Advances in natural language processing

By Julia Hirschberg, Christopher D. Manning. Science, 2015

974 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/e050b06b2e4937b516d630043cb68b8bc77078df

How researchers see this paper

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

In Artificial Intelligence

Citations in the corpus

Natural language processing (NLP) in management research: A literature review (2020, background): “In recent years, NLP has experienced significant breakthroughs with the emergence of Artificial Intelligence (AI) (Barr and Feigenbaum, 2014; Hirschberg and Manning, 2015; Lu, 2019).”
Machine Learning for Reliability Engineering and Safety Applications: Review of Current Status and Future Opportunities (2020, background): “The problem may involve an output with only two classes or bi-class, such as spam email identification (spam/not spam) [29], or multi-class output, such as speech and handwriting recognition [30].”
ChatGPT for shaping the future of dentistry: the potential of multi-modal large language model (2023, background): “For instance, NLR algorithms can examine adverse drug reaction (ADR) patterns linked to various dental procedures and drugs.(71) Sometimes, drug administration can lead to gum bleeding and severer diseases like bisphosphonate-related osteonecrosis.”
Deep learning in business analytics and operations research: Models, applications and managerial implications (2018, positive): “Notably, embeddings can be constructed in such a way that they map from several neighboring input vectors onto the low-dimensional representation in order to encode additional semantics ( Hirschberg & Manning, 2015 ).”
Dealing with Disagreements: Looking Beyond the Majority Vote in Subjective Annotations (2021, background): “Detecting emotions from language has been a signiﬁcant area of research in NLP for the past two decades (Liscombe et al., 2003; Aman and Szpakowicz, 2007; Desmet and Hoste, 2013; Hirschberg and Manning, 2015; Poria et al., 2019).”
Opportunities and Challenges for Artificial Intelligence in India (2018, background): “Substantive progress made on these topics has resulted in viable systems for machine translation, spoken dialogue, sentiment analysis, and social media analysis [25].”
Deep Learning Algorithms for Rotating Machinery Intelligent Diagnosis: An Open Source Benchmark Study (2020, background): “language processing (NLP) [6–8], and other fields [9, 10].”
Analysis of Explainers of Black Box Deep Neural Networks for Computer Vision: A Survey (2019, background): “An interesting example are word embeddings [19] that a Natural Language Processing (NLP) algorithm creates from training datasets, which are just any available texts of a special origin [20].”
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).”
Artificial Intelligence for Mental Health and Mental Illnesses: An Overview (2019, background): “…(NLP) NLP is a subfield of AI that involves using the aforementioned algorithmic methods; however, it specifically refers to how computers process and analyze human language in the form of unstructured text and involves language translation, semantic understanding, and information extraction [50].”
Improving Sentiment Analysis with Document-Level Semantic Relationships from Rhetoric Discourse Structures (2017, background): “The reason behind this difficulty is of a fundamental nature as it is virtually impossible to encode a text with a fixed-length vector while preserving its order and context [4].”