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Lexalytics Celebrates Its Anniversary: 20 Years of NLP Innovation

Lexalytics

We’ve pioneered a number of industry firsts, including the first commercial sentiment analysis engine, the first Twitter/microblog-specific text analytics in 2010, the first semantic understanding based on Wikipedia in 2011, and the first unsupervised machine learning model for syntax analysis in 2014.

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10 Graphs That Sum Up the State of AI in 2023

Flipboard

This split has steadily grown since 2011, when the percentages were nearly equal. researchers surveyed natural language processing researchers, as evidenced by publications, to get a handle on what AI experts think about AI research, HAI reported. Industry, not academia, is drawing new AI Ph.D.’s percent of all AI Ph.D.’s

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Amr Nour-Eldin, Vice President of Technology at LXT – Interview Series

Unite.AI

However, the more innovative paper in my view, is a paper with the second-most citations, a 2011 paper titled “ Memory-Based Approximation of the Gaussian Mixture Model Framework for Bandwidth Extension of Narrowband Speech “ In that work, I proposed a new statistical modeling technique that incorporates temporal information in speech.

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What Is Retrieval-Augmented Generation?

NVIDIA

That’s when researchers in information retrieval prototyped what they called question-answering systems, apps that use natural language processing ( NLP ) to access text, initially in narrow topics such as baseball. IBM’s Watson became a TV celebrity in 2011 when it handily beat two human champions on the Jeopardy!

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Testing the Robustness of LSTM-Based Sentiment Analysis Models

John Snow Labs

Sentiment analysis, a branch of natural language processing (NLP), has evolved as an effective method for determining the underlying attitudes, emotions, and views represented in textual information. The 49th Annual Meeting of the Association for Computational Linguistics (ACL 2011). abs/2005.03993 Andrew L.

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Can ChatGPT Compete with Domain-Specific Sentiment Analysis Machine Learning Models?

Topbots

SA is a very widespread Natural Language Processing (NLP). Journal of Finance (2011), 66(1):35–65. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 595–605. I am a researcher, and its ability to do sentiment analysis (SA) interests me. Hamilton, W.

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Question answering using Retrieval Augmented Generation with foundation models in Amazon SageMaker JumpStart

AWS Machine Learning Blog

There are a few limitations of using off-the-shelf pre-trained LLMs: They’re usually trained offline, making the model agnostic to the latest information (for example, a chatbot trained from 2011–2018 has no information about COVID-19). They’re mostly trained on general domain corpora, making them less effective on domain-specific tasks.

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