Text datasets become useful when language is consistently mapped to the concepts a model needs to learn. Entities, intents, relationships, spans, categories, attributes, and linguistic structure all require clear annotation rules and careful review. Josisoft builds text annotation workflows around your taxonomy, label definitions, edge cases, language requirements, and QA criteria so raw text becomes reliable structured ground truth.
Talk to a Data SpecialistAcme LogisticsORGANIZATION will open a new warehouse in KolkataLOCATION next monthDATE.
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Identification and span-level boundary annotation of standard and custom entities (persons, organizations, locations, products, numeric expressions), with grounding and disambiguation to external knowledge bases and client ontologies.
Semantic relation labeling between co-occurring entities within sentences or multi-paragraph texts, generating structured Subject-Predicate-Object triples for knowledge base construction and information extraction.
Joint labeling of user utterances across dialogue flows, assigning top-level domain intents alongside fine-grained semantic slot entities for task-oriented chatbots, virtual assistants, and IVR systems.
Fine-grained sentiment tagging linking polarity values (positive, negative, neutral) directly to specific entity attributes, feature aspects, and contextual opinion targets within customer reviews and feedback.
Hierarchical, single-label, and multi-label document categorization tagging topic taxonomies, subject matter, routing priority, and language registers across support tickets, corporate emails, and articles.
Directed clustering linking pronouns, noun phrases, aliases, and nominal mentions to their single shared referent entity across complex, multi-speaker, and multi-page texts.
Frame-based linguistic parsing mapping predicate-argument structures ("who did what to whom, when, where, and how") alongside Part-of-Speech (POS) tags and Universal Dependency syntax trees.
SQuAD-style span-extractive, multi-hop, and generative question-answer pair authoring tied to specific source passages to train retrieval-augmented generation (RAG) and machine reading comprehension models.
Pairwise sentence labeling categorizing semantic relationships into Entailment, Contradiction, or Neutral states, alongside graded Semantic Textual Similarity (STS) scoring for cross-encoders and embedding models.
Generation of human reference summaries (extractive and abstractive) paired with sentence-level faithfulness labeling, factual consistency verification, and hallucination detection against source documents.
Rigorous multi-label categorization tagging policy violations—including hate speech, harassment, profanity, violent extremism, self-harm cues, and brand-safety risks—against strict platform guidelines.
Span-accurate detection and replacement (redaction, hashing, or synthetic surrogate swapping) of Personally Identifiable Information (PII) and Protected Health Information (PHI) to ensure regulatory compliance prior to model training.
Specialized medical record annotation executed by qualified clinicians, mapping unstructured clinical notes, pathology reports, and discharge summaries to standardized ontologies (UMLS, ICD-10, SNOMED-CT, RxNorm).
Paralegal-vetted identification, classification, and extraction of standard and non-standard clauses—including indemnification, governing law, termination triggers, and confidentiality terms—across corporate agreements and regulatory filings.
Our language annotation teams can work directly inside client-approved text-labeling environments supporting span annotation, entities, classification, relationships, attributes, and review workflows, including Label Studio, SuperAnnotate, Kili Technology, or other approved systems.
Annotators can operate within client-owned NLP platforms through approved secure access, following your existing taxonomy, span rules, keyboard workflow, relation schema, language conventions, and review stages.
When no production annotation platform is available, we can configure controlled project workspaces around your text corpus, label taxonomy, entity definitions, relation schema, role permissions, and QA stages.
Annotate intents, entities, requests, topics, actions, and structured language signals across chat, support, assistant, and customer-service datasets.
Label product attributes, queries, categories, named entities, relevance signals, and semantic relationships across search and ecommerce text datasets.
Facility 04 opened in Kolkata.
Annotate entities, sections, concepts, relationships, and domain terminology across reports, records, internal documents, knowledge bases, and operational content.
Create structured text annotations across languages, scripts, regional variations, transliteration requirements, and client-defined multilingual taxonomies.
Every annotator, QA reviewer, and project manager signs an NDA before accessing project assets.
Personnel are trained on data confidentiality: strict restrictions on screen sharing, zero tolerance for screen recording or screenshots, and supervised session management.
On-premise operations at our central Durgapur facility enforce controlled local networks, restricted USB and removable media ports, and supervised work environments.
Each client is assigned a dedicated team working in siloed environments, preventing cross-project data contamination and maintaining domain context.
Share a representative text sample, label taxonomy, annotation guidelines, language requirements, and QA criteria with our delivery team. We will calibrate the schema, annotate a controlled pilot batch, review ambiguous language and edge cases, and return the sample for acceptance before production scaling.