[PDF] Top 20 Building Graph Representations of Deep Vector Embeddings
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Building Graph Representations of Deep Vector Embeddings
... and deep neural networks have been previously explored, but most con- tributions do so from a different ...a graph representation of the embedding produced by a CNN when processing an image, most related ... See full document
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Fusing Document, Collection and Label Graph based Representations with Word Embeddings for Text Classification
... the graph-based method for TC, rely on graph mining algorithms that are applied to extract frequent subgraphs, which are then used to produce feature vectors for classifi- cation (Deshpande et ...other ... See full document
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Building Compact Entity Embeddings Using Wikidata
... entity representations using their example occurrences in a large text corpus (Wikipedia) instead of a ...for building the entity representations using a large number of different contexts where an ... See full document
9
Deep Contextualized Word Embeddings in Transition Based and Graph Based Dependency Parsing A Tale of Two Parsers Revisited
... feature representations, most parsing models are still either transition-based (Chen and Manning, 2014; Dyer et ...or graph-based (Kiperwasser and Goldberg, 2016; Dozat and Manning, ...Similarly, ... See full document
14
Building Lexical Vector Representations from Concept Definitions
... of vector spaces, called embeddings. Good embeddings enable the use of vector operations on words, such as comparison by cosine similar- ... See full document
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Efficient Graph based Word Sense Induction by Distributional Inclusion Vector Embeddings
... word representations more ...efficient graph- based method for WSI that builds a global non-negative vector embedding basis (which are interpretable like topics) and clusters the basis indexes in the ... See full document
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Embedding Imputation with Grounded Language Information
... of embeddings as input representations for a wide range of natu- ral language tasks, imputation of embeddings for rare and unseen words is a critical problem in language ...learning ... See full document
6
Graph Embeddings for Frame Identification
... traditional graph-based methods for word simi- larity and prediction ...Word representations learned in these models, called embeddings, pro- vide the latent features of a word in context as ... See full document
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Node Embeddings for Graph Merging: Case of Knowledge Graph Construction
... node embeddings is through word ...for deep contextualized word representation (Peters et ...graph building. In other words, given a document we generate ELMo embeddings for every ... See full document
5
Making Fast Graph based Algorithms with Graph Metric Embeddings
... original graph-based metrics to Ham- ming distance between 128D FSE binary embed- dings (Subercaze et ...large graph. Also, low- dimensional vector representations of nodes take much less ... See full document
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A Comparison of Context sensitive Models for Lexical Substitution
... compare representations that model context in different ways: they exploit context embeddings generated within the skip-gram model (Melamud et ...a deep bidirectional language model (biLM) (Peters et ... See full document
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How Much Topological Structure Is Preserved by Graph Embeddings?
... Abstract. Graph embedding aims at learning representations of nodes in a low di- mensional vector ...Good embeddings should preserve the graph topological ...the graph can be ... See full document
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Cross Lingual Word Representations via Spectral Graph Embeddings
... of vector representations is K = 40, 100, or ...two vector representations are measured by the unweighted cosine similar- ity in CL-LSI and ... See full document
6
Domain Adaptation with Adversarial Training and Graph Embeddings
... the deep learning paradigm, Glo- rot et ...domain-invariant representations, with domain adaptation as a ...semi-supervised graph embedding for unsupervised domain ... See full document
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Improving Neural Entity Disambiguation with Graph Embeddings
... For future work, we plan to examine graph em- beddings on other relationships, e.g. taxonomic or otherwise typed relations such as works-for, married-with, and so on, generalizing the notion to arbitrary ... See full document
8
CaRe: Open Knowledge Graph Embeddings
... adapts graph convo- lutional network (GCN) (Kipf and Welling, 2016) to a relational graph proposing an auto-encoder model for the link prediction ...the graph is a different entity, and distinct edge ... See full document
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Leveraging Social Network Data to Alleviate Cold-Start Problem in Recommender Systems
... Word representations or embeddings learned to use neural language models help addressing the problem of traditional bag-of-word approaches which fail to capture words‟ contextual ...word embeddings, ... See full document
8
Opinion Mining with Deep Contextualized Embeddings
... We stacked BiLSTM on top of embedders be- cause of the following reasons. First of all, in our research, BERT and ELMo are only used as word embedders instead of the whole architecture. Second, many RNN-based neural ... See full document
8
Semantic Word Clusters Using Signed Spectral Clustering
... space representations of words cap- ture many aspects of word similarity, but such methods tend to produce vector spaces in which antonyms (as well as syn- onyms) are close to each ...a vector space ... See full document
11
KBGAN: Adversarial Learning for Knowledge Graph Embeddings
... However, even steady progress has been made in developing novel algorithms for knowledge graph embedding, there is still a common chal- lenge in this line of research. For space effi- ciency, common knowledge ... See full document
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