ODSC - Open Data Science

MAY 24, 2023

Generative NLP Models in Customer Service: Evaluating Them, Challenges, and Lessons Learned in Banking Editor’s note: The authors are speakers for ODSC Europe this June. Be sure to check out their talk, “ Generative NLP models in customer service. How to evaluate them?

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NLP People

APRIL 10, 2019

It’s Institute of Computational Linguistics , which includes the Phonetics Laboratory , lead by Martin Volk and Volker Dellwo, as well as the URPP Language and Space perform research in NLP topics, such as machine translation, sentiment analysis, speech recognition and dialect detection. University of St. Gallen The University of St.

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NLP People

DECEMBER 8, 2017

Are you looking to study or work in the field of NLP? For this series, NLP People will be taking a closer look at the NLP education landscape in different parts of the world, including the best sites for job-seekers and where you can go for the leading NLP-related education programs on offer.

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Lexalytics

MAY 25, 2021

One of the most important and most-used functions in text analytics and NLP is sentiment analysis — the process of determining whether a word, phrase, or document is positive, negative, or neutral.

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Flipboard

JULY 10, 2023

Unstructured text and data are like gold for business applications and the company bottom line, but where to start? Here are three tools worth a look.

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NLP People

MAY 18, 2018

Are you looking to study or work in the field of NLP? For this series, NLP People will be taking a closer look at the NLP education & development landscape in different parts of the world, including the best sites for job-seekers and where you can go for the leading NLP-related education programs on offer.

NLP 52

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Analytics Vidhya

SEPTEMBER 6, 2021

An important application of Natural Language Processing is text classification and text analytics. But, the problem that lies in dealing with text data is that computers […]. The post Creating a Movie Reviews Classifier Using TF-IDF in Python appeared first on Analytics Vidhya.

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Analytics Vidhya

AUGUST 27, 2021

This article was published as a part of the Data Science Blogathon Overview In today’s world, one of the biggest sources of information is text data, which is unstructured in nature. Finding customer sentiments from product reviews or feedbacks, extracting opinions from social media data are a few examples of text analytics.

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Unite.AI

FEBRUARY 19, 2024

For example, by leveraging Natural Language Processing (NLP) and text analytics, OCR can proficiently scan and transform handwritten or printed documents, such as prescription labels, patient forms, doctor's notes, and lab results, into digital format. Simply put, it is a superior iteration of intelligent automation.

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Ehud Reiter

APRIL 22, 2024

Instead the focus was on what the above-mentioned report called Information Gathering and Sensemaking (eg, using text analytics to analyse stuff) and Business Uses (eg, finding potential advertisers). Obviously this kind of thing is still very important, but nice to see that the NLG usage is now the most common!

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AssemblyAI

NOVEMBER 9, 2023

In Natural Language Processing (NLP), Text Summarization models automatically shorten documents, papers, podcasts, videos, and more into their most important soundbites. What is Text Summarization for NLP? The models are powered by advanced Deep Learning and Machine Learning research.

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LevelAI

FEBRUARY 5, 2024

Text Analytics: Spotting occurrences of words This approach matches pre-defined keywords or sequences of words to text excerpts within call transcripts. A text analytics solution would be able to match the name of the company to verify if the agent has said, “Hello, this is Level AI customer service.

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ODSC - Open Data Science

OCTOBER 23, 2023

Traditionally, our NLP track has focused on the usual aspects of NLP, such as text analytics and sentiment analysis. The Rise of Large Language Models One of the biggest themes you’ll see at ODSC West this year is the focus on LLMs, generative AI, and prompt engineering.

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Lexalytics

SEPTEMBER 9, 2021

For nearly two decades, Lexalytics has been a pioneer in structured and unstructured data analytics, translating text into profitable decisions with our natural language processing (NLP), machine learning (ML), and artificial intelligence (AI) solutions for the world’s most customer-centric brands.

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Chatbots Life

MARCH 16, 2023

Text analytics Text analytics is another data collection method that has gained popularity over the last few years due to advances in machine learning algorithms and extensive data processing capabilities.

Machine Learning 52

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Lexalytics

JUNE 11, 2021

That’s where AI, ML, and NLP come in. Using an NLP platform and your industry expertise, you can mine the data in the cloud and on the web to create a map of themes that deliver valuable, actionable insights into customers’ emotions and behaviors. Learn more about how AI and NLP can assist with pharma matters.

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IBM Journey to AI blog

AUGUST 28, 2023

One of the best ways to take advantage of social media data is to implement text-mining programs that streamline the process. What is text mining? Text analysis takes it a step farther by focusing on pattern identification across large datasets, producing more quantitative results.

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ODSC - Open Data Science

FEBRUARY 17, 2023

What to Expect in 2023: A Data Scientist’s Top 5 AI Predictions Between improved NLP and increased use of AI in finance, here are one data scientist’s 2023 AI predictions. Here’s how you can uncover actionable textual and geospatial patterns related to counter human trafficking using NLP. Here’s why that may or may not be true.

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Pickl AI

AUGUST 1, 2023

Introduction to Applied Text Mining in Python Before going ahead, it is important to understand, What is Text Mining in Python? Text mining is also known as text analytics or Natural Language Processing (NLP). What is text mining in NLP?

Data Analysis 52

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Pickl AI

JULY 20, 2023

5. Text Analytics and Natural Language Processing (NLP) Projects: These projects involve analyzing unstructured text data, such as customer reviews, social media posts, emails, and news articles. NLP techniques help extract insights, sentiment analysis, and topic modeling from text data.

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Kavita Ganesan

AUGUST 25, 2022

They combined insights from an independent off-the-shelf text analytics tool such as Netbase (extremely pricey, by the way) with custom-built pipelines for a complete market research analysis. Recommeded Reading 7 NLP Applications in Business List of Common AI Problem Types in Business This is what one of my clients did.

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Analytics Vidhya

JUNE 16, 2021

The post How to Perform Basic Text Analysis without Training Dataset appeared first on Analytics Vidhya. ArticleVideo Book This article was published as a part of the Data Science Blogathon Overview This article will give you a basic understanding of how.

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Bitext

SEPTEMBER 17, 2021

Recapping, the main limitation of Machine Learning for text analytics is that it is “blind” to text structure. And text structure is essential for moving towards text understanding. This is the first benefit Linguistics provides to data sicentists.

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IBM Journey to AI blog

JULY 17, 2023

These encoder-only architecture models are fast and effective for many enterprise NLP tasks, such as classifying customer feedback and extracting information from large documents. With multiple families in plan, the first release is the Slate family of models, which represent an encoder-only architecture. To bridge the tuning gap, watsonx.ai

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DataRobot Blog

MARCH 9, 2022

This is one of the reasons why detecting sentiment from natural language (NLP or natural language processing ) is a surprisingly complex task. It could be anything from a sentence to a paragraph to a longer-form collection of text. Because these networks are recurrent, they are ideal for working with sequential data such as text.

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Pickl AI

JULY 23, 2023

Text Analytics (Natural Language Processing) Text analytics, also known as natural language processing (NLP), involves extracting valuable information and insights from unstructured text data, such as customer reviews, social media posts, or survey responses.

Data Analysis 52

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Lexalytics

APRIL 5, 2021

Fast-forward a couple of decades: I was (and still am) working at Lexalytics, a text-analytics company that has a comprehensive NLP stack developed over many years. As with ULMFiT and ELMo, these contextual word vectors could be incorporated into any NLP application. I was out of the neural net biz. and BERT.

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Explosion

FEBRUARY 14, 2016

If you were doing text analytics in 2015, you were probably using word2vec. Sense2vec: Using NLP annotations for more precise vectors The idea behind sense2vec is super simple. def transform_texts(texts): # Load the annotation models nlp = English() # Stream texts through the models. Sense2vec (Trask et.

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