Text Annotation Services
Every label your NLP demands
Expert human text annotation across NER, sentiment, intent classification, relation extraction, coreference resolution, and RLHF preference labeling — in 40+ languages, with domain-expert annotators and 98.5% QA accuracy.
James Whitfield, Senior Partner at Alderman & Cross LLP, signed the agreement on 14 March 2024 at their Chicago office. The contract was unanimously approved by all parties and valued at $4.2 million.
Meridian Holdings expressed serious concerns regarding the liability clause. Their counsel, Dr. Priya Sharma, requested an amendment before the Q2 deadline.
Trusted by AI teams worldwide
50M+
Utterances annotated
98.5%
QA accuracy
40+
Languages
2K+
Domain expert annotators
48h
Pilot batch turnaround
Annotation types
Every text labeling task, done precisely
Each annotation task is handled by specialists trained on task-specific guidelines and QA rubrics — with inter-annotator agreement measured on every project.
01
Named Entity Recognition
Token and span-level entity annotation across standard types (PERSON, ORG, LOC, DATE, MONEY) and custom domain-specific taxonomies — for legal, medical, financial, and technical NLP.
02
Sentiment & Emotion Annotation
Document, sentence, and aspect-level sentiment polarity labeling plus fine-grained emotion classification — for product review analysis, social media monitoring, and CX intelligence models.
03
Intent & Slot Annotation
Utterance-level intent classification and slot-value pair extraction for task-oriented dialogue systems — covering multi-intent utterances, composite slots, and ambiguous query handling.
04
Text Classification
Single-label, multi-label, and hierarchical document classification — topic routing, content moderation, legal document typing, regulatory category assignment, and custom business taxonomies.
05
Question-Answer Pair Creation
Human-written question–answer pairs with context passages, span extraction answers, unanswerable question flags, and question type labels — for reading comprehension, RAG, and QA model training.
06
Instruction Tuning Data
Expert-crafted instruction–response pairs, chain-of-thought examples, and multi-turn dialogue datasets — domain-adapted for fine-tuning foundation models on enterprise reasoning tasks.
07
Music Annotation
Genre classification, tempo and beat detection, chord progression labeling, instrument identification, and mood tagging for music information retrieval and streaming AI systems.
08
Language & Dialect Identification
Segment-level language detection, accent classification, and code-switching boundary marking — for multilingual ASR routing, language-adaptive models, and dialect research.
09
Audio Quality Assessment
Signal-to-noise ratio scoring, clipping detection, reverberation flags, background noise classification, and overall usability ratings — for dataset filtering and quality control pipelines.
Use cases
Text annotation for every NLP application
From training foundational LLMs to fine-tuning domain classifiers — every text AI system depends on accurately labeled data. Synnth builds it.
LLM Fine-Tuning & Alignment
Instruction-response pairs, RLHF preference data, chain-of-thought annotations, and safety labels for fine-tuning and aligning large language models to enterprise tasks and human values.
Conversational AI & Chatbots
Intent classification, slot filling, dialogue act labeling, and multi-turn conversation datasets for customer service automation, virtual assistants, and task-oriented dialogue systems.
Legal AI & Contract Intelligence
Entity extraction, clause classification, obligation and right labeling, and relation annotation in legal documents — by annotators with legal professional training and jurisdiction awareness.
Clinical NLP & Healthcare AI
Medical entity recognition, clinical note classification, diagnosis-procedure relation extraction, and medication event annotation — by annotators with clinical knowledge, under HIPAA-ready protocols.
Financial NLP & FinTech AI
Earnings call sentiment, financial entity extraction, ESG classification, and risk event detection in financial text — annotated by professionals with financial markets domain knowledge.
Multilingual NLP & Translation
Cross-lingual NER, multilingual sentiment annotation, translation quality evaluation, and parallel corpus labeling — native-speaker annotators across 40+ languages, no machine translation.
Quality assurance
QA built for NLP precision
Text annotation has unique quality challenges — subjective tasks like sentiment, ambiguous entity boundaries, nested spans, and inter-annotator disagreement. Our QA pipeline is designed for all of them.
Inter-annotator agreement is measured on calibration samples for every project using Cohen’s kappa (two annotators) or Fleiss’ kappa (three or more). We target IAA above 0.80 on standard tasks and share scores with every delivery.
For subjective tasks like RLHF preference ranking and emotion labeling, we run annotator calibration sessions with shared examples and decision anchors before production work begins — ensuring consistent interpretation across the annotator cohort.
Domain-expert annotators
Legal text annotated by legal professionals. Medical records by clinicians. Financial documents by finance specialists. Domain knowledge is the difference between useful labels and noise.
Custom annotation guidelines
We build task-specific ontologies, edge-case decision trees, and labeling rubrics for your exact model requirements — not off-the-shelf templates that generate systematic errors on your domain's corner cases.
Multi-pass QA pipeline
Every annotation passes IAA measurement, automated consistency validation, and senior reviewer sign-off before delivery. Rejection rate, revision log, and annotator calibration stats in every QA report.
How it works
From text to production-ready labels
A transparent four-stage pipeline with IAA measurement and quality gates at every step — designed for NLP teams who need consistent, repeatable delivery at scale.
Define scope
Share your NLP task, label taxonomy, domain, language targets, and IAA requirements. We design custom annotation guidelines, edge-case decision trees, and calibration tests with your team.
Calibrate annotators
Domain-matched annotators complete calibration samples with gold-standard answers. IAA is measured before production begins. Annotators below threshold are re-trained or replaced.
Annotate & QA
Expert annotators label your text. Every batch passes IAA measurement on a random sample, automated consistency checks, and senior reviewer sign-off before it leaves our pipeline.
Deliver & iterate
Receive clean annotations in JSON, JSONL, CoNLL, BRAT, or your custom schema — with a full QA report including IAA scores, rejection rates, and revision log. Same annotator pool every batch.
Why Synnth
Built for teams where label quality is everything
What separates Synnth from generic text labeling platforms — especially for domain-specific, multilingual, and high-stakes NLP annotation tasks.
Domain-expert annotators
Legal text by legal professionals. Medical records by clinicians. Financial documents by finance specialists. We match annotator expertise to annotation task — the primary difference between useful labels and systematic noise.
200+ specialists
IAA-driven quality
Inter-annotator agreement is measured on every project — not spot-checked. We target IAA above 0.80 (Cohen’s kappa) with adjudication workflows for borderline cases, and share scores in every delivery report.
0.86 avg. IAA specialists
Native-speaker multilingual
Every language annotated by native speakers who understand idiomatic usage, domain-specific register, and regional variation — not bilingual workers approximating a second language.
40+ languages
Custom ontology design
We co-design entity taxonomies, relation schemas, and sentiment scales with your ML team — then build annotator training around your domain’s edge cases, not a generic rubric that produces systematic errors.
Enterprise security
All text encrypted at rest and in transit. GDPR compliant, HIPAA-ready for healthcare NLP. NDAs on every engagement. Proprietary documents and model outputs never leave our controlled, audited environments.
Fast pilot SLAs
Validate annotation quality before committing to full production volume. Pilot batches of up to 5,000 documents in 48 hours — with the same QA standards, annotators, and IAA targets used in production.
48h pilot delivery
Input & output formats
Delivered in the format your pipeline already expects
No conversion scripts. Annotations arrive clean and structured, ready for ingestion into your training infrastructure or annotation tooling.
Language coverage
40+ languages, native-speaker annotators for each
Multilingual NLP annotation requires native fluency — not literacy. Every language is annotated by people for whom it is a first language, including domain vocabulary and regional register.
FAQ
Common questions about text annotation
💡 Can’t find your answer here? Talk to our team — we typically respond within one business day.
What is text annotation and why does it matter for NLP?
Text annotation is the process of labeling written text with structured metadata — entity spans, sentiment scores, intent categories, relation types, preference rankings, or other attributes — to create training data for NLP models and LLMs. The quality, diversity, and precision of text annotations directly determines model accuracy on downstream tasks. Noisy or inconsistent labels introduce systematic errors that compound across millions of training examples.
How does Synnth measure and report annotation quality?
Quality is measured through inter-annotator agreement (IAA) on a statistically significant sample of every batch. For two-annotator tasks we use Cohen’s kappa; for three or more annotators we use Fleiss’ kappa. We target IAA above 0.80 on standard tasks. Disagreements above a threshold are adjudicated by a senior reviewer. Every delivery includes a QA report with per-task IAA scores, rejection rates, revision counts, and annotator calibration statistics.
Can Synnth annotate domain-specific text — legal, medical, financial?
Yes — and domain expertise is where Synnth most clearly outperforms generic annotation platforms. Legal documents are annotated by professionals with legal training who understand contractual terminology, obligation types, and jurisdiction-specific concepts. Medical text is annotated by clinicians. Financial documents by finance specialists. We staff annotation tasks based on domain match, not just language availability.
What is the difference between NER annotation and relation extraction?
Named Entity Recognition (NER) identifies and classifies specific spans of text as entities — people, organizations, locations, dates, monetary values, etc. Relation extraction goes further, identifying the semantic relationship between two or more entities in the text — for example, “works_at,” “acquired_by,” or “treats.” Both are supported by Synnth, individually or in a combined multi-task annotation workflow on the same documents.
How does Synnth handle annotation of highly ambiguous text?
Ambiguous cases are handled through three mechanisms: (1) detailed annotation guidelines with explicit decision rules for common ambiguous patterns; (2) calibration sessions where annotators align on edge-case examples before production begins; and (3) adjudication workflows where disagreements between annotators on the same item are resolved by a senior reviewer whose decision becomes the gold label. Ambiguous items are flagged in the delivery metadata so your ML team can handle them appropriately during training.
What formats are text annotations delivered in?
We deliver in your preferred format at no additional cost: JSON, JSONL, CoNLL-2003, BRAT standoff, Label Studio JSON, Prodigy JSONL, Doccano JSON, Hugging Face Datasets format, SQuAD JSON, CSV, TSV, or custom schemas aligned to your training pipeline. Output format is agreed during the scoping phase.
How is our proprietary text data kept secure during annotation?
All text is transferred through TLS-encrypted channels and stored with AES-256 encryption at rest. Annotation work is performed only within access-controlled, audited environments — annotators access documents through our secure platform and cannot export raw files. NDAs are signed on every engagement. For healthcare text, we operate HIPAA-ready workflows. For legal and financial text, we can work under strict data processing agreements with custom access controls.
What is the turnaround time for text annotation projects?
Pilot batches of up to 5,000 documents are typically delivered within 48–72 hours at full QA standards. For ongoing production runs, we agree velocity targets during scoping — annotation throughput depends on task complexity (NER is faster per document than relation extraction or RLHF preference ranking). We provide honest velocity estimates before commitment, not optimistic projections.
Get started
Start your text annotation project today
Tell us your NLP task, domain, label taxonomy, language targets, and volume. Our team responds within one business day with a scoping plan and no-obligation quote.
- info@synnth.com
- Mon–Fri, 9am–6pm IST
- Response within 1 business day
- No setup fees
- No setup fees
- NDA available on request
- Free pilot for qualifying projects