Navigating data preparation for natural language processing applications comes with its fair share of complexity. With such a wide variety of tasks that fall under NLP, developing selection criteria can help accelerate and improve project outcomes. The path to production-ready NLP models depends on matching your technical requirements with the right combination of tools and expertise.
Key Selection Criteria for NLP Data Labeling Solutions
When evaluating data labeling solutions, consider these critical factors:
Security Requirements
Organizations handling proprietary legal documents, medical records, or financial data need providers with secure facilities, data encryption, access controls, and relevant compliance certifications.
Domain Expertise
Simple tasks, such as sentiment analysis, may only require reading comprehension, while specialized applications demand annotators with deep domain knowledge in medical terminology, legal concepts, or technical jargon.
Data Volume and Scale
Small pilot projects may work with crowdsourced solutions, but enterprise applications requiring millions of data points need professional services that deliver consistent quality at scale.
Annotation Complexity
As requirements grow complex, tasks involving entity relationships, intent classification, or contextual understanding benefit from professional annotators who apply consistent judgment.
NLP Data Labeling Use Case Examples
Different NLP applications present unique challenges that inform the optimal labeling strategy.
Question Answering Engines
Question answering (QA) is an exciting subfield of NLP focused on building models designed to facilitate information retrieval around a specific topic or set of topics. QA systems can be used as the backend for a virtual assistant tool on a travel website, allowing users to ask a variety of questions about available flights. The system can then respond with relevant information that helps users coordinate their booking.
The best QA models leverage some form of deep learning and, therefore, require access to very large datasets to be trained adequately. A fantastic dataset for question answering is the Stanford Question Answering Dataset (SQuAD), which contains a set of 100,000 questions based on Wikipedia articles, with answers embedded within the texts corresponding to the questions asked.
For QA engines, a crowdsourced data labeling solution can be perfectly adequate, with SQuAD serving as a perfect example of that. These questions were both posed and answered by crowdsourced workers, and could be done so adequately as the questions only required English reading comprehension abilities to answer correctly.
If the guidelines needed to generate a valid dataset are complicated, professional data annotation is typically the only answer, which can be done in-house (budget permitted) or by an annotation service. For example, if a QA system is being designed to answer a researcher’s highly specialized questions, crowdsourcing will fail to meet this demand as annotators lack the expertise required for accurate annotation.
NLP for Law
NLP has seen increasing adoption within the legal industry as firms leverage it to:
- Perform legal research
- Conduct electronic discovery
- Automate contract review
- Draft & analyze legal documents
- Predict rulings
Security is the biggest challenge for annotating data for use in a legal AI model. Legal documentation demands the highest security due to the proprietary nature of the document’s content. To adequately protect the confidentiality of the documentation, the work must be annotated by a professional service that can guarantee the security of any documents shared when creating and annotating the datasets.
There’s also the issue of legal expertise. Legal documents are very structured and dense; it’s best to have a legal expert annotate and label them. Only individuals with a law degree or who have worked in the law in some capacity (clerk, secretary, paralegal, etc) will have the ability to sift through complicated legal terminology and understand it.
Medical NLP and Computer Vision Integration
Medicine is another key area where NLP is increasingly finding new applications, particularly in the exam room. Doctors using speech-to-text models packaged in dictation software can easily record clinical notes verbally, allowing them to focus more on their patients while speeding up exam time.
Training an NLP system around the medical lexicon naturally demands a certain level of subject matter expertise. Correctly identifying and transcribing terms like “appendicitis” or “cephalexin” when annotating a dataset requires strong English fluency with some medical background and knowledge. This is why it’s best to source data labelers from a professional service that guarantees a high level of subject matter expertise.
The medical field is also seeing a number of scenarios in which NLP and computer vision (CV) models are combined. Because NLP and CV models typically require lots of data to train, the crowdsourcing route typically wins out, as it can result in huge cost savings. This is the best choice for simpler use cases.
However, imagine a scenario in which a medical team is using diagnostic radiology images such as X-rays, MRIs, and CT Scans to identify potential sources of cancer in patients. Suppose also that the model is twofold: there is a CV model which identifies the suspicious tumor or lesion in the radiology image, and then an NLP model which takes that selected subset and labels it with text specifying the tumor location and type. For example, it might take in an image of the brain and label it as “Glioblastoma located in the prefrontal cortex”.
In this scenario, medical expertise is critical on behalf of the data labelers, as differentiating between tumor subtypes can be challenging, even for highly trained doctors. Annotators will also require familiarity with the medical terms being used for the NLP portion. In such a circumstance, a specialized data labeling service is your best bet.
Limitations of Auto-Labeling Tools
Automated annotation technology has made impressive strides, but it comes with limitations that can impact project success.
Auto-labeling tools can accelerate initial annotation phases, but they consistently require human oversight to maintain quality standards. Research shows 42% of automated data labeling requires human correction. Organizations often struggle to find robust solutions that handle complex annotation requirements, which explains why 45% of companies have used four or more different annotation tools in the last year.
Automated systems particularly struggle with edge cases. These rare occurrences require contextual understanding and nuanced judgment that automated tools cannot consistently provide. Human intelligence remains essential for identifying and solving the edge cases that make or break commercial NLP applications.
How to Choose the Right Solution for Your NLP Project
Begin with a Security Assessment
Applications involving sensitive data require professional annotation services with secure, monitored facilities. Look for providers who can guarantee confidentiality for legal documents, medical records, and proprietary business information through strict access controls and data protection protocols.
Match Annotator Expertise to Task Complexity
Basic tasks like named entity recognition (NER) or general sentiment analysis may work with crowdsourced solutions. Specialized domains such as medical terminology, legal language, or technical documentation require professional services with linguists and domain experts who understand context and nuance. The best providers maintain dedicated workforces with verifiable expertise rather than relying on gig workers.
Evaluate Technology and Human Expertise Integration
The most effective solutions combine domain-trained annotators with purpose-built platforms that streamline workflows. Look for providers offering custom annotation tooling, automated pre-labeling capabilities, and workflow automation that allows experts to focus on complex judgments while technology handles routine tasks.
Prioritize Comprehensive Quality Control
Effective solutions implement multi-annotator review systems, real-time issue resolution, and performance benchmarking. The best providers offer dedicated project management, customizable annotation guidelines, sample labels for training consistency, and detailed analytics to track annotator performance and identify improvement areas throughout the project lifecycle.
Why Partner with iMerit for NLP Data Labeling
iMerit brings over 20 million annotations of expertise through data labeling services for NLP that help customers extract business insights and build next-generation conversational technology. Our linguistic experts and domain specialists provide named entity recognition, sentiment analysis, audio and text transcription, and intent analysis to power chatbots, digital assistants, and conversational AI products across retail, finance, and healthcare.
iMerit’s secure and monitored facilities offer reliable solutions for sensitive data requiring strict security protocols. Our experienced teams, custom tooling capabilities, and dedicated project management ensure your NLP project receives the edge case insights and quality control necessary for production-ready models.
If your business utilizes NLP in a highly specialized domain where data security and annotation accuracy are crucial, contact iMerit today to speak with an annotation expert.
