Natural language processing: state of the art, current trends and challenges
By Diksha Khurana, Aditya Koli, Kiran Khatter, Sukhdev Singh. Multimedia tools and applications, 2017
1,531 citations recorded by the source corpus.
Natural language processing (NLP) has recently gained much attention for representing and analyzing human language computationally. It has spread its applications in various fields such as machine translation, email spam detection, information extraction, summarization, medical, and question answering etc. In this paper, we first distinguish four phases by discussing different levels of NLP and components of N atural L anguage G eneration followed by presenting the history and evolution of NLP. We then discuss in detail the state of the art presenting the various applications of NLP, current trends, and challenges. Finally, we present a discussion on some available datasets, models, and evaluation metrics in NLP.
Subfields: Artificial Intelligence
View Paper: https://www.semanticscholar.org/paper/c52044dcf1b985c1373de672d89c354e85463575
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
- Kelvin C. K. Chan
- Eduardo C. Garrido-Merchán
- Chen Change Loy
- Santiago González-Carvajal
- Terry Yue Zhuo
- Yujin Huang
- Zhenchang Xing
- Chunyang Chen
Citations in the corpus
- Exploring AI Ethics of ChatGPT: A Diagnostic Analysis (2023, background): “Instead of contemplating hypothetical or distant uses of technology, we believe it is more crucial to address moral or ethical issues in current and future applications [28].”
- Comparing BERT against traditional machine learning text classification (2020, background): “Generally, linguistic approaches consist in applying a series of rules, which are designed by linguistic experts [14].”
- Exploring CLIP for Assessing the Look and Feel of Images (2022, background): “2022; Zhou, Loy, and Dai 2022), it is not viable for perception assessment, due to linguistic ambiguity (Khurana et al. 2017).”
- Large language models in medicine (2023, background): “Generative AI developers aim to produce models that can create content on demand and intersect with natural language processing within applications, such as chatbots and text prediction — in other words, ‘natural language generation’ tasks 4 .”