Word embedding is a Natural Language Processing (NLP) technique that automatically maps words from a vocabulary to vectors of real numbers in an embedding space. It has been widely used in recent years to boost the performance of a variety of NLP tasks such as Named Entity Recognition, Syntactic Parsing and Sentiment Analysis. Classic word embedding methods such as Word2Vec and GloVe work well ...

For online product recommendation engines, learning highquality product embedding that captures various aspects of the product is critical to improving the accuracy of user rating prediction. In recent research, in conjunction with user feedback, the appearance of a product as side information has been shown to be helpful for learning product embedding. However, since a product has a variety of...

In this paper, we introduce a new setting for graph embedding, which considers embedding communities instead of individual nodes. Community embedding is useful as a natural community representation for applications, and it provides an exciting opportunity to improve community detection. Specifically, we see the interaction between community embedding and detection as a closed loop, through node...

‎For a given measure space $(X,{mathscr B},mu)$ we construct all measure spaces $(Y,{mathscr C},lambda)$ in which $(X,{mathscr B},mu)$ is embeddable‎. ‎The construction is modeled on the ultrafilter construction of the Stone--v{C}ech compactification of a completely regular topological space‎. ‎Under certain conditions the construction simplifies‎. ‎Examples are given when this simplification o...

Due to the rapidly growing volume of data on Internet, methods efficiently and accurately processing massive text information have been focus research. In natural language theory, sentence embedding representation is an important method. This paper proposes a new learning model called BRFP (Factorization Process with Bidirectional Restraints) that fuses syntactic information, uses matrix decomp...

Heterogeneous data may arise in many real-life applications under different scenarios. In this paper, we formulate a general framework to address the problem of modeling heterogeneous data. Our main contribution is a novel embedding method, called multiple kernel preserving embedding (MKPE), which projects heterogeneous data into a unified embedding space by preserving crossdomain interactions ...

Knowledge graph embedding aims to represent entities and relations in a large-scale knowledge graph as elements in a continuous vector space. Existing methods, e.g., TransE and TransH, learn embedding representation by defining a global margin-based loss function over the data. However, the optimal loss function is determined during experiments whose parameters are examined among a closed set o...

This study examined the use of neural word embeddings for clinical abbreviation disambiguation, a special case of word sense disambiguation (WSD). We investigated three different methods for deriving word embeddings from a large unlabeled clinical corpus: one existing method called Surrounding based embedding feature (SBE), and two newly developed methods: Left-Right surrounding based embedding...

In this paper a novel technique for Neighbor embedding single image super resolution (SR) is proposed. Given a low-resolution image, its high-resolution image is reconstructed from a set of training images, which are composed of one or more low-resolution and corresponding highresolution image pairs. In this paper we propose a new approach to a single image super-resolution through neighbor emb...

Embedding techniques represent a powerful advance in the development of experimental chaos. However there seems no universal method to find the best set of parameters to use. In this paper we present a new approach, an automated embedding method, to estimate a near optimum embedding dimension and delay time based on the Γ-test [Stefánsson 1997]. A strange attractor can be regarded as the union ...