Joint Learning of Words and Meaning Representations for Open-Text Semantic Parsing

By Antoine Bordes, Xavier Glorot, Jason Aaron Edward Weston, Yoshua Bengio. 2012

381 citations recorded by the source corpus.

Subfields: Representation & Self-Supervised Learning, Information Extraction

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Citations in the corpus

Ask Me Anything: Dynamic Memory Networks for Natural Language Processing (2015, background): “, 2007), some use neural networks, dependency trees and KBs (Bordes et al., 2012), others only sentences (Iyyer et al.”
Dynamic Memory Networks for Visual and Textual Question Answering (2016, background): “Neural approaches can include reasoning over knowledge bases, (Bordes et al., 2012; Socher et al., 2013a) or directly via sentences for trivia competitions (Iyyer et al., 2014).”
Learning Entity and Relation Embeddings for Knowledge Graph Completion (2015, methodology): “SME model (Bordes et al. 2012; 2014) aims to capture correlations between entities and relations via multiple matrix products and Hadamard product.”
Neural-Symbolic Computing: An Effective Methodology for Principled Integration of Machine Learning and Reasoning (2019, background): “Technically, this has been done by searching for the answer to a query that gives the highest grounding score [4, 3, 47, 48].”
Connecting Language and Knowledge Bases with Embedding Models for Relation Extraction (2013, methodology): “in (Nickel et al., 2011; Bordes et al., 2011; Bordes et al., 2012)): we chose in this work to use the approach of (Bordes et al.”
A semantic matching energy function for learning with multi-relational data (2013, background): “Note that this paper extends a shorter version (Bordes et al. 2012), which first introduced the model.”
Practical Recommendations for Gradient-Based Training of Deep Architectures (2012, background): “The idea proposed in Bordes et al. (2011, 2012) is to learn an energy function that is lower for positive (valid) relations present in the training set, and parametrized in two parts: on the one hand the symbol embeddings and on the other hand the rest of the neural network that maps them to a…”
Open Question Answering with Weakly Supervised Embedding Models (2014, background): “Recently, some embedding models have been proposed to perform a connection between nat-ural language and KBs for word-sense disambiguation [5] and for information extraction [24].”
Knowledge Graph Embedding via Dynamic Mapping Matrix (2015, methodology): “2012; 2014) O(Nem + Nrn + 4mk + 4k)(m = n) O(4mkNt) SME (bilinear) (Bordes et al. 2012; 2014) O(Nem + Nrn + 4mks + 4k)(m = n) O(4mksNt) LFM (Jenatton et al.”
Evolutionary Multitasking via Explicit Autoencoding (2019, methodology): “has been successfully applied for solving many challenging learning problems, such as image classification [32], natural language processing [33], transfer learning [34], [35],”
Benchmark of Deep Learning Models on Large Healthcare MIMIC Datasets (2017, background): “Deep Learning models have yielded outstanding results in several applications, including speech recognition [47, 48], computer vision [49, 50, 51], and natural language processing [52, 53, 54, 55].”
Embedding Words and Senses Together via Joint Knowledge-Enhanced Training (2016, neutral): “f words and senses for obtaining a Most Common Sense (MCS) baseline. The MCS baseline is generally integrated into the pipeline of state-of-the-art WSD and Entity Linking systems (Zhong and Ng, 2010; Bordes et al., 2012; Moro et al., 2014) as a back-off strategy. Therefore, a system which automatica”
Deep learning for visual understanding (2017, positive): “It is an emerging approach and has been widely applied in traditional arti cial intelligence domains, such as semantic parsing [11], natural language processing [12], computer vision [13, 14] and many more.”
From One Point to a Manifold: Knowledge Graph Embedding for Precise Link Prediction (2015, methodology): “A variety of methods have been proposed for this task, including TransE [Bordes et al. , 2013], PTransE [Lin et al. , 2015a] and KG2E [He et al. , 2015].”
Learning New Facts From Knowledge Bases With Neural Tensor Networks and Semantic Word Vectors (2013, methodology): “[8].”