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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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NLP4NLP+5: The Deep (R)evolution in Speech and Language Processing.

Joseph Mariani¹, Gil Francopoulo², Patrick Paroubek¹

¹Université Paris-Saclay, CNRS, Laboratoire Interdisciplinaire des Sciences du Numérique, Orsay, France.

Frontiers in Research Metrics and Analytics

|August 15, 2022

Summary

Recent analysis of speech and natural language processing (NLP) shows significant growth, with 2020 alone publishing as many articles as 1965-1989. Key shifts include a rise in artificial intelligence and machine learning research.

Keywords:

artificial intelligence machine learning natural language processing neural networks research metrics speech processing text mining

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Area of Science:

Computer Science
Computational Linguistics
Artificial Intelligence

Background:

The study analyzes the evolution of speech and natural language processing (NLP) research.
It builds upon previous analyses of the NLP4NLP corpus, extending the timeframe to 55 years (1965-2020).
The extended corpus (NLP4NLP+5) includes nearly 90,000 documents, 67,000 authors, and 590,000 references.

Purpose of the Study:

To analyze changes in speech and NLP research from 2016-2020.
To identify shifts in research topics and author demographics.
To compare recent trends with historical data and general scientific literature.

Main Methods:

Analysis of the extended NLP4NLP+5 corpus (55 years of publications).
Quantitative analysis of document volume, author numbers, references, and word count.
Comparative analysis across different publication sources and with general scientific literature.

Main Results:

A substantial increase in publications, with 2020 matching the output of 1965-1989.
Significant growth in authors, references, and total words analyzed.
Profound changes in research topics, with a notable rise in artificial intelligence, neural networks, machine learning, and word embedding.

Conclusions:

The fields of speech and NLP have undergone rapid transformation in recent years.
A new generation of researchers is emerging, focusing on AI-driven topics.
The landscape of NLP research is rapidly evolving, marked by new publication trends.