41 |
Acquisition and Analysis of a Meme Corpus to Investigate Web Culture
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42 |
Distant Reading of Religious Online Communities: A Case Study for Three Religious Forums on Reddit
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43 |
Live Sentiment Annotation of Movies via Arduino and a Slider
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44 |
Towards an Analysis of Gender in Video Game Culture: Exploring Gender specific Vocabulary in Video Game Magazines
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45 |
Der Einsatz von Distant Reading auf einem Korpus deutschsprachiger Songtexte
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46 |
Investigating the Transformation of Original Work by the Online Fan Fiction Community: A Case Study for Supernatural
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47 |
Can we predict new facts with open knowledge graph embeddings? A benchmark for open link prediction
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Abstract:
Open Information Extraction systems extract(“subject text”, “relation text”, “object text”)triples from raw text. Some triples are textualversions of facts, i.e., non-canonicalized men-tions of entities and relations. In this paper, weinvestigate whether it is possible to infernewfacts directly from theopen knowledge graphwithout any canonicalization or any supervi-sion from curated knowledge. For this pur-pose, we propose the open link prediction task,i.e., predicting test facts by completing(“sub-ject text”, “relation text”, ?)questions. Anevaluation in such a setup raises the question ifa correct prediction is actually anewfact thatwas induced by reasoning over the open knowl-edge graph or if it can be trivially explained.For example, facts can appear in different para-phrased textual variants, which can lead to testleakage. To this end, we propose an evaluationprotocol and a methodology for creating theopen link prediction benchmark OLPBENCH.We performed experiments with a prototypicalknowledge graph embedding model for openlink prediction. While the task is very chal-lenging, our results suggests that it is possibleto predict genuinely new facts, which can notbe trivially explained.
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Keyword:
004 Informatik
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URL: https://madoc.bib.uni-mannheim.de/55724/1/Can%20We%20Predict%20New%20Facts%20with%20Open%20Knowledge%20Graph%20Embeddings%20A%20Benchmark%20for%20Open%20Link%20Prediction.pdf https://madoc.bib.uni-mannheim.de/55724/ https://madoc.bib.uni-mannheim.de/55724
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48 |
Treebanking user-generated content: A proposal for a unified representation in universal dependencies
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49 |
Unsupervised stance detection for arguments from consequences
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52 |
Specializing unsupervised pretraining models for word-level semantic similarity
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53 |
Non-linear instance-based cross-lingual mapping for non-isomorphic embedding spaces
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54 |
Classification-based self-learning for weakly supervised bilingual lexicon induction
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56 |
LibKGE – A knowledge graph embedding library for reproducible research
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57 |
AraWEAT: Multidimensional analysis of biases in Arabic word embeddings
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59 |
Rezension zu: Michael Beißwenger / Steffen Pappert: Handeln mit Emojis. Grundriss einer Linguistik kleiner Bildzeichen in der WhatsApp-Kommunikation. Duisburg: UVRR 2019
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60 |
Common sense or world knowledge? Investigating adapter-based knowledge injection into pretrained transformers
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