The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Jumping NLP Curves: A Review of Natural Language Processing Research [Review Article]
Abstract
Natural language processing (NLP) is a theory-motivated range of computational techniques for the automatic analysis and representation of human language. 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). This review paper draws on recent developments in NLP research to look at the past, present, and future of NLP technology in a new light. Borrowing the paradigm of `jumping curves' from the field of business management and marketing prediction, this survey article reinterprets the evolution of NLP research as the intersection of three overlapping curves-namely Syntactics, Semantics, and Pragmatics Curveswhich will eventually lead NLP research to evolve into natural language understanding.
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Cites
Building a Large Annotated Corpus of English: The Penn Treebank
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SWITCHBOARD: telephone speech corpus for research and development
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“In particular, subsumptive knowledge representations build upon IsA relationships, which are usually extracted through syntactic patterns for automatic hypernym discovery (Hearst, 1992) able to infer tr iples such as <Pablo Picasso-IsA-ar tist> from stretches of text like “...artists such as Pablo…”
BackgroundToward an architecture for never-ending language learning
Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Hruschka, E., & Mitchell, T. (2010) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v24i1.7519 · Cited by 2,000
Learning deep physiological models of affect
Martinez, H. P., Bengio, Y., & Yannakakis, G. N. (2013) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2013.2247823 · Cited by 287
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Recent Trends in Deep Learning Based Natural Language Processing [Review Article]
Young, T., Hazarika, D., Poria, S., & Cambria, E. (2018) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2018.2840738 · Cited by 2,921
Self-citationA review of affective computing: From unimodal analysis to multimodal fusion
Poria, S., Cambria, E., Bajpai, R., & Hussain, A. (2017) · Information Fusion · doi:10.1016/j.inffus.2017.02.003 · Cited by 1,644
Self-citationDeep Learning Enabled Semantic Communication Systems
Xie, H., Qin, Z., Li, G. Y., & Juang, B.-H. (2021) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2021.3071210 · Cited by 1,602
Affective Computing and Sentiment Analysis
Cambria, E. (2016) · IEEE Intelligent Systems · doi:10.1109/mis.2016.31 · Cited by 1,204
Self-citationMachine learning in materials science
Wei, J., Chu, X., Sun, X., Xu, K., Deng, H., Chen, J., Wei, Z., & Lei, M. (2019) · InfoMat · doi:10.1002/inf2.12028 · Cited by 1,069
Metadata
- Authors
- Cambria, E., & White, B.
- Venue
- IEEE Computational Intelligence Magazine
- Published
- 2014-05
- Volume
- 9
- Issue
- 2
- Pages
- 48-57
- ISSN
- 1556-603X (ISSN-L)
- OpenAlex
- W2028140375
- Cited by
- 1,102
- References
- 7
- FWCI
- 61.07
- Type
- journal article
- Language
- en
- Open access
- closed
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