neural machine translation (NMT) from Chinese to English. We found that a token representation computed only from visual features can achieve competitive results to lookup embeddings. However, we also show different strengths and weaknesses in the models’ performance in a part-of- speech tagging task and also a semantic similarity task. In summary, we show that it is possible to achieve a text representation only from pixels. We hope that this is a useful stepping stone for future studies that exclusively rely on visual input, or aim at exploiting visual features of written language.
Keyword: 004 Informatik
URL: https://madoc.bib.uni-mannheim.de/45649/1/Learning%20Distributional%20Token%20Representations%20from%20Visual%20Features.pdf
https://madoc.bib.uni-mannheim.de/45649
https://madoc.bib.uni-mannheim.de/45649/
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9
MinIE: minimizing facts in open information extraction
Corro, Luciano del; Gashteovski, Kiril; Gemulla, Rainer. - : Association for Computational Linguistics, 2017
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10
Methods for open information extraction and sense disambiguation on natural language text ; Methoden der Offenen Informationsextraktion und Bedeutungsdisambiguierung in Texten
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11
FINET: context-aware fine-grained named entity typing
Abujabal, Abdalghani; Corro, Luciano del; Gemulla, Rainer. - : Assoc. for Computational Linguistics, 2015
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12
CORE: Context-aware open relation extraction with factorization machines
Corro, Luciano del; Petroni, Fabio; Gemulla, Rainer. - : Assoc. for Computational Linguistics, 2015
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13
Werdy: Recognition and disambiguation of verbs and verb phrases with syntactic and semantic pruning
Corro, Luciano del [Verfasser]; Gemulla, Rainer [Verfasser]; Weikum, Gerhard [Verfasser]. - Mannheim : Universitätsbibliothek Mannheim, 2014
DNB Subject Category Language
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14
Senti-LSSVM: Sentiment-oriented multi-relation extraction with latent structural SVM
Weikum, Gerhard; Gemulla, Rainer; Zhang, Yi. - : Assoc. for Computational Linguistics, 2014
BASE
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15
Werdy: Recognition and disambiguation of verbs and verb phrases with syntactic and semantic pruning
Corro, Luciano del; Gemulla, Rainer; Weikum, Gerhard. - : Assoc. for Computational Linguistics, 2014
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