Ontotext

FEBRUARY 14, 2024

It has been an important capability for Ontotext ever since we dove into Natural Language Processing (NLP), as it is a crucial aspect of the interplay between text analysis and knowledge graphs. One such example is the tagging behind News On the Web. We’ve had a fair share of such implementations over the years.

Cost-Benefit 52

Cost-Benefit Modeling Metadata Optimization 52

Ontotext

FEBRUARY 1, 2024

When our auto-categorization engine, Graphite Knowledge Studio, tags content with metadata derived from taxonomies, we also capture and store this content metadata inside the knowledge graph, thereby linking concepts to the content they describe. What do we mean by content aware knowledge graphs?

Enterprise 59

Enterprise Management Metadata Modeling 59

IBM Big Data Hub

FEBRUARY 14, 2024

Voice-based queries use natural language processing (NLP) and sentiment analysis for speech recognition so their conversations can begin immediately. With text to speech and NLP, AI can respond immediately to texted queries and instructions. Humanize HR AI can attract, develop and retain a skills-first workforce.

Cost-Benefit 86

Cost-Benefit Insurance Unstructured Data Machine Learning 86

Ontotext

JULY 13, 2023

Knowledge graphs have greatly helped to successfully enhance business-critical enterprise applications, especially those where high performance tagging and agile data integration is needed. The customer and employee experience when using an application is key for companies to have a real impact on their processes and results.

Enterprise 52

Enterprise Metadata Digital Transformation Software 52

Ontotext

DECEMBER 16, 2021

Coupling natural language processing (NLP) with a knowledge graph, as Ontotext has done for many of its clients, creates a positive feedback loop that improves document discoverability. NLP models trained on collections of terms and concepts, like a knowledge graph, are better able to sort through documents to extract information.

Metadata 98

Metadata Reporting Unstructured Data Risk 98

Ontotext

DECEMBER 15, 2023

A newer repo is PaddlePaddle/PaddleNLP that has topics: nlp search-engine compression sentiment-analysis transformers information-extraction question-answering llama pretrained-models embedding bert semantic-analysis distributed-training ernie neural-search uie document-intelligence paddlenlp llm. By exploring the topic nlp , we find 26.7k

Machine Learning 69

Machine Learning Interactive Deep Learning Statistics 69

IBM Big Data Hub

OCTOBER 16, 2023

Voice-based queries use Natural Language Processing (NLP) and sentiment analysis for speech recognition. This communication can involve speech recognition, speech-to-text conversion, NLP, or text-to-speech. Then, it suggests the social media user tag that individual. Companies also take advantage of ML in smartphone cameras.

Machine Learning 51

Machine Learning Marketing Forecasting Modeling 51

Sisense

APRIL 30, 2020

As the title suggests, in this article we’ll explore best practices in natural language processing (NLP). One of the first things you have to do for semantic analysis for an NLP project is text preprocessing. This is a vital practice in NLP and makes data more understandable for the algorithms. Preprocessing works like this: 1.

Machine Learning 91

Machine Learning Statistics Modeling IT 91

IBM Big Data Hub

AUGUST 28, 2023

Text mining —also called text data mining—is an advanced discipline within data science that uses natural language processing (NLP) , artificial intelligence (AI) and machine learning models, and data mining techniques to derive pertinent qualitative information from unstructured text data. What is text mining?

Data mining 56

Data mining Machine Learning Deep Learning Marketing 56

Smart Data Collective

MARCH 26, 2021

It entails deep learning from its neural networks, natural language processing (NLP), and constant changes based on incoming information. Manually, it would take individuals countless hours to sort types of images into folders or tags. These bots use machine learning and NLP to understand your request.

Machine Learning 141

Machine Learning Interactive Deep Learning Technology 141

bridgei2i

NOVEMBER 25, 2020

Natural Language Processing (NLP) and Semantic Search. Semantic search and NLP in general terms deal with how the search engines process a search request using not just the keywords but also their variations, synonyms, and other related factors. This will also minimize the risk involved in the process. Automation and Data Governance.

Enterprise 52

Enterprise Data mining Consulting Risk Management 52

BizAcuity

OCTOBER 21, 2019

Text analysis involves information retrieval, lexical analysis to study word frequency distributions, pattern recognition, tagging/annotation, information extraction, data mining techniques including link and association analysis, visualization, and predictive analytics. The way forward.

Analytics 73

Analytics Unstructured Data Data mining Structured Data 73

Ontotext

MAY 26, 2021

As Wikipedia is not only the biggest encyclopedia, but its contributors adhere to a strict editorial process, each industry tag is usually assigned by one person and reviewed by another. Gold Standards are also needed to create training and evaluation sets for AI systems when we want to extract information from free text.

Data Quality 87

Data Quality Machine Learning Measurement Metadata 87

Insight

MARCH 12, 2020

Using nothing more than a book’s synopsis, the AIgent can surface similar books, genre tags, and sales proxies. for example genre tags, ratings, and sales. For my purposes, the most interesting data on Goodreads comes in the form of genre/content tags. Likewise, it will help writers identify agents who are a good fit.

Modeling 59

Modeling Metadata Publishing Sales 59

CIO Business Intelligence

JULY 31, 2023

AI/ML will create the first drafts of a tagging structure and recommend questions and answers by identifying the areas where your team is most frequently asking for good documentation or solutions.

Testing 80

Testing Visualization Enterprise Technology 80

Laminar Security

DECEMBER 13, 2023

The power of AI and data classification Data classification , often referred to as “tagging” or “labeling”, is a crucial process that categorizes data based on its type and sensitivity. Natural language processing (NLP) is employed when working with text or speech data. It helps in determining what data you have and its sensitivity.

Data-driven 119

Data-driven Machine Learning Risk Deep Learning 119

bridgei2i

FEBRUARY 6, 2020

Digital Enterprise” is not a tag that companies can attach to themselves based on a few initiatives they undertake. On the Enterprise front, the combination of AI-powered analytics, machine learning, NLP and augmented analytics is set to become the new norm, as stated by Gartner. Improving Operational Effectiveness. Digital Enterprise.

IoT 69

IoT Internet of Things Digital Transformation ROI 69

IBM Big Data Hub

APRIL 18, 2024

Achieving these feats is accomplished through a combination of sophisticated algorithms, natural language processing (NLP) and computer science principles. NLP techniques help them parse the nuances of human language, including grammar, syntax and context. 46% of survey respondents in 2024 showed a preference for open source models.

Cost-Benefit 87

Cost-Benefit Modeling Manufacturing Interactive 87

BizAcuity

OCTOBER 21, 2019

Text analysis involves information retrieval, lexical analysis to study word frequency distributions, pattern recognition, tagging/annotation, information extraction, data mining techniques including link and association analysis, visualization, and predictive analytics. THE WAY FORWARD.

Analytics 40

Analytics Unstructured Data Data mining Structured Data 40

Jet Global

MARCH 5, 2019

Natural language processing (NLP): Natural language processing is focused on improving communication between humans and machines, by analyzing human language inputs (either via direct messages, online comments, or searches) and providing outputs (either analysis or generating a response.) So what can AI actually do?

Business Intelligence 75

Business Intelligence OLAP Visualization Reporting 75

Insight

NOVEMBER 14, 2019

Part of Speech (POS) Tagging After LDA, I decided to tag the part of speech (POS) for each reflection and extract the verbs. I parsed the reflection and extracted all of the verbs used in the reflections via POS tagging. Start GuidedLDA by preprocessing the data, as you do with any NLP work. python setup.py

Testing 54

Testing Modeling Strategy Interactive 54

Analytics Vidhya

SEPTEMBER 27, 2023

Introduction In recent years, the integration of Artificial Intelligence (AI), specifically Natural Language Processing (NLP) and Machine Learning (ML), has fundamentally transformed the landscape of text-based communication in businesses.

Machine Learning 275

Machine Learning Technology Analytics Modeling 275

Ontotext

NOVEMBER 22, 2020

Ontotext’s unique offering of NLP technology and semantic-enrichment coupled with the Platform’s knowledge graphs solves this problem. Machine learning coupled with knowledge graphs is already collecting, categorizing, tagging and adding the needed structure to the endless (and useless) swathes of unstructured data.

Metadata 72

Metadata Cost-Benefit Unstructured Data Technology 72

Analytics Vidhya

MAY 30, 2022

Introduction on NLP Preprocessing Hello friends, In this article, we will discuss text preprocessing techniques used in NLP. The post NLP Preprocessing Steps in Easy Way appeared first on Analytics Vidhya. This article was published as a part of the Data Science Blogathon.

Machine Learning 323

Machine Learning Data Science Publishing Modeling 323

Smart Data Collective

JUNE 4, 2021

Natural Language Processing (NLP). Facebook and google photos identify people from images and recommend tags. From the graph, it is evident that the number of travelers peaks from June to August. As the data is seasonal, time series techniques are a good option for forecasting the number of passengers in the future.

Data Science 104

Data Science Statistics Deep Learning Machine Learning 104

Edwin Chen

JULY 5, 2012

To make this map, I sampled geo-tagged tweets containing the words “soda”, “pop”, or “coke”, performed some state-of-the-art NLP technology to ensure the tweets were soft drink related (e.g., Recall the classic soda vs. pop. Let’s take a look. Coke Zero).

Interactive 81

Interactive Data-driven Measurement Visualization 81

bridgei2i

MAY 11, 2020

For faster issue resolution, team members can scan through the existing knowledge base but this has a few disadvantages: Inefficient search in the current systems as the search algorithms and tags are not updated. Delayed updates on the existing documentation ensures that the latest resolution and fixes are not available to the team.

Data Science 52

Data Science Interactive Enterprise Strategy 52

AWS Big Data

JANUARY 17, 2024

Choose Amazon EMR for Session tag values. He possesses expertise in natural language processing (NLP), recommender systems, diverse ML algorithms, and ML operations. Select Allow external engines to filter data in Amazon S3 locations registered with Lake Formation. Enter your AWS account ID for AWS account IDs. Choose Save.

Data Lake 91

Data Lake Snapshot Big Data Data-driven 91

Data Science 101

OCTOBER 12, 2023

“In general, the most common use of the work I do is to remove bad stuff from the Internet or tag it as suspicious.” The first project we did used NLP for finance contracts (this was 2016). In general, the most common use of the work I do is to remove bad stuff from the Internet or tag it as suspicious.

Finance 64

Finance Machine Learning Interactive Manufacturing 64

Analytics Vidhya

JULY 28, 2020

Overview Learn about Part-of-Speech (POS) Tagging, Understand Dependency Parsing and Constituency Parsing Introduction Knowledge of languages is the doorway to wisdom. . The post How Part-of-Speech Tag, Dependency and Constituency Parsing Aid In Understanding Text Data? appeared first on Analytics Vidhya.

Analytics 286

Analytics Unstructured Data 286

Domino Data Lab

FEBRUARY 19, 2021

brings many improvements to help build, configure and maintain your NLP models, including. The first step in processing raw text into an NLP workflow is to tokenize the text into a document object. These components are added as arguments during the initial phase of converting your raw text into an NLP document object.

Enterprise 119

Enterprise Data Science Modeling Visualization 119

Analytics Vidhya

MARCH 24, 2021

ArticleVideo Book Objective Parts of speech tagging and dependency parsing are widely used techniques in text processing. Understand parts of speech tags and grammars. The post Parts of Speech Tag and Dependency Grammer appeared first on Analytics Vidhya.

Analytics 208

Analytics 208

Ontotext

MAY 22, 2023

Doug Kimball : Using our knowledge graph, you can develop more complex analytics, such as data mining, Natural Language Processing (NLP) and Machine Learning (ML). A CMS may include features that allow for tagging or categorizing content, but it does not have the advanced reasoning and analytics capabilities that a knowledge graph provides.

Management 59

Management Unstructured Data Metadata Cost-Benefit 59

IBM Big Data Hub

MARCH 22, 2024

Requirement analytics (RA) : NLP-based tool for analysis of requirements to identify ambiguity, drive in shift left and determine complexity. Search tag & model (STAM) : Text-based analytics tool for quick analysis of a huge number of existing tests to identify redundancy and identify key attributes for the optimization journey.

Testing 80

Testing Optimization Software Risk 80

AWS Big Data

OCTOBER 25, 2023

The following are the most commonly used services for unstructured data processing: Amazon Comprehend – This natural language processing (NLP) service uses ML to extract metadata from text data. AWS AI services are designed to extract metadata from different types of unstructured data.

Unstructured Data 117

Unstructured Data Metadata Management Analytics 117

Sisense

MAY 26, 2020

The science of understanding and learning from text data is called natural language processing (NLP). Most common challenges we face in NLP are around unstructured data and Big Data. Preprocessing of data means removal of unwanted URLs, HTML tags, stop words, numeric and alphanumeric words, punctuation, and special characters.

Unstructured Data 98

Unstructured Data Big Data Testing Machine Learning 98

Domino Data Lab

JULY 1, 2021

Named Entity Recognition (NER) is an NLP problem, which involves locating and classifying named entities (people, places, organizations etc.) This problem is used in many NLP applications that deal with use-cases like machine translation, information retrieval, chatbots and others. What is Named Entity Recognition? The IOB format.

Modeling 111

Modeling Statistics Testing Metrics 111