Sebastian Ruder

JANUARY 24, 2022

2021) 2021 saw many exciting advances in machine learning (ML) and natural language processing (NLP). If CNNs are pre-trained the same way as transformer models, they achieve competitive performance on many NLP tasks [28].   Popularized by GPT-3 [32] , prompting has emerged as a viable alternative input format for NLP models.

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Google Research AI blog

JULY 10, 2023

Posted by Malaya Jules, Program Manager, Google This week, the 61st annual meeting of the Association for Computational Linguistics (ACL), a premier conference covering a broad spectrum of research areas that are concerned with computational approaches to natural language, is taking place online.

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AWS Machine Learning Blog

MAY 22, 2023

The test data consists of three different paragraphs, one on the Australian city of Adelaide from the first two paragraphs of it Wikipedia page , one regarding Amazon Elastic Block Store (Amazon EBS) from the Amazon EBS documentation , and one of Amazon Comprehend from the Amazon Comprehend documentation.

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Heartbeat

FEBRUARY 21, 2023

Picture by Anna Nekrashevich , Pexels.com Introduction Sentiment analysis is a natural language processing technique which identifies and extracts subjective information from source materials using computational linguistics and text analysis. Spark NLP is a natural language processing library built on Apache Spark.

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Heartbeat

FEBRUARY 8, 2023

Source: Author The field of natural language processing (NLP), which studies how computer science and human communication interact, is rapidly growing. By enabling robots to comprehend, interpret, and produce natural language, NLP opens up a world of research and application possibilities.

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NLP People

JANUARY 2, 2019

Hundreds of researchers, students, recruiters, and business professionals came to Brussels this November to learn about recent advances, and share their own findings, in computational linguistics and Natural Language Processing (NLP). BERT is a new milestone in NLP.

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Bugra Akyildiz

JULY 16, 2023

In here, the distinction is that base models want to complete documents(with a given context) where assistant models can be used/tricked into performing tasks with prompt engineering. Natural language processing (NLP) or computational linguistics is one of the most important technologies of the information age.

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Explosion

DECEMBER 28, 2019

Jan 15: The year started out with us as guests on the NLP Highlights podcast , hosted by Matt Gardner and Waleed Ammar of Allen AI. In the interview, Matt and Ines talked about Prodigy , where training corpora come from and the challenges of annotating data for an NLP system – with some ideas about how to make it easier. ?

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Sebastian Ruder

NOVEMBER 14, 2022

At the same time, a wave of NLP startups has started to put this technology to practical use. I will be focusing on topics related to natural language processing (NLP) and African languages as these are the domains I am most familiar with. This post takes a closer look at how the AI community is faring in this endeavour.

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Sebastian Ruder

JUNE 6, 2022

Seeing the emergence of such multilingual multimodal approaches is particularly encouraging as it is an improvement over the previous year’s ACL where multimodal approaches mainly dealt with English (based on an analysis of “multi-dimensional” NLP research we did for an ACL 2022 Findings paper ). Hershcovich et al.

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Sebastian Ruder

DECEMBER 6, 2021

Open-Retrieval QA vs Reading Comprehension Open-retrieval QA focuses on the most general setting where given a question we first need to retrieve relevant documents from a large corpus such as Wikipedia. We then process these documents to identify the relevant answer as can be seen below. XQA ( Liu et al., TyDi QA ( Clark et al.,

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Explosion

MAY 8, 2016

This means that an English-only NLP system can get away with some very useful simplifying assumptions. In the same way that a physicist might assume a frictionless surface or a spherical cow , sometimes it’s useful for computational linguists to assume projective trees and context-free grammars. It’s a simplifying assumption.

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Probably Approximately a Scientific Blog

JULY 13, 2015

You expect the search engine to retrieve the following documents: Figure 1: search results for the query "actor Scientology" that don't directly involve the word "actor" since they are talking about a certain actor (Tom Cruise or John Travolta) and Scientology. However, what if these documents don't contain the word actor ?

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Mlearning.ai

FEBRUARY 6, 2023

OpenAI themselves have included some considerations for education in their ChatGPT documentation, acknowledging the chatbot’s use in academic dishonesty. A Chatbot Detector could pick up on the writing style of a human (since a human re-wrote the chatbot answer) and classify the document as human-written. Attention is not Explanation.

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ML Review

MARCH 5, 2019

Apart from averaging the word vectors of all the words within a paper, there are also specialised approaches to learn embeddings for whole documents. Both LSA and doc2vec allow us to take documents of text and embed them in a vector space. In our case, we deliberately use the whole full-text of an ArXiv submission as the document.

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NLP People

JULY 24, 2019

The 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019) is starting this week in Florence, Italy. We took the opportunity to review major research trends in the animated NLP space and formulate some implications from the business perspective. But what is the substance behind the buzz?

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AWS Machine Learning Blog

AUGUST 8, 2024

By integrating LLMs, the WxAI team enables advanced capabilities such as intelligent virtual assistants, natural language processing (NLP), and sentiment analysis, allowing Webex Contact Center to provide more personalized and efficient customer support.

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Probably Approximately a Scientific Blog

OCTOBER 30, 2017

For example, this blog post is published in a blog that largely discusses natural language processing, so if I write "NLP", you'd know I refer to natural language processing rather than to neuro-linguistic programming. Word Sense Disambiguation (WSD) is an NLP task aimed at disambiguating a word in context.

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