Jumping NLP Curves: A Review of Natural Language Processing Research [Review Article]

By Erik Cambria, Bebo White. 2014

1,104 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

A Survey of Deep Network Solutions for Learning Control in Robotics: From Reinforcement to Imitation (2016, neutral): “ example image recognition (He et al., 2016), object detection (Ren et al., 2015), semantics segmentation (Chen et al., 2016a), audio recognition (Deng et al., 2013), and natural language processing (Cambria and White, 2014). Deep-learning is capable of extracting multi-level features from raw senso”
Recent Trends in Deep Learning Based Natural Language Processing (2017, background): “NLP research has evolved from the era of punch cards and batch processing, in which the analysis of a sentence could take up to 7 minutes, to the era of Google and the likes of it, in which millions of webpages can be processed in less than a second [1].”
Evaluating Large Language Models in Generating Synthetic HCI Research Data: a Case Study (2023, background): “Language modeling and generation has a long history in AI and computational creativity research [12, 44, 70].”
A review of natural language processing techniques for opinion mining systems (2017, background): “Cambria and White [42] focused on the evolution of NLP, including the historical background of NLP, syntax and semantics technologies and others.”
Text‐based emotion detection: Advances, challenges, and opportunities (2020, neutral): “Their approach was made up of two(2) phases, that is, the encoder and classification phases.”
SenticNet 4: A Semantic Resource for Sentiment Analysis Based on Conceptual Primitives (2016, methodology): “Statistical methods, however, are generally semantically weak (Cambria and White, 2014).”
Deep-learning in Mobile Robotics - from Perception to Control Systems: A Survey on Why and Why not (2016, background): “…almost all of contests in machine learning and pattern recognition, for example image recognition (He et al., 2016), object detection (Ren et al., 2015), semantics segmentation (Chen et al., 2016a), audio recognition (Deng et al., 2013), and natural language processing (Cambria and White, 2014).”
Transforming the communication between citizens and government through AI-guided chatbots (2019, background): “…ones; (ii) recognition and identification of (named) entities in the selected documents (Augenstein, Derczynski, & Bontcheva, 2017); and (iii) identification and extraction of relations based on natural language processing (NLP) - both syntactic and semantic – techniques (Cambria & White, 2014).”
Artificial Intelligence for Mental Health and Mental Illnesses: An Overview (2019, background): “The ability of a computer algorithm to automatically understand meanings of underlying words, despite the generativity of human language, is a huge advancement in technology and essential for mental healthcare applications [52].”
Natural language based financial forecasting: a survey (2017, background): “Stepping back for a holistic view, we are at the dawn of the semantics curve of NLP technologies [21].”