His primary areas of investigation include Artificial intelligence, Natural language processing, Word, Artificial neural network and Machine learning. As part of his studies on Artificial intelligence, Richard Socher often connects relevant areas like Pattern recognition. His Natural language processing research incorporates themes from SemEval and Principle of compositionality.
Richard Socher combines subjects such as Word2vec, Word embedding, Vocabulary mismatch, Named-entity recognition and Sememe with his study of SemEval. His biological study spans a wide range of topics, including Context and Representation. His Context study combines topics in areas such as Semantics, Distributional semantics, Word lists by frequency and Sequence labeling.
Richard Socher mainly investigates Artificial intelligence, Natural language processing, Machine learning, Artificial neural network and Language model. His Artificial intelligence research incorporates themes from Context and Pattern recognition. His research related to Natural language, Parsing, Treebank, Sentence and Sentiment analysis might be considered part of Natural language processing.
His Natural language research focuses on SQL and how it connects with Relational database. Machine learning is closely attributed to Inference in his research. He has researched Word in several fields, including Speech recognition, Representation and Machine translation.
His scientific interests lie mostly in Artificial intelligence, Natural language processing, Language model, Machine learning and Artificial neural network. Richard Socher integrates many fields, such as Artificial intelligence and Code, in his works. Richard Socher mostly deals with Parsing in his studies of Natural language processing.
As a part of the same scientific family, he mostly works in the field of Parsing, focusing on SQL and, on occasion, Relational database. Within one scientific family, Richard Socher focuses on topics pertaining to Task oriented under Language model, and may sometimes address concerns connected to Natural language understanding and Selection. He interconnects Linguistic discrimination, Morphology, Standard English and Singapore English in the investigation of issues within Artificial neural network.
Richard Socher spends much of his time researching Artificial intelligence, Language model, Code, Natural language processing and Machine learning. His research integrates issues of Space and Detector in his study of Artificial intelligence. The Language model study combines topics in areas such as Inductive bias, Task oriented and SQL.
His work carried out in the field of Natural language processing brings together such families of science as Scalability, Source document, Consistency, Consistency model and Selection. His biological study focuses on Leverage. Richard Socher combines subjects such as Latent variable, Minimum bounding box, Feature learning and Cluster analysis with his study of Embedding.
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ImageNet: A large-scale hierarchical image database
Jia Deng;Wei Dong;Richard Socher;Li-Jia Li.
computer vision and pattern recognition (2009)
Glove: Global Vectors for Word Representation
Jeffrey Pennington;Richard Socher;Christopher Manning.
empirical methods in natural language processing (2014)
Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank
Richard Socher;Alex Perelygin;Jean Wu;Jason Chuang.
empirical methods in natural language processing (2013)
Parsing Natural Scenes and Natural Language with Recursive Neural Networks
Richard Socher;Cliff C. Lin;Chris Manning;Andrew Y. Ng.
international conference on machine learning (2011)
Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions
Richard Socher;Jeffrey Pennington;Eric H. Huang;Andrew Y. Ng.
empirical methods in natural language processing (2011)
Reasoning With Neural Tensor Networks for Knowledge Base Completion
Richard Socher;Danqi Chen;Christopher D Manning;Andrew Ng.
neural information processing systems (2013)
Semantic Compositionality through Recursive Matrix-Vector Spaces
Richard Socher;Brody Huval;Christopher D. Manning;Andrew Y. Ng.
empirical methods in natural language processing (2012)
Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks
Kai Sheng Tai;Richard Socher;Christopher D. Manning.
arXiv: Computation and Language (2015)
Improving Word Representations via Global Context and Multiple Word Prototypes
Eric Huang;Richard Socher;Christopher Manning;Andrew Ng.
meeting of the association for computational linguistics (2012)
A Deep Reinforced Model for Abstractive Summarization
Romain Paulus;Caiming Xiong;Richard Socher.
arXiv: Computation and Language (2017)
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