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Enhancing Sequence-to-Sequence Neural Lemmatization with External Resources ...
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EstBERT: A Pretrained Language-Specific BERT for Estonian ...
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STransE: a novel embedding model of entities and relationships in knowledge bases ...
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Abstract:
Knowledge bases of real-world facts about entities and their relationships are useful resources for a variety of natural language processing tasks. However, because knowledge bases are typically incomplete, it is useful to be able to perform link prediction or knowledge base completion, i.e., predict whether a relationship not in the knowledge base is likely to be true. This paper combines insights from several previous link prediction models into a new embedding model STransE that represents each entity as a low-dimensional vector, and each relation by two matrices and a translation vector. STransE is a simple combination of the SE and TransE models, but it obtains better link prediction performance on two benchmark datasets than previous embedding models. Thus, STransE can serve as a new baseline for the more complex models in the link prediction task. ... : V1: In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2016. V2: Corrected citation to (Krompa{\ss} et al., 2015). V3: A revised version of our NAACL-HLT 2016 paper with additional experimental results and latest related work ...
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Keyword:
Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://arxiv.org/abs/1606.08140 https://dx.doi.org/10.48550/arxiv.1606.08140
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STransE : a novel embedding model of entities and relationships in knowledge bases
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Query-based single document summarization using an Ensemble Noisy Auto-Encoder
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POS induction with distributional and morphological information using a distance-dependent Chinese Restaurant Process
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Minimally-supervised morphological segmentation using adaptor grammars
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Noisy-channel spelling correction models for Estonian learner language corpus lemmatisation
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A Hierarchical dirichlet process model for joint part-of-speech and morphology induction
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Korpuste tükeldamine : rakendusi silpide ning allkeeltega ; Cutting the text corpora : applications with syllables and sub-languages
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Eesti silbisüsteemi struktuurist ; A preliminary structural view of the Estonian syllable system
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