This paper presents a theoretical analysis of multi-view embedding – feature embedding that can be learned from unlabeled data through the task of predicting one view from another. We prove its usefulness in supervised learning under certain conditions. The result explains the effectiveness of some existing methods such as word embedding. Based on this theory, we propose a new semi-supervised l...

Abstract To accelerate the performance estimation in neural architecture search, recently proposed algorithms adopt surrogate models to predict of architectures instead training network from scratch. However, it is time-consuming collect sufficient labeled for model training. enhance capability using a small amount data, we propose surrogate-assisted evolutionary algorithm with embedding search...

Recently, the identification of human text and ChatGPT-generated has become a hot research topic. The current study presents Tunicate Swarm Algorithm with Long Short-Term Memory Recurrent Neural Network (TSA-LSTMRNN) model to detect both as well text. purpose proposed TSA-LSTMRNN method is investigate model’s decision presence any particular pattern. In addition this, technique focuses on desig...

Distributed representations of textual elements in low dimensional vector space to capture context has gained great attention recently. Current state-of-the-art word embedding techniques compute distributed representations using co-occurrences of words within a contextual window discounting the flexibility to incorporate other contextual phenomena like temporal, geographical, and topical contex...

Document-based Question Answering system, which needs to match semantically the short text pairs, has gradually become an important topic in the fields of natural language processing and information retrieval. Question Answering system based on English corpus has developed rapidly with the utilization of the deep learning technology, whereas an effective Chinese-customized system needs to be pa...

Word embedding algorithms like word2vec (Mikolov et al., 2013) have enabled advances in topic modelling by training shallow neural networks on the co-occurrence of words in corpuses of sentences. However, it is not clear how this process reflects human cognition. This poster will compare the results of document classification using the word2vec skipgram model and the 20k sensorimotor word norms...

In recent times, the trend of online shopping through e-commerce stores and websites has grown to a huge extent. Whenever product is purchased on an platform, people leave their reviews about product. These are very helpful for store owners product’s manufacturers betterment work process as well quality. An automated system proposed in this that operates two datasets D1 D2 obtained from Amazon....

Word spotting is to make searchable unindexed image documents by locating word/words in a doc-ument image, given a query word. This problem is challenging, mainly due to the large numberof word classes with very small inter-class and substantial intra-class distances. In this paper, asegmentation-based word spotting method is presented for multi-writer Persian handwritten doc-...

Deep neural network (DNN) based natural language processing models rely on a word embedding matrix to transform raw words into vectors. Recently, a deep structured semantic model (DSSM) has been proposed to project raw text to a continuously-valued vector for Web Search. In this technical report, we propose learning word embedding using DSSM. We show that the DSSM trained on large body of text ...

In network analysis, a community is typically considered of as a group of nodes with a great density of edges among themselves and a low density of edges relative to other network parts. Detecting a community structure is important in any network analysis task, especially for revealing patterns between specified nodes. There is a variety of approaches presented in the literature for overlapping...