Named Entity Recognition Experts in Berlin
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Meet FRATCH Experts in Berlin, who have recently used Named Entity Recognition
Josphat G.
Last position:
Data Annotation Lead at Sigma AI
- Lead a team of 15 annotators on large-scale computer vision projects for autonomous vehicle systems
- Developed comprehensive annotation guidelines that improved inter-annotator agreement by 35 percent
- Implemented quality control processes that reduced error rates by 42% across all projects
- Collaborated with ML engineers to identify edge cases and improve dataset quality
- Managed annotation projects for Fortune 500 clients, delivering 100% on time
Hamza K.
Last position:
Academic Research Contributor in Health Sector (Volunteer)
- Acted as technical consultant to optimize multi-layer ensemble models combining ResNet, CNN-BiGRU-Attention, and XGBoost.
- Guided implementation of a Logistic Regression meta-learner to solve class imbalance problems, achieving 92.86% accuracy and 0.9644 AUC on PTB-XL and Chapman-Shaoxing datasets.
Mathias W.
Last position:
Implementation of an on-premise OCR solution with information extraction at Mindhopper GmbH
- Insurance service provider*
Challenge: Business-critical documents were processed through external OCR providers, with ongoing costs, dependency, and data privacy risks for sensitive insurance data.
Implementation:
- Architecture and production implementation of an on-premise OCR solution with full data ownership
- Methods for recognizing document structures as the basis for automated further processing
- ML-, NLP-, and LLM/VLM-based information extraction, especially from invoices and quotations
Success: Replaced external providers: full data ownership, GDPR-compliant processing, and 75% lower recurring OCR costs per year
Used technologies: Python, Docker, Microservices, FastAPI, PyTorch, Torchvision, MongoDB, MySQL
Kashaf K.
Last position:
AI Consultant / Expert at Siemens Mobility
- Evaluated 45+ AI use cases and developed a prioritization framework for Siemens’ internal AI roadmap.
- Tested internal tools like DRIM, SiemensGPT, Microsoft Copilot; presented evaluation outcomes to stakeholders.
- Identified performance gaps and improved tool adoption by 65%.
- Supported AI knowledge-sharing initiatives, led tool onboarding sessions, and improved team AI literacy.
- Collaborated with engineering, procurement, and digital teams on tool feedback and strategy alignment.
Fiona S.
Last position:
Freelance at Flyworks
Ege P.
Last position:
AI Research Collaborator at NPO
- Contributed to the Karakutu project, developing AI-driven tools to analyze news in Turkey.
- Assisted in web scraping, applied NER for entity extraction, and built interactive filtering interfaces (Vue.js, Plotly.js) for entity and location based search.
- Performed sentiment and content-shift analysis to detect editorial influence in modified news articles.
Sanket T.
Last position:
Master of Engineering: Information and Electrical Engineering at Hochschule Wismar
Discover over 15,000 top freelancers
Statistics of experts using Named Entity Recognition
Aggregated from the professional profiles of matched freelancers.
Experience
10 years
Position duration
2.4 years
Positions per freelancer
8
Top business areas
Information Technology, Research and Development, Product Development
Top industries
Healthcare, Information Technology, Professional Services
Certification focus areas
Information Technology, Business Intelligence, Research and Development
Bachelor's degree or higher
100%
Master's degree or higher
71%
Doctorate
14%
Certifications per freelancer
3
Most common languages
German, English, Spanish
Speak two or more languages
100%
Based on our profile pool as of 19 Sep 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Berlin are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows how many freelancers charge within that range.
Average rates of experts in Berlin using Named Entity Recognition
Rates are based on recent contracts and do not include FRATCH margin.
The average daily rate is the mean of all daily rates from recent contracts of comparable freelancers on our platform.
The median daily rate is the middle value of all daily rates — half of comparable freelancers charge less, half charge more. Unlike the average, it is barely affected by outliers.
Calculated based on our freelancers’ daily rates as of 19 Sep 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Named Entity Recognition experts industry focus
See which industries our matched freelancers work in most often — every figure is calculated live from the freelancers on FRATCH.
- Healthcare (86%)
- Information Technology (86%)
- Professional Services (43%)
- Retail (43%)
- Advertising (29%)
- Automotive (29%)
- Education (29%)
- Banking and Finance (29%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What it does
Named Entity Recognition, often called NER or entity extraction, finds names, places, companies, dates, products, and other entities in text. It turns unstructured language into data that can be searched, filtered, linked, and analyzed. Companies use it in search, support, compliance, research, and document workflows.
Common uses
- Tag people, organizations, and locations in articles or documents
- Extract fields from contracts, emails, and reports
- Improve search, recommendations, and knowledge graphs
- Support chatbots, assistants, and workflow automation
Strong professionals also define entity schemas clearly, so the output fits the business case and not just the model.
Stack and methods
NER work often combines machine learning, language rules, and human review. Typical tools include spaCy, Hugging Face transformers, Stanford NER, and annotation tools for training data. Good specialists know when to use patterns, fine-tuning, or hybrid approaches instead of forcing one method everywhere.
When companies bring help
Freelance expertise is useful when a team needs to start a new extraction task, improve weak model quality, or move from proof of concept to production. It also helps when text is messy, entity types are domain-specific, or labels must be aligned across systems. In Berlin, this often comes up in multilingual product teams, media, legal tech, and AI search projects.
What strong experts do
- Design entity labels that match the real use case
- Build and clean training data with consistent annotation rules
- Evaluate false positives, misses, and boundary errors
- Integrate outputs into APIs, pipelines, and downstream apps
The best experts think beyond model scores. They care about language coverage, maintenance, latency, and how the results will be consumed.
What good delivery looks like
A solid NER setup is accurate, explainable, and easy to maintain. It should handle edge cases, ambiguous words, and domain terms without breaking existing text flows. For many teams, the real goal is not a model alone, but reliable entity data that fits into products and operations.
Frequently asked questions
Not sure where to start with Named Entity Recognition? These answers cover the essentials.
Named Entity Recognition is used to find people, organizations, locations, dates, products, and other meaningful terms in text. Companies use it to structure documents, improve search, automate data entry, and support analytics. It is especially useful when large amounts of text need to be turned into clean fields.
NER is more precise because it identifies entity types, not just repeated terms. Keyword extraction can show what a text is about, but NER tells you which parts are names, places, or companies. For many business workflows, that distinction is what makes automation reliable.
A strong Named Entity Recognition specialist usually understands annotation design, text preprocessing, evaluation, and model tuning. In many projects, Python, spaCy, transformer models, and API integration are also important. Domain knowledge matters too, because legal, media, and product data all use language differently.
You do not need a finished data science plan, but you should know what entities matter and where the text comes from. A good NER expert can help define labels, review sample documents, and shape the workflow. The clearer the target output, the faster the work can move.
Most Named Entity Recognition work can be done remotely because it depends on text, datasets, and feedback loops. On-site sessions can still help when sensitive documents, stakeholder workshops, or annotation rules need close alignment. In Berlin, many teams use a mixed setup with short in-person reviews and remote delivery.
Look at whether the extracted entities match the business meaning, not only the model score. A good NER deliverable should handle boundaries correctly, avoid obvious false positives, and stay consistent across similar texts. Human review on real examples is often the fastest way to spot weaknesses.
No. Named Entity Recognition finds and labels entities in text, while entity linking connects those entities to a specific record or knowledge base. Information extraction is broader and can include relations, events, and rules beyond entity detection.
Named Entity Recognition works well on support tickets, contracts, emails, news text, product catalogs, and knowledge base content. The best projects have enough examples of the target language and enough repeatable patterns for training or rule design. Clean source text and clear entity definitions make a big difference.
The average hourly rate of freelancers in Berlin, Germany who have used Named Entity Recognition in their recent projects is 71 €, which corresponds to a daily rate of about 571 € based on an 8-hour working day.
Of the freelancers in Berlin, Germany who have used Named Entity Recognition in their recent projects, 100% hold at least a Bachelor's degree, 71% hold at least a Master's degree, and 14% hold a doctorate.
On average, freelancers in Berlin, Germany who have used Named Entity Recognition in their recent projects have 10 years of professional experience, with a single engagement typically lasting around 2.4 years.
The most common languages among freelancers in Berlin, Germany who have used Named Entity Recognition in their recent projects are German (100%), English (100%), and Spanish (14%).
The most common industries among freelancers in Berlin, Germany who have used Named Entity Recognition in their recent projects are Healthcare (86%), Information Technology (86%), and Professional Services (43%).
The most common business areas among freelancers in Berlin, Germany who have used Named Entity Recognition in their recent projects are Information Technology (100%), Research and Development (86%), and Product Development (71%).
Main locations of FRATCH Experts, who have recently used Named Entity Recognition
Our freelancers and interim experts are at home across the DACH region — available on-site in the major business hubs or fully remote. Choose a location to discover matched specialists, local market insights and up-to-date availability.
Countries:
Berlin 
Hamburg Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg Request a free demo
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Philipp Thomaschewski
FRATCH CEO
