Advances on natural language processing

By R. Muñoz, A. Montoyo. 2007

614 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Natural Language Processing

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

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In Natural Language Processing

Citations in the corpus

Experience Grounds Language (2020, background): “toward a science (Steedman, 2008), and its major challenges (Hirschberg and Manning, 2015; McClelland et al., 2019), we hope to provide momentum for a direction many are already heading.”
Local Aggregation for Unsupervised Learning of Visual Embeddings (2019, background): “Deep convolutional neural networks (DCNNs) have achieved great success on many tasks across a variety of domains, such as vision [41, 64, 26, 25, 8], audition [29, 23, 12, 51], and natural language processing [73, 32, 10, 42].”
Natural language processing in mental health applications using non-clinical texts† (2017, background): “For example, a recent special issue of Science Magazine dedicated to progress in AI had several mentions to this area (Bohannon 2015; Hirschberg and Manning 2015; Horvitz and Mulligan 2015).”
Computational Grounded Theory: A Methodological Framework (2020, methodology): “…F. Brown et al. 1993; Hinton et al. 2012), are using computer-assisted text analysis techniques to bolster, and in some cases completely replace, traditional content analysis methods in their disciplines (see Hirschberg and Manning 2015, for a summary of recent developments in this larger field).”
ChartDialogs: Plotting from Natural Language Instructions (2020, background): “Advances in machine language understanding (Hirschberg and Manning, 2015) have sparked interest in using artiﬁcial intelligence to address difﬁ-cult problems involving language.”
Deep Reinforcement Learning: An Overview (2017, background): “1 We refer readers to materials for further study: reinforcement learning (Sutton and Barto, 2017; Szepesv´ari, 2010; Bertsekas, 2012; Powell, 2011; Bertsekas and Tsitsiklis, 1996; Puterman, 2005; Littman, 2015; Kaelbling et al., 1996); deep learning (LeCun et al., 2015; Goodfellow et al., 2016; Ben”
Auditing large language models: a three-layered approach (2023, neutral): “Until the 1980s, most NLP systems used logic-based rules and focused on automating the structural analysis of language needed to enable machine translation and speech recognition [57].”
Attention Spiking Neural Networks (2022, background): “, compute vision (CV) [3], natural language processing (NLP) [4], medical diagnosis [5], game playing [6], etc.”
Exploiting the Ground-Truth: An Adversarial Imitation Based Knowledge Distillation Approach for Event Detection (2019, background): “With the rapid development in the ﬁelds of Text Mining (TM) and Natural Language Processing (NLP), the study on Event Detection (ED) has gained great popularity (Aggar-wal and Zhai 2012; Hirschberg and Manning 2015).”
Three Problems With Current Digital Mental Health Research … and Three Things We Can Do About Them (2017, background): “New technologies and methods, such as artificial intelligence (12), natural language processing (13), and virtual reality (14), open fundamentally new intervention paradigms.”
Large Margin Prototypical Network for Few-shot Relation Classification with Fine-grained Features (2019, background): “Relation classification (RC) [3] is a pivotal task for both natural language understanding (NLU) [7] and knowledge graph completion (KGC) [14].”