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
Our technology is based on 10 years of experience in the sector with clients 3 of the top5 NASDAQ companies.
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NER (Named Entity Recognition)

Named Entity Recognition (NER) is a fundamental component in the realm of natural language processing (NLP) that identifies and categorizes key information within text, such as names of people, organizations, locations, dates, and other entities. At Bitext, our NER services are specifically designed to enhance the capabilities of labeling platforms by providing precise and automated pre-labeling. This automation allows for the rapid and accurate identification of entities within large datasets, significantly reducing the manual effort required and increasing the overall efficiency of the annotation process. By integrating our NER tools, platforms can ensure that their AI models are trained with high-quality, contextually relevant data, leading to better performance and more reliable outcomes.

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.

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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
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POS Tagging

Part-of-Speech (POS) Tagging is an essential technique in natural language processing (NLP) that assigns grammatical categories, such as nouns, verbs, adjectives, and adverbs, to each word in a text. At Bitext, our POS Tagging services are designed to enhance the capabilities of labeling platforms by providing precise and automated pre-labeling. This service allows platforms to rapidly and accurately categorize words within large datasets, significantly reducing the manual effort required and improving the overall efficiency of the annotation process. By integrating our POS Tagging tools, platforms can ensure that their AI models are trained with high-quality, linguistically accurate data, leading to better performance and more reliable outcomes.

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.

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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
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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…

MADRID, SPAIN

Camino de las Huertas, 20, 28223 Pozuelo
Madrid, Spain

SAN FRANCISCO, USA

541 Jefferson Ave Ste 100, Redwood City
CA 94063, USA