High-accuracy text labeling for named entity recognition, intent classification, sentiment analysis, relation extraction, and more. AI pre-labeling cuts annotation time by 40% — human experts validate and correct everything.
NLP annotation is the process of adding structured labels to raw text so that machine learning models can learn linguistic patterns. Every search engine, chatbot, document processor, and voice assistant relies on millions of carefully annotated text examples to understand what human language means.
When your NLP model reads a sentence like "Dr. Arora at Apollo Hospital prescribed metformin for diabetes management," it needs to have learned — from thousands of labeled examples — that "Dr. Arora" is a PERSON, "Apollo Hospital" is an ORGANISATION, "metformin" is a MEDICATION, and "diabetes" is a CONDITION. That learning comes entirely from annotation.
The challenge is that NLP annotation is deceptively difficult. For a general-purpose model, "Apple" can be a fruit, a company, or a person's name — and only context determines which. For a medical model, understanding whether "cold" is a symptom, a temperature, or a descriptor requires clinical knowledge that a non-expert annotator simply does not have.
Concave AI's NLP annotation combines an AI pre-labeling layer (which handles the clear, unambiguous cases quickly) with domain-specialist human annotators who handle every ambiguous case, entity boundary decision, and domain-specific judgment call. The result is datasets with the speed advantage of AI pre-labeling and the accuracy guarantee of expert human review.
NLP annotation requires human linguists who understand pragmatics, irony, and domain-specific terminology. Our annotators are trained on your domain before touching a single label.
Get a Free Audit →Domain-specialist annotators tag entities across legal, medical, financial, and news corpora — building training sets for production NER models.
Rajesh Kumar Mehta, former CFO of Infotech Ventures Ltd., was found by SEBI to have made undisclosed trades on March 14, 2023 prior to the merger announcement with Bharti Digital Solutions. The total gain was estimated at ₹4.2 crore.
The order, issued from the SEBI Mumbai Regional Office, imposes a ₹1.8 crore penalty and bars Mehta from securities markets for 3 years effective 01 April 2024.
Counsel Adv. Sunita Rao of Rao & Pillai Associates, New Delhi, filed an appeal at the Securities Appellate Tribunal citing procedural violations under Regulation 4(2)(g).
We do not use a one-size-fits-all annotator pool. Each task type is matched to the appropriate domain expert to ensure annotation accuracy exceeds what guidelines alone can achieve.
Our NLP annotation pipeline is built to prevent the two most common failure modes: annotator inconsistency and domain knowledge gaps. Both are addressed structurally, not just through guidelines.
NLP annotation is priced per document based on task complexity, document length, and domain. Volume discounts apply at 5,000+ documents.
Request a Custom Quote →Send us 100 text samples from your domain. We will annotate them with our expert team and return a kappa report and label distribution analysis — at no cost.