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Distant Reading Sentiments and Emotions in Historic German Plays
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83 |
Inter-Rater Agreement and Usability: A Comparative Evaluation of Annotation Tools for Sentiment Annotation
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Stilometrie in der Rechtslinguistik. Nutzung korpuslinguistischer Verfahren für die Analyse deutschsprachiger Urteile
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85 |
Sentiment Annotation for Lessing’s Plays: Towards a Language Resource for Sentiment Analysis on German Literary Texts
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86 |
HHMM at SemEval-2019 Task 2: Unsupervised frame induction using contextualized word embeddings
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87 |
Policy preference detection in parliamentary debate motions
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88 |
Exploiting background knowledge for argumentative relation classification
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89 |
Using Hidden Markov Models for the accurate linguistic analysis of process model activity labels
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91 |
On evaluating embedding models for knowledge base completion
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Abstract:
Knowledge graph embedding models have recently received significant attention in the literature. These models learn latent semantic representations for the entities and relations in a given knowledge base; the representations can be used to infer missing knowledge. In this paper, we study the question of how well recent embedding models perform for the task of knowledge base completion, i.e., the task of inferring new facts from an incomplete knowledge base. We argue that the entity ranking protocol, which is currently used to evaluate knowledge graph embedding models, is not suitable to answer this question since only a subset of the model predictions are evaluated. We propose an alternative entity-pair ranking protocol that considers all model predictions as a whole and is thus more suitable to the task. We conducted an experimental study on standard datasets and found that the performance of popular embeddings models was unsatisfactory under the new protocol, even on datasets that are generally considered to be too easy. Moreover, we found that a simple rule-based model often provided superior performance. Our findings suggest that there is a need for more research into embedding models as well as their training strategies for the task of knowledge base completion.
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Keyword:
004 Informatik
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URL: https://madoc.bib.uni-mannheim.de/52546/1/On%20Evaluating%20Embedding%20Models%20for%20Knowledge%20Base%20Completion.pdf https://madoc.bib.uni-mannheim.de/52546/ https://madoc.bib.uni-mannheim.de/52546
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92 |
Watset: Local-global graph clustering with applications in sense and frame induction
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93 |
SEAGLE: A platform for comparative evaluation of semantic encoders for information retrieval
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96 |
Specializing distributional vectors of all words for lexical entailment
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98 |
How to (properly) evaluate cross-lingual word embeddings: On strong baselines, comparative analyses, and some misconceptions
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99 |
A spreading activation framework for tracking conceptual complexity of texts
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Cross-lingual semantic specialization via lexical relation induction
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