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Found 5 papers, 3 papers with code
Efficient Sentence Embedding via Semantic Subspace Analysis
Given the fact that word embeddings can capture semantic relationship while semantically similar words tend to form semantic groups in a high-dimensional embedding space, we develop a sentence representation scheme by analyzing semantic subspaces of its constituent words.
Graph Representation Learning: A Survey
Research on graph representation learning has received a lot of attention in recent years since many data in real-world applications come in form of graphs.
Evaluating Word Embedding Models: Methods and Experimental Results
Extensive evaluation on a large number of word embedding models for language processing applications is conducted in this work.
Graph-based Deep-Tree Recursive Neural Network (DTRNN) for Text Classification
A novel graph-to-tree conversion mechanism called the deep-tree generation (DTG) algorithm is first proposed to predict text data represented by graphs.
Post-Processing of Word Representations via Variance Normalization and Dynamic Embedding
Although embedded vector representations of words offer impressive performance on many natural language processing (NLP) applications, the information of ordered input sequences is lost to some extent if only context-based samples are used in the training.
