[PDF] Top 20 KBGAN: Adversarial Learning for Knowledge Graph Embeddings
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KBGAN: Adversarial Learning for Knowledge Graph Embeddings
... and optimize it with gradient-based algorithms. Policy Gradient Theorem arises from reinforce- ment learning (RL), so we would like to draw an analogy between our model and an RL model. The generator can be viewed ... See full document
11
Learning Semantic Word Embeddings based on Ordinal Knowledge Constraints
... semantic knowledge into the corpus-based learning of word ...tic knowledge as many word ordinal ranking in- ...tic knowledge can all be represented as a num- ber of such ranking inequalities, ... See full document
11
Making Fast Graph based Algorithms with Graph Metric Embeddings
... for learning graph ...the graph struc- ture, our method takes structural measures of pairwise node similarities into account and learns dense node representations reflecting user-defined graph ... See full document
7
Long tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks
... To evaluate the performance of our proposed model, we compare the precision-recall curves of our model with various previous RE models. The evaluation results are shown in Figure 4 and Figure 5. We report the results of ... See full document
10
Sparsity and Noise: Where Knowledge Graph Embeddings Fall Short
... sophisticated learning methods to represent entities and re- ...learn embeddings, we used the public implementations of Lin et ...these embeddings meth- ...of embeddings on the extracted ... See full document
6
Graph Embeddings for Frame Identification
... large knowledge base such as WordNet (Miller, 1995) or taxonomies built from Wikipedia links (Cucerzan, 2007) to uncover relationships be- tween the nodes in the knowledge ...in learning word ... See full document
10
Towards Lexical Chains for Knowledge-Graph-based Word Embeddings
... (the graph nodes) and of different types of relations between them (the graph arcs; some relation types are antonymy, hypernymy, deriva- tion, ...WordNet. Learning is first performed on each of the ... See full document
7
Learning Attention based Embeddings for Relation Prediction in Knowledge Graphs
... cumulates knowledge from distant neighbors of an ...entity embeddings after every generalized GAT layer and prior to the first layer, for every main ... See full document
14
Compositional Learning of Embeddings for Relation Paths in Knowledge Base and Text
... our knowledge base, which was created by editors from the Nature Publish- ing Groups, in collaboration with the National Cancer ...a graph with 2774 genes and 14323 ... See full document
11
CaRe: Open Knowledge Graph Embeddings
... ding has been an active area of research (Bor- des et al., 2013; Yang et al., 2014), all the pro- posed KG embedding methods have focused on embedding Ontological KGs, such as WikiData (Vrandeˇci´c and Kr¨otzsch, 2014), ... See full document
11
Recognizing Mentions of Adverse Drug Reaction in Social Media Using Knowledge Infused Recurrent Models
... edge graph embeddings of DBpedia, and show the resulting model to be highly accurate ...active learning technique and using purpose built annotation tools, we can train the RNN to perform well ... See full document
10
Towards Understanding the Geometry of Knowledge Graph Embeddings
... of learning embeddings for Knowledge Graphs has received significant atten- tion in recent years, with several methods being proposed (Bordes et ... See full document
10
Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings
... tively. Each knowledge base includes a list of items, where each item has a value for each at- tribute. For example, in the MutualFriends set- ting, Figure 1, items are friends and attributes are name, school, ... See full document
11
DIVINE: A Generative Adversarial Imitation Learning Framework for Knowledge Graph Reasoning
... Knowledge graphs (Suchanek et al., 2007; Auer et al., 2007; Bollacker et al., 2008; Carlson et al., 2010; Vrandeˇci´c and Kr¨otzsch, 2014), typically composed of massive relational triples, are useful resources ... See full document
10
Collaborative Policy Learning for Open Knowledge Graph Reasoning
... ding methods aim to unite text corpus and KG. Contrary to our focus, they mainly utilize KGs for better performances of other tasks. (Toutanova et al., 2015) focuses on fact-extraction on the cor- pus labeled via ... See full document
10
Domain Adaptation with Adversarial Training and Graph Embeddings
... a graph-based inductive deep learning approach proposed by Yang et ...deep learning model by predicting contextual ...the graph by computing the distance be- tween tweets based on word ...the ... See full document
11
Graph Convolution for Multimodal Information Extraction from Visually Rich Documents
... the graph convolution layers and BiLSTM-CRF extractors are trained ...task learning in Section 5.4.3. We feed the graph embedding of each text segment into a sigmoid classifier to predict the ...the ... See full document
8
Adversarial Label Learning
... ALL trains using weak supervision and aims to mitigate these problems by adversarially labeling the data. The adver- sarial labeling can construct scenarios where dependencies in the weak supervision are as confounding ... See full document
8
DUT NLP at MEDIQA 2019: An Adversarial Multi Task Network to Jointly Model Recognizing Question Entailment and Question Answering
... Multi-Task Learning (MTL) is a learning paradigm in machine learning and its aim is to leverage shared representations contained in multiple related tasks to help improve the generalization ... See full document
9
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
... Abstract: There is a chasm between an NLP technology that works well in the research lab and something that works for applications that real people use. Research conditions are often theoret- ical or idealized. The first ... See full document
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