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Can we predict new facts with open knowledge graph embeddings? A benchmark for open link prediction
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LibKGE – A knowledge graph embedding library for reproducible research
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On aligning OpenIE extractions with Knowledge Bases: A case study
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On evaluating embedding models for knowledge base completion
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A neural autoencoder approach for document ranking and query refinement in pharmacogenomic information retrieval
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Learning distributional token representations from visual features
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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.
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Keyword:
004 Informatik
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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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Methods for open information extraction and sense disambiguation on natural language text ; Methoden der Offenen Informationsextraktion und Bedeutungsdisambiguierung in Texten
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CORE: Context-aware open relation extraction with factorization machines
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Senti-LSSVM: Sentiment-oriented multi-relation extraction with latent structural SVM
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Werdy: Recognition and disambiguation of verbs and verb phrases with syntactic and semantic pruning
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