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Xelex.ai
Richmond, United States
1-10 Employees
2006
Xelex provides training data to technology companies for use in improving the accuracy of their artificial intelligence applications. With Xelex, we've applied those same core competencies to automate and simplify data annotation workflow management at scale. Plus, the Xelex platform gives all stakeholders in the company the kind of access to projects we’ve not had before“. Xelex AI excels at classifying hallucination types for model correction, including nuanced tasks like identifying partially correct statements, and identifying the source material used in misinterpretations. Both equip Xelex AI to deliver highly accurate and dependable data classification services across a wide range of domains. Xelex AI has curated thousands of hours of exam room conversations and are experts at data curation projects that help improve healthcare large language model accuracy. Xelex AI excels at voicing and collecting high-fidelity audio source material for large language model creation and refinement, including synthetic office note dictation and exam room conversations. Making domain experts more efficient by simplifying complex workflow tasks so that non-technical team members can play a larger role in project management.
Core business
Xelex | text classification and data collection services for NLP training
... Xelex | text classification and data collection services for NLP ...
machsense
Istanbul, Turkey
1-10 Employees
2019
Find out how many products you will sell tomorrow, this week or this month. Learn the feelings of customers towards your products. If you have a customer loyalty program, our recommendation engine can predict which products your each customer is willing to buy and the price they are willing to pay for it. Forecasting future sales provides companies to plan accordingly and be ready for a sudden increase in sales. Time series forecast is also useful for energy demand prediction which enable energy production facilites to optimize their production. There are millions of comments written every day by customers for different products and companies. It is a challenge to keep track of the comments about you on social media channels. Image segmentation is the ultimate point of image recognition and detection.
Core business
Text Classification
... Text Classification. ...
Daedalus
Madrid, Spain
11-50 Employees
1998
We are currently developing projects for Pfizer in 30 different countries. The company uses MeaningCloud to analyze unstructured customer feedback in a variety of channels: surveys, panels, interviews, contact center conversations. MeaningCloud categorizes and extracts very deep insights from those contents, and publishes them through an online tool for consumption by Carrefour’s executives and decision making. They have integrated MeaningCloud’s APIs into their new corporate content management system to embed information extraction and content organization and enrichment capabilities. They integrated MeaningCloud APIs to analyze user-generated content to infer their demographic profile and aspect-based sentiment toward specific brands. They used MeaningCloud APIs to categorize according to predefined themes (products, channels), identify brands/products/competitors… and assess the sentiment related to those. MeaningCloud helps you get the information buried under the text. Use MeaningCloud's APIs easily from one of the available integrations, or use your favorite programming language.
Product
Text Classification
... precision on news or social media with hybrid Text Classification API. ...
NxGN
Johannesburg, South Africa
11-50 Employees
2015
NxGN is a data science company at heart. Our team of data scientists then uses a variety of leading-edge data modelling techniques, such as artificial intelligence, machine learning and deterministic modelling, to build models and create data visualisations that give you the most useful insights for your business. We offer a comprehensive turnkey data science service to help you get the most out of your data. Our offering includes everything fromsourcing data through systems integrations, to designing and building data warehouses, to building data models using AI, ML anddeterministic modelling, to ultimately visualising data using modern data visualisation platforms.
Core business
Free text data classification
... Free text data classification ...
Hureka Technologies Inc
New Brunswick, United States
11-50 Employees
2013
We are committed to Delivering Technology Solutions that make a Transformative Difference for You and Your Business. At CasinoPhilippines10, we are passionate about all things gaming. At Hureka, we focus on transforming a static digital experience into a thriving, responsive environment with real-time conversations ─ a live-data hub that is easily accessible to you, your employees, and your customers. Your success is crucial for our growth and for achieving our mission. Our value-added HYBRID PAYMENT AND SUBSCRIPTION model is the best our industry has to offer. Here is a small sample of some of our best work. The world of online casinos is a vast and exciting one. This is a huge advantage for Filipino players, as it means that you don't have to worry about conversion fees or fluctuating exchange rates.
Service
Text classification
... Text Classification | Hureka Technologies ...
Lettria
Paris, France
11-50 Employees
2019
Lettria combines the best of LLMs and symbolic AI to overcome current GenAI limitations in knowledge extraction.
Product
Text Classification API
... Lettria NLP Studio: Text Classification ...
Eden AI
Lyon, France
1-10 Employees
2020
Our mission is to make AI accessible to as many people as possible and empower businesses to solve complex problems and create new opportunities for growth and innovation. We're united by a shared vision of bringing the power of artificial intelligence to the world, but we also know that having fun is just as important as getting work done! So, in between coding and strategizing, you can find us having a blast playing games and finding new ways to have fun. If you're ever in Lyon, come say hello and join us for some fun and games! If you have any questions, feel free to schedule a call with us! Drop your email and we'll get back to you ASAP to answer any questions you have or just to say hi —we promise not to spam you!
Product
Custom Text Classification
... Custom Text Classification is a process that allows you to classify any ...
Prodigy
-
- Employees
-
Prodigy is a scriptable annotation tool so efficient that data scientists can do the annotation themselves, enabling a new level of rapid iteration. With Prodigy you can take full advantage of modern machine learning by adopting a more agile approach to data collection. Prodigy brings together state-of-the-art insights from machine learning and user experience. Prodigy makes the right thing easy, encouraging you to spend more time understanding your problem and interpreting your results. With Prodigy, you can have an idea over breakfast and get your first results by lunch. Prodigy is fully scriptable, and slots neatly into the rest of your Python-based data science workflow. That's why Prodigy comes with a rich Python API, elegant command-line integration, and a super productive Jupyter extension. Today’s transfer learning technologies mean you can train production-quality models with very few examples.
Product
Text Classification · Prodigy · An annotation tool for AI, Machine Learning & NLP
... Text Classification · Prodigy · An annotation tool for AI, Machine ...
Data Language
Mole Valley, United Kingdom
11-50 Employees
2014
From our team’s roots in scalable data engineering and digital media knowledge graphs at the BBC, we have delivered Data Platforms across sectors.Today, we have codified this experience and expertise into market-leading SaaS products that enable our customers get products to market rapidly, while maintaining a low total cost of ownership. We are passionate about purposeful, well structured, high-performance data products.We work with organizations who want the best value from their core digital assets, and we know how to help them achieve that. From simple data models right through to large scale AI-driven platforms, we have the right buy/build/partner approach to achieve speed to market and maximum agility thereafter.
Product
AI Text Classification
... Our Text Classification service is state-of-the-art, AI and machine ...
Lexalytics
Amherst, United States
11-50 Employees
2003
Lexalytics, an InMoment Company, Recognized for Artificial Intelligence Innovation in 2023 AI Breakthrough Awards for Best Overall NLP Company. Integrate our text analytics APIs to add world-leading NLP into your product, platform, or application. Find people, places, dates, companies, products, jobs, titles, and more. Integrate into your cloud-based enterprise data analytics infrastructure or deliver powerful text analytics to your own customers. Store, manage, and analyze unstructured text documents in a complete solution built on the power of the Semantria API. Pre-built industry configurations for out-of-the-box improvements in sentiment accuracy, topic detection, categorization, and more. Hear what's going on as customers interact with staff, products, and locations throughout their shopping experience.
Product
Document Categorization & Text Classification
... What is document categorization (text classification)? ...
Technologies which have been searched by others and may be interesting for you:
Some interesting numbers and facts about your company results for Text Classification
| Country with most fitting companies | Germany |
| Amount of fitting manufacturers | 1033 |
| Amount of suitable service providers | 1229 |
| Average amount of employees | 1-10 |
| Oldest suiting company | 1998 |
| Youngest suiting company | 2019 |
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Text classification, a pivotal technique within the realm of natural language processing (NLP), involves the process of assigning predefined categories or labels to unstructured text data. This automated method relies on machine learning algorithms that learn from a set of pre-labeled documents, enabling the accurate categorization of new, unseen texts based on their content. The significance of text classification spans various applications, from organizing documents in databases for efficient retrieval, filtering spam in emails, to sentiment analysis where it discerns the sentiment behind a piece of text, be it positive, negative, or neutral. Its role extends into more sophisticated realms like chatbots and virtual assistants, where it helps in understanding user requests and delivering relevant responses. By automating the process of sorting and analyzing vast amounts of textual data, text classification not only enhances operational efficiency but also paves the way for deeper insights into customer behavior, market trends, and beyond. The technology's growing sophistication promises further advancements in how we interact with information, making it an indispensable tool in the arsenal of data scientists and developers working towards more intelligent, responsive technology solutions.
1. Efficiency in Data Organization
Text classification automates the process of sorting vast amounts of unstructured text into predefined categories. This significantly speeds up data processing, allowing for quick retrieval of relevant information, thereby enhancing productivity and reducing manual labor.
2. Improved Accuracy and Consistency
By employing algorithms that learn from predefined examples, text classification minimizes human error, ensuring a higher level of accuracy in data categorization. This method also maintains consistency across large datasets, where manual classification could lead to variability and inconsistency.
3. Enhanced Scalability
As data volumes grow, text classification systems can easily scale to accommodate the increased workload without a proportional increase in resources or costs. This scalability makes it a cost-effective solution for managing large datasets.
4. Valuable Insights and Decision Making
Through the organized and categorized data, text classification provides actionable insights that can inform decision-making processes. By filtering out irrelevant information, it helps focus on the data that matters, leading to better-informed strategies and actions.
While evaluating the different suppliers make sure to check the following criteria:
1. Technology and Methodology
Ensure the supplier uses advanced machine learning algorithms and natural language processing (NLP) techniques for accurate text classification.
2. Data Security and Privacy
Check for robust data security measures to protect sensitive information and comply with data privacy regulations.
3. Customization and Flexibility
The ability to customize solutions according to specific industry needs and requirements is crucial for effective text classification.
4. Integration Capabilities
Ensure the supplier's solutions can easily integrate with existing systems and workflows without significant disruptions.
5. Scalability
Consider whether the supplier's solutions can scale with your business needs, handling increasing volumes of data efficiently.
6. Support and Maintenance
Look for suppliers offering comprehensive support and maintenance services to ensure continuous and smooth operation.
7. Cost-Effectiveness
Evaluate the overall cost against the benefits and ROI the text classification service offers, ensuring it aligns with your budget and value expectations.
Text classification serves as a pivotal tool in content moderation processes within social media platforms and online communities. By automatically categorizing user-generated content based on predefined policies or community guidelines, businesses can efficiently filter out inappropriate or harmful material. This ensures a safe and positive online environment for all users, fostering trust and engagement. In customer service and support, text classification is instrumental in sorting incoming queries and feedback. By analyzing and categorizing tickets or emails based on their content, companies can route them to the appropriate departments or personnel. This streamlines response times and enhances the overall customer experience, as queries are addressed more accurately and promptly. For market research and sentiment analysis, businesses leverage text classification to gauge public opinion and sentiment towards products, services, or brands. By analyzing vast amounts of data from social media, reviews, and forums, companies can extract valuable insights into customer preferences and trends. This information aids in strategic decision-making, helping businesses align their offerings with customer expectations and market demands. Lastly, in the realm of compliance and legal document analysis, text classification assists in reviewing and categorizing legal documents, contracts, and regulatory filings. This automation significantly reduces the manual effort and time required, ensuring that documents are compliant with relevant laws and regulations. It also facilitates easier retrieval and management of documents, streamlining legal processes and operations.
Text classification technology has advanced significantly, positioning it at a high Technology Readiness Level (TRL), generally between 8 and 9. This classification is due to its widespread operational use and the extensive validation it has undergone in various environments. The technical foundations contributing to this TRL include sophisticated natural language processing (NLP) algorithms and machine learning (ML) models that have been extensively trained on diverse datasets. These models have demonstrated high accuracy and reliability in categorizing text across numerous applications, from sentiment analysis in social media feeds to topic categorization in academic research. The development of deep learning techniques has further enhanced text classification, enabling the extraction of nuanced patterns and contexts from large textual datasets that were previously challenging to analyze. Additionally, the integration of text classification systems into commercial and research-oriented platforms has been thoroughly tested, showcasing their operational efficacy. The adaptability of these systems to various languages and contexts, coupled with ongoing improvements in computational efficiency and model interpretability, underscores their high TRL. This level reflects not just the technological maturity of text classification but also its proven utility and reliability in real-world applications.
In the Short-Term, advancements in text classification are expected to focus on improving model efficiency and accuracy. Immediate developments include the integration of more sophisticated natural language processing (NLP) algorithms that better understand context, nuance, and the subtleties of human language. Enhanced pre-trained models like BERT and GPT-3 will become more accessible, enabling businesses and researchers to achieve more accurate classifications with less computational cost and time. The Mid-Term phase will likely witness the rise of semi-supervised and unsupervised learning methods in text classification. These methods will reduce the reliance on large labeled datasets, which are costly and time-consuming to produce. Breakthroughs in algorithmic design will allow for more efficient data utilization, enabling models to learn from a mixture of labeled and unlabeled data, thereby improving the scalability and applicability of text classification across various domains. In the Long-Term, we anticipate the emergence of AI systems capable of continuous learning in text classification. These systems will adapt to new information without the need for retraining, closely mimicking human learning. The integration of cognitive computing technologies will enable these systems to understand, reason, learn, and interact in human-like ways, revolutionizing how we manage and interpret the vast amounts of text data generated daily. This evolution will pave the way for highly personalized and context-aware applications across industries, from healthcare to customer service.
Some interesting questions that has been asked about the results you have just received for Text Classification
What are related technologies to Text Classification?
Based on our calculations related technologies to Text Classification are Big Data, E-Health, Retail Tech, Artificial Intelligence & Machine Learning, E-Commerce
Which industries are mostly working on Text Classification?
The most represented industries which are working in Text Classification are IT, Software and Services, Other, Marketing Services, Education, Media and Entertainment
How does ensun find these Text Classification Companies?
ensun uses an advanced search and ranking system capable of sifting through millions of companies and hundreds of millions of products and services to identify suitable matches. This is achieved by leveraging cutting-edge technologies, including Artificial Intelligence.
