Bitext NLP+NLG Data Services for GenAI

Bitext provides a wide range of data services and products: from off-the-shelf datasets –including manually curated resources like morphological dictionaries in 70+ languages and synthetically generated conversational datasets; to custom annotation both for NLP (entities, sentiment, topic…) and GenAI (model finetuning, RAG…) tasks.

Automating Data Annotation and Data Generation for Gen AI

Bitext automatically annotates and generates NLP data for and AI/ML applications, both for
training and for evaluation.

Our unique differentiator: we automate all processes, using our NLP technology to annotate
data and NLG technology to produce Synthetic Training Data.

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DAL: Automation Tools for Data Annotation and Labelling

We provide custom Data Annotation and Labeling (DAL) services for (Generative) AI. We focus on the automation of human annotation, building custom Human-in-the-loop (HITL) pipelines to improve data annotation speed and quality with custom software applications. A few examples:

 

  • We use custom and proprietary data sources of linguistic knowledge like ontologies or morphological dictionaries
  • We use NLP tools, like entity detection or sentiment annotation, to pre-annotate the data for human annotators
  • We train AI models to perform pre-annotation tasks so human annotators are relieved from mechanical tasks

NLP: Text Annotation Tools for NLP Tasks in 70+ Languages

Bitext provides core linguistic tools to automatically pre-annotate custom corpora & datasets:

 

  • Lemma, POS and morphological attributes
  • Named Entities like Person Name, Last Name, Company, etc.
  • Key Phrases or Constituents
  • Topic-Level sentiment analysis
  • Offensive language
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NLP: Lexical and Semantic Data in 70+ Languages

Core linguistic data for any NLP application: Lexical Data and Semantic Data

Lexical Data:

Bitext produces lexical dictionaries that contain detailed information like POS, morphological
attributes, frequency in corpora, and more

Bitext has produced these dictionaries for 77 languages (including Indian and Asian languages)
and 25 language variants (including 6 variants for Spanish, Canadian French, etc.)
.

These dictionaries are used for a wide range of use cases:

 

  • Lemmatization for search and indexing
  • Lemmatization for topic modelling
  • Spelling and grammar checking
  • Key phrase extraction
  • Corpus annotation

Semantic Data: 

Bitext produces synonym dictionaries both for general purposes (complementing WordNet) and for specific verticals like Finance, Human Resources, and Legal.

All synonyms include linguistic attributes like POS, inflected forms, frequency in Bitext in general, and vertical-specific corpora.

NLG: Synthetic Text Generation Tools to generate custom datasets

Currently focused on assistants/chatbots, Bitext NLG toolset generates custom training and
eval datasets for your chatbots.

These datasets are annotated for:

  • Language register (colloquial, formal, etc.)
  • Offensive language
  • Syntactic complexity
  • Spelling and grammar checking

We also tag speech/voice transcription errors (customized for different ASR engines) and other linguistic features like lemma, POS, morphological attributes, entities, and more

Pre-Built Datasets to train and evaluate your assistant/chatbot

Bitext has produced different vertical datasets to instantly train and evaluate your bot

These datasets are already tagged with:

 

  • Language register (colloquial, formal…)
  • Offensive language
  • Syntactic complexity…
  • Spelling and grammar checking
  • Speech/voice transcription errors (customized for different ASR engines)
  • Linguistic features like lemma, POS, morphological attributes, entities, and many more

Custom Tagging

The main focus of Bitext’s recent consulting projects has been the generation of instructional prompts for AI models. These projects typically involve training models that can answer questions by extracting information from financial reports and tables as well as by performing calculations. Given an answer, we generate a prompt that is linguistically correct, as well as the set of calculation steps to reach the answer – both the questions and the steps are validated by financial experts for accuracy and relevance. 

 

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Most of our projects have three ingredients:

    • Linguistic structure: our linguists prepare the necessary linguistic data and tools 
    • Scientific/specialist structure: our linguists work with subject-matter experts, typically to create ontologies to map domain knowledge
    • Tagging/annotation: our software generates tagged data by combining linguistic data and ontologies. Then, subject matter experts validate the output

Use Cases

As an example, we’ve worked on projects with medical/healthcare experts to validate the creation and annotation of linguistic resources, incorporating knowledge such as semantic equivalence between texts and paraphrasing.
We have also worked in multiple languages (German, French, Japanese, etc.) and language variants.

MADRID, SPAIN

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

SAN FRANCISCO, USA

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