WebI am a Research Engineer at New York University, Abu Dhabi, working on online misinformation detection. Before that, I was an MS by Research student at Complex Network Research Group (CNeRG), Department of Computer Science & Engineering, IIT Kharagpur India. I am broadly interested in NLP and Graph representation learning. In … WebIt provides a brief introduction to deep learning methods on non-Euclidean domains such as graphs and justifies their relevance in NLP. It then covers recent advances in applying graph-based deep learning methods for …
Graph Learning and Network Science for Natural Language …
Dec 28, 2024 · WebOct 3, 2024 · The solution starts from a graph-based unsupervised technique called TextRank [1]. Thereafter, the quality of extracted keywords is greatly improved using a typed dependency graph that is used to filter out meaningless phrases, or to extend keywords with adjectives and nouns to better describe the text. It is worth noting here that the proposed ... packing job responsibilities
Graph-based Deep Learning in Natural Language …
WebJul 10, 2024 · Graphs have always formed an essential part of NLP applications ranging from syntax-based Machine Translation, knowledge graph-based question answering, abstract meaning representation for … WebMar 4, 2024 · 1. Background. Lets start with the two keywords, Transformers and Graphs, for a background. Transformers. Transformers [1] based neural networks are the most successful architectures for representation learning in Natural Language Processing (NLP) overcoming the bottlenecks of Recurrent Neural Networks (RNNs) caused by the … WebJun 22, 2024 · Network Science by Albert-László Barabási is a comprehensive, freely available textbook. It can be used as a reference work to look up the gritty nitty details of … packing job from home