Attention Selectively Reshapes the Geometry of Distributed Semantic Representation.

Effect of Historical Buildings Representation in Cyberspace in Creating Tourists’ Destination Image (Qualitative Study of Traditional Accommodations in Kashan)

Introduction: Understanding the representation components of the historical buildings in cyberspace and their impact on the mental image of the tourists is a significant fact in tourism recognition and management. A part of this subject is the impact of place representation on the destination image of the tourist. In this research, the destination is traditional accommodations that attract tour...

Neuron Mathematical Model Representation of Neural Tensor Network for RDF Knowledge Base Completion

In this paper, a state-of-the-art neuron mathematical model of neural tensor network (NTN) is proposed to RDF knowledge base completion problem. One of the difficulties with the parameter of the network is that representation of its neuron mathematical model is not possible. For this reason, a new representation of this network is suggested that solves this difficulty. In the representation, th...

The Effect of Semantic Transfer on Iranian EFL Learners’ Lexical Representation and Processing

A Joint Semantic Vector Representation Model for Text Clustering and Classification

Text clustering and classification are two main tasks of text mining. Feature selection plays the key role in the quality of the clustering and classification results. Although word-based features such as term frequency-inverse document frequency (TF-IDF) vectors have been widely used in different applications, their shortcoming in capturing semantic concepts of text motivated researches to use...

Image Classification via Sparse Representation and Subspace Alignment

Image representation is a crucial problem in image processing where there exist many low-level representations of image, i.e., SIFT, HOG and so on. But there is a missing link across low-level and high-level semantic representations. In fact, traditional machine learning approaches, e.g., non-negative matrix factorization, sparse representation and principle component analysis are employed to d...