NLP Labeling for AI
Training AI models requires large volumes of data, and this in turn requires increasing levels of precision and efficiency in data labeling. At Bitext, we offer advanced linguistic tools designed for automated pre-labeling of datasets to help scale Data Annotation and Labeling (DAL) projects. We offer a full range of solutions:
- Multilingual: up to 77 languages (English, Spanish, French, German, Italian, Portuguese, Arabic, Chinese, Japanese, Korean…)
- Multiple NLP functions: NER, Sentiment, POS Tagging, Anonymization / PII Detection, Intent Detection…
- As Software:
- Extremely efficient performance: multiplatform C libraries, 500,000 words per second w/ 8 CPUs
- Flexible deployment: as SDK or as API, both in cloud and on-premise
- As Data for GenAI Model Training:
- Rich data dictionaries: 80 Million tagged words in 77 languages
- Rich tagged corpora: 50 Billion tagged & categorized words in 77 languages
![ner-named-entity-recognition data-pre-annotation-tool-bitext](https://sp-ao.shortpixel.ai/client/to_auto,q_lossless,ret_img,w_900,h_900/https://www.bitext.com/wp-content/uploads/2024/07/ner-named-entity-recognition.png)
NER (Named Entity Recognition)
Example:
John lives in New York → “John” – person name, “New York” – place
Available As:
- Software
- SDK: 100KB/s of raw text per CPU core
- On-premise or SaaS API
- Data (Dictionaries)
- 50,000+ entities per language
- Annotated Corpora (for model training)
- From public domain sources
- Annotation of custom corpora available on demand
Features:
- 20 Entity types: person, place, company/brand, organization, phone, account number…
- Software and annotated corpora available in English, Spanish, French, German, Italian, Portuguese, Dutch…
- Dictionaries available in 77 languages
Topic-Based Sentiment Analysis
Topic-Based Sentiment Analysis is a powerful technique in natural language processing (NLP) that determines the sentiment expressed in text related to specific topics or themes. At Bitext, our Topic-Based Sentiment Analysis services are crafted to elevate labeling platforms by offering precise and automated pre-labeling capabilities. This service enables platforms to quickly and accurately assess sentiment in relation to particular topics within large datasets, minimizing the need for extensive manual annotation and enhancing the efficiency of the overall process. By incorporating our Topic-Based Sentiment Analysis tools, platforms can ensure that their AI models are trained with high-quality, context-aware data, resulting in improved performance and more insightful outcomes.
![topic-based-sentiment-analysis data-pre-annotation-tool-bitext](https://sp-ao.shortpixel.ai/client/to_auto,q_lossless,ret_img,w_1024,h_1024/https://www.bitext.com/wp-content/uploads/2024/07/topic-based-sentiment-analysis.png)
Example:
I hate my old phone → opinion: “hate” (negative), topic: “my old phone”
Available As:
- Software
- SDK: 50KB/s of raw text per CPU core
- On-premise or SaaS API
- Data (Dictionaries)
- 10,000 words and expressions labeled for sentiment (per language)
- Annotated Corpora (for model training)
- From public domain sources
- Annotation of custom corpora available on demand
Features:
- Returns the polarity, magnitude and corresponding topic of opinions in text
- Software and dictionaries available in Catalan, Dutch, English, French, German, Italian, Portuguese, Spanish
- Annotated corpora available in 22 languages and 10 variants
![pos-tagging data-pre-annotation-tool-bitext](https://sp-ao.shortpixel.ai/client/to_auto,q_lossless,ret_img,w_900,h_900/https://www.bitext.com/wp-content/uploads/2024/07/pos-tagging.png)
POS Tagging
Example:
John runs back home → “John” – proper noun (“John”), “runs” – verb (“run”), “back” – preposition (“back”), “home” – noun (“home”)
Available As:
- Software
- SDK: 400KB/s of raw text per CPU core
- On-premise or SaaS API
- Data (Dictionaries)
- 30,000+ root words and up to 20M inflected word forms per language
- Annotated Corpora (for model training)
- From public domain sources
- Annotation of custom corpora available on demand
Features:
- Returns the part of speech and lemma for each word in a sentence
- Software and corpora available in 21 languages
- Dictionaries available in 77 languages
Anonymization / PII Detection
Anonymization is a critical process in data management and natural language processing (NLP) that ensures sensitive information within text is protected by masking or removing identifiable details. At Bitext, our Anonymization services are specifically designed to enhance labeling platforms by providing precise and automated pre-labeling. This service allows platforms to efficiently identify and anonymize sensitive data within large datasets, reducing the manual effort involved and increasing the overall efficiency of the annotation process. By incorporating our Anonymization tools, platforms can ensure compliance with privacy regulations and protect personal information, while still training AI models with high-quality, contextually relevant data. This leads to improved performance and more secure outcomes.
![anonymization data-pre-annotation-tool-bitext](https://sp-ao.shortpixel.ai/client/to_auto,q_lossless,ret_img,w_900,h_900/https://www.bitext.com/wp-content/uploads/2024/07/anonymization.png)
Example:
My name is John and my account number is 1234567 → My name is XXXX and my account number is XXXX
Available As:
- Software
- SDK: 400KB/s of raw text per CPU core
- On-premise or SaaS API
- Annotated Corpora (for model training)
- From public domain sources
- Annotation of custom corpora available on demand
Features:
- Remove sensitive or personal information (PII) from text
- Software and corpora available in 25 languages
![ner-named-entity-recognition-rectan data-pre-annotation-tool-bitext](https://sp-ao.shortpixel.ai/client/to_auto,q_lossless,ret_img,w_589,h_788/https://www.bitext.com/wp-content/uploads/2024/07/ner-named-entity-recognition-rectan.png)
Deployment
Our NLP labeling tools are available as software (either an on-premise SDK or through our SaaS API), dictionaries or annotated corpora.
SDK – for maximum performance and scalability
- Performance:
- Throughput: 50KB/s to 400KB/s of raw text per CPU core (depending on the label type)
- Memory usage: 400MB
- Storage Footprint: 30MB (no additional dependencies)
- Portable: C/C++ library that can be called from any programming language (C/C++, Python, Java…) and any platform (Linux, macOS, Windows, iOS…)
API – for ease of integration
- On-premise API
- SaaS API
Additional Resources
We also offer additional NLP Resources including computational lexicons, synonym dictionaries, custom text corpora…
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MADRID, SPAIN
Camino de las Huertas, 20, 28223 Pozuelo
Madrid, Spain
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SAN FRANCISCO, USA
541 Jefferson Ave Ste 100, Redwood City
CA 94063, USA