Mlearning.ai

APRIL 1, 2023

NLP A Comprehensive Guide to Word2Vec, Doc2Vec, and Top2Vec for Natural Language Processing In recent years, the field of natural language processing (NLP) has seen tremendous growth, and one of the most significant developments has been the advent of word embedding techniques.

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Kavita Ganesan

MAY 24, 2022

I took one natural language processing class and the professor. But around 2013 is where data science started to really become a thing. So it’s only around 2013, 2011, where AI became a thing in the industry. Kavita Ganesan 16:00 So USC is a special breed, they were so much into AI, even when I joined.

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AWS Machine Learning Blog

FEBRUARY 2, 2024

Embeddings capture the information content in bodies of text, allowing natural language processing (NLP) models to work with language in a numeric form. In entered the Big Data space in 2013 and continues to explore that area. He also holds an MBA from Colorado State University.

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ML Review

FEBRUARY 26, 2018

Following its successful adoption in computer vision and voice recognition, DL will continue to be applied in the domain of natural language processing (NLP). ACM, 2013: 2333–2338. [2] 2] Minghui Qiu and Feng-Lin Li. MeChat: A Sequence to Sequence and Rerank based Chatbot Engine.

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Robotics Deep Learning Chatbots Natural Language Processing 52

Hal Daumé III

AUGUST 14, 2016

Spruit The Social Impact of Natural Language Processing This is a nice paper summarizing four issues that come up in ethics that also come up in NLP. This paper shows that current techniques don't achieve that: there's a consistent win to be had by doing global normalization. P16-2096 : Dirk Hovy; Shannon L.

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Unite.AI

JULY 28, 2023

MarketMuse, founded by Aki Balogh and Jeff Coyle in 2013, is a content marketing and keyword planner tool that utilizes artificial intelligence and machine learning. MarketMuse Key Features: Competitive content analysis. Content clusters. Content planning. Keyword research. Content brief generator. Content optimization tools.

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AWS Machine Learning Blog

JANUARY 4, 2024

One such area that is evolving is using natural language processing (NLP) to unlock new opportunities for accessing data through intuitive SQL queries. Instead of dealing with complex technical code, business users and data analysts can ask questions related to data and insights in plain language.

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Topbots

JUNE 15, 2023

Language Disparity in Natural Language Processing This digital divide in natural language processing (NLP) is an active area of research. 2 ] Multilingual models perform worse on several NLP tasks on low resource languages than on high resource languages such as English.[

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Topbots

SEPTEMBER 11, 2023

3] Don Norman (2013). 4] Google, Gartner and Motista (2013). Continuous Discovery Habits: Discover Products that Create Customer Value and Business Value. [2] 2] Orbit Media (2022). New Blogging Statistics: What Content Strategies Work in 2022? We asked 1016 Bloggers. [3] The Design of Everyday Things. [4]

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Lexalytics

APRIL 5, 2021

This is the sort of representation that is useful for natural language processing. Corrado and Jeffrey Dean (2013) “ Distributed Representations of Words and Phrases and their Compositionality ” J. Socher, L.-J. Bengio, and P. Haffner (1998) “ Gradient-based learning applied to document recognition ” S. Hochreiter and J.

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Mlearning.ai

MARCH 9, 2023

2013; Goodfellow et al., Generative adversarial networks-based adversarial training for natural language processing. Adversarial attacks have been shown to be effective in evading state-of-the-art machine learning models, including those used for image classification and segmentation (Szegedy et al., Szegedy, C.,

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AWS Machine Learning Blog

NOVEMBER 22, 2023

AIM333 (LVL 300) | Explore text-generation FMs for top use cases with Amazon Bedrock Tuesday November 28| 2:00 PM – 3:00 PM (PST) Foundation models can be used for natural language processing tasks such as summarization, text generation, classification, open-ended Q&A, and information extraction.

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Sebastian Ruder

JANUARY 19, 2021

The selection of areas and methods is heavily influenced by my own interests; the selected topics are biased towards representation and transfer learning and towards natural language processing (NLP).

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The Stanford AI Lab Blog

MAY 31, 2022

Language Model Pretraining Language models (LMs), like BERT 1 and the GPT series 2 , achieve remarkable performance on many natural language processing (NLP) tasks. We will now see how LinkBERT performs on several downstream natural language processing tasks. Let’s use LinkBERT!

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Probably Approximately a Scientific Blog

JANUARY 3, 2016

We're already after 6 posts on the topic of natural language processing, and I can't believe I haven't discussed this basic topic yet. So today I'm going to discuss words; More accurately - I will discuss how words are represented in natural language processing. CoRR, 2013. EMNLP 2014.

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Artificial Corner

JUNE 6, 2023

Natural Language Processing (NLP) techniques can be applied to analyze and understand unstructured text data. MapReduce: simplified data processing on large clusters. The integration of AI and ML into data engineering pipelines enables a wide range of applications. Zaharia, M., Spark: cluster computing with working sets.

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Data Mining Big Data ETL Machine Learning 52

Sebastian Ruder

AUGUST 18, 2019

2013 ) learned a single representation for every word independent of its context. Major themes Several major themes can be observed in how this paradigm has been applied: From words to words-in-context  Over time, representations incorporate more context. Early approaches such as word2vec ( Mikolov et al., 2019 ; Logan IV et al.,

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Sebastian Ruder

OCTOBER 26, 2019

2013 ): (mathbf{W}_{L_2} = arg min_{mathbf{W}} | mathbf{X}_Smathbf{W} - mathbf{X}_T |_2 ) After having learned this mapping, we can now project a word embedding (mathbf{x}_{L_2}) from (mathbf{X}_{L_2}) simply as (mathbf{W}_{L_2} mathbf{x}_{L_2} ) to the space of ( mathbf{X}_{L_1}). 2015 , Artetxe et al.,

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Sebastian Ruder

DECEMBER 6, 2021

RC Olympics: The many domains of reading comprehension Datasets in the Fiction domain typically require processing narratives in books such as NarrativeQA ( Kočiský et al., 2013 ), MCScript ( Modi et al., The MLQA alignment and annotation process ( Lewis et al., 2018 ), Children's Book Test ( Hill et al.,

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BERT NLP Natural Language Processing Computational Linguistics 52

Probably Approximately a Scientific Blog

AUGUST 30, 2019

Scope The reason that everyone is talking about language models (LMs) lately is not so much that they're all working on text generation, but because pre-trained LMs (like the OpenAI GPT-2 or Google's BERT ) are used to produce text representations across various NLP applications, greatly improving their performances.

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Explosion

DECEMBER 17, 2013

I wrote this blog post in 2013, describing an exciting advance in natural language understanding technology. Natural languages introduce many unexpected ambiguities, which our world-knowledge immediately filters out. The derivation for the transition system we’re using, Arc Hybrid, is in Goldberg and Nivre (2013).

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ML Review

JUNE 4, 2018

Recent Intersections Between Computer Vision and Natural Language Processing (Part Two) This is the second instalment of our latest publication series looking at some of the intersections between Computer Vision (CV) and Natural Language Processing (NLP). In: Daniilidis K., Paragios N. 12, December.

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ODSC - Open Data Science

FEBRUARY 29, 2024

The Stanford AI Lab Founded in 1963, the Stanford AI Lab has made significant contributions to various domains, including natural language processing, computer vision, and robotics. Recently, they unveiled new mind-body neural control prostheses. Another project, SynthID , helps to identify and watermark AI-generated images.

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Artificial Intelligence Artificial Intelligence AI Researcher AI Research 52

NLP People

JULY 24, 2019

Jumping NLP Curves: A Review of Natural Language Processing Research [Review Article]. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Neural Network Methods in Natural Language Processing. Recent Trends in Deep Learning Based Natural Language Processing.

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ML Review

MAY 3, 2018

Recent Intersections Between Computer Vision and Natural Language Processing (Part One) This is the first instalment of our latest publication series looking at some of the intersections between Computer Vision (CV) and Natural Language Processing (NLP). Available: [link] (last update, 18/03/2013).

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Viso.ai

DECEMBER 21, 2023

His doctoral thesis studied the design of convolutional/recurrent neural networks and their applications across computer vision, natural language processing, and their intersections. Transitioning to Facebook (now Meta) in 2013, LeCun served as the first Director of AI Research.

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Probably Approximately a Scientific Blog

NOVEMBER 12, 2016

In EMNLP 2013. [3] Sanda Harabagiu and Andrew Hick. In ACL and COLING 2006. [2] 2] Semantic Parsing on Freebase from Question-Answer Pairs. Jonathan Berant, Andrew Chou, Roy Frostig, and Percy Liang. 3] Learning to parse database queries using inductive logic programming. Zelle and Raymond J. In AAAI 1996. *

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Probably Approximately a Scientific Blog

NOVEMBER 23, 2016

ACL 2015. [3] 3] Computing Word-Pair Antonymy. Saif Mohammad, Bonnie Dorr and Graeme Hirst. EMNLP 2008. [4] 4] Computing Lexical Contrast. Saif Mohammad, Bonnie Dorr, Graeme Hirst, and Peter Turney. 5] Taking Antonymy Mask off in Vector Space. Enrico Santus, Qin Lu, Alessandro Lenci, Chu-Ren Huang. PACLIC 2014. [6]

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