Essential Work Samples for Evaluating Applied Natural Language Processing Skills

Applied Natural Language Processing (NLP) has become a cornerstone technology in today's data-driven world. From chatbots and virtual assistants to sentiment analysis and document classification, NLP specialists are in high demand across industries. However, evaluating a candidate's proficiency in this complex field requires more than just reviewing their resume or asking theoretical questions.

Traditional interviews often fail to reveal a candidate's true capabilities in applied NLP. While candidates may be able to discuss algorithms and techniques, their ability to implement practical solutions, handle real-world data challenges, and solve business problems through NLP requires hands-on evaluation. Work samples provide a window into how candidates approach problems, structure their solutions, and adapt to feedback—all critical skills for success in NLP roles.

The technical nature of NLP work demands evidence of both theoretical understanding and practical implementation skills. By observing candidates as they work through realistic NLP challenges, hiring managers can assess their coding proficiency, problem-solving approach, and familiarity with industry tools and frameworks. This practical demonstration reveals capabilities that might otherwise remain hidden in a traditional interview format.

Furthermore, NLP projects typically require collaboration, planning, and communication skills alongside technical expertise. Well-designed work samples can evaluate a candidate's ability to explain their approach, document their work, and translate technical concepts into business value—essential skills for any NLP practitioner working in cross-functional teams.

The following four work sample activities are designed to comprehensively evaluate a candidate's applied NLP skills. Each exercise targets different aspects of NLP work, from preprocessing and model building to deployment planning and problem-solving. By implementing these exercises in your hiring process, you'll gain deeper insights into which candidates possess the practical skills needed to succeed in NLP roles at your organization.

Activity #1: Text Classification Implementation

This activity evaluates a candidate's ability to build a practical text classification system—one of the most common NLP applications. Candidates will demonstrate their skills in text preprocessing, feature extraction, model selection, and evaluation. This exercise reveals their practical coding abilities, familiarity with NLP libraries, and understanding of classification techniques.

Directions for the Company:

• Prepare a dataset of 100-200 text samples with predefined categories (e.g., customer support tickets categorized by department, news articles by topic, or product reviews by sentiment).
• Divide the dataset into training and testing sets.
• Provide access to a development environment with common NLP libraries (NLTK, spaCy, scikit-learn, etc.) or allow candidates to use their preferred environment.
• Allocate 60-90 minutes for this exercise.
• Prepare evaluation criteria focusing on preprocessing techniques, feature selection, model choice, evaluation metrics, and code quality.

Directions for the Candidate:

• Build a text classification system that categorizes the provided text samples into their appropriate classes.
• Implement text preprocessing steps (tokenization, stopword removal, etc.) as you deem appropriate.
• Select and implement a suitable classification algorithm.
• Evaluate your model using appropriate metrics and be prepared to explain your choices.
• Document your approach, including preprocessing steps, feature extraction methods, model selection rationale, and potential improvements.
• Submit your code with comments explaining your implementation decisions.

Feedback Mechanism:

• After reviewing the candidate's solution, provide specific feedback on one strength (e.g., "Your preprocessing pipeline effectively handled special characters and maintained important contextual information") and one area for improvement (e.g., "Consider how you might handle class imbalance in the dataset").
• Give the candidate 15-20 minutes to implement the suggested improvement or explain how they would approach it if time doesn't permit implementation.
• Observe how receptive they are to feedback and their ability to adapt their approach.

Activity #2: Named Entity Recognition System Design

This activity assesses a candidate's ability to design and implement a named entity recognition (NER) system for a specific domain. NER is a fundamental NLP task with applications across industries, from healthcare to finance. This exercise evaluates the candidate's understanding of entity extraction techniques, domain adaptation, and practical implementation skills.

Directions for the Company:

• Prepare a domain-specific text corpus (e.g., medical reports, financial news, technical documentation) containing various entity types.
• Define 3-5 entity types relevant to the domain (e.g., for healthcare: medications, conditions, procedures).
• Provide access to NLP libraries and frameworks that support NER (spaCy, NLTK, Hugging Face transformers).
• Allocate 60-90 minutes for this exercise.
• Prepare evaluation criteria focusing on entity definition, extraction approach, handling of edge cases, and evaluation methodology.

Directions for the Candidate:

• Design and implement a named entity recognition system for the provided domain-specific text.
• Define how you would approach identifying and extracting the specified entity types.
• Implement a prototype that demonstrates your approach (this can be a partial implementation given time constraints).
• Explain how you would evaluate the system's performance and improve it over time.
• Document any domain-specific challenges you identify and how your approach addresses them.
• Be prepared to discuss how your solution could scale to handle larger volumes of text.

Feedback Mechanism:

• Provide feedback on one strength of their approach (e.g., "Your use of contextual embeddings effectively captures domain-specific entity patterns") and one area for improvement (e.g., "Consider how you might handle overlapping entities").
• Allow the candidate 15 minutes to refine their approach based on the feedback.
• Assess their ability to incorporate feedback and adapt their solution to address specific challenges.

Activity #3: Sentiment Analysis with Aspect Extraction

This activity evaluates a candidate's ability to implement a more advanced NLP task: sentiment analysis with aspect extraction. This exercise tests their understanding of sentiment analysis beyond simple positive/negative classification, requiring them to identify specific aspects or features mentioned in text and the associated sentiments.

Directions for the Company:

• Prepare a dataset of 50-100 product reviews or customer feedback entries.
• Provide a list of aspects or features to extract (e.g., for a restaurant: food quality, service, ambiance, price).
• Set up a development environment with necessary NLP libraries or allow candidates to use their preferred tools.
• Allocate 60-90 minutes for this exercise.
• Prepare evaluation criteria focusing on aspect identification accuracy, sentiment analysis nuance, and solution elegance.

Directions for the Candidate:

• Develop a system that identifies mentioned aspects in each review and determines the sentiment associated with each aspect.
• Implement preprocessing steps appropriate for this task.
• Choose and implement suitable techniques for both aspect extraction and sentiment analysis.
• Create a simple output format that clearly shows each identified aspect and its associated sentiment.
• Document your approach, including any assumptions made and techniques used.
• Discuss how your solution handles challenges like implicit aspects, negation, and mixed sentiments.

Feedback Mechanism:

• Provide feedback on one strength (e.g., "Your solution effectively handles negation patterns that reverse sentiment") and one area for improvement (e.g., "Consider how you might better identify implicit aspects that aren't directly mentioned").
• Give the candidate 15-20 minutes to implement a specific improvement based on your feedback.
• Evaluate their ability to refine their approach and address nuanced challenges in sentiment analysis.

Activity #4: NLP Pipeline Design and Planning

This activity assesses a candidate's ability to design a comprehensive NLP pipeline for a business problem. It evaluates their project planning skills, architectural thinking, and ability to translate business requirements into technical solutions—essential skills for senior NLP roles.

Directions for the Company:

• Prepare a realistic business scenario requiring an NLP solution (e.g., automating customer support triage, extracting insights from earnings calls, or analyzing clinical notes).
• Define key business requirements and constraints (e.g., accuracy requirements, processing speed, integration needs).
• Provide any relevant context about existing systems or data sources.
• Allocate 60-90 minutes for this exercise.
• Prepare evaluation criteria focusing on solution architecture, technology choices, implementation planning, and business alignment.

Directions for the Candidate:

• Design an end-to-end NLP pipeline to address the business problem.
• Create a system architecture diagram showing key components and data flows.
• Specify technologies, models, and approaches you would use for each component.
• Outline an implementation plan with key milestones and potential challenges.
• Explain how you would evaluate the system's performance against business requirements.
• Discuss how your solution balances technical sophistication with practical implementation concerns.
• Prepare to present your design and answer questions about your choices.

Feedback Mechanism:

• Provide feedback on one strength of their design (e.g., "Your phased implementation approach effectively balances quick wins with long-term sophistication") and one area for improvement (e.g., "Consider how you might handle multilingual requirements as the business expands").
• Give the candidate 15-20 minutes to refine their design based on the feedback.
• Assess their ability to adapt their plan while maintaining alignment with business objectives.

Frequently Asked Questions

How should we adapt these exercises for candidates with different experience levels?

For junior candidates, consider providing more structure and guidance in the exercises, such as starter code or more specific requirements. For senior candidates, introduce additional complexity like handling edge cases, scaling considerations, or more open-ended design challenges. The evaluation criteria should also be adjusted based on experience level, with more emphasis on fundamentals for junior roles and architectural thinking for senior positions.

Should candidates be allowed to use external resources during these exercises?

Yes, allowing candidates to use documentation, StackOverflow, and other resources mirrors real-world working conditions. This approach evaluates their research skills and familiarity with NLP resources. However, be clear about what's permitted—general documentation is fine, but directly copying complete solutions is not. Observing how candidates use resources can provide additional insights into their problem-solving approach.

How can we evaluate candidates who use different NLP frameworks or approaches?

Focus on evaluating the underlying principles and decisions rather than specific technology choices. A good NLP practitioner should be able to explain why they chose a particular approach and demonstrate understanding of its strengths and limitations. Prepare your evaluation team to recognize equivalent solutions across different frameworks (e.g., spaCy vs. NLTK vs. Hugging Face implementations).

What if we don't have domain-specific data for these exercises?

Public datasets can be used as alternatives. For text classification, consider datasets like Amazon product reviews or news article collections. For NER, the CoNLL-2003 dataset or domain-specific datasets from Kaggle can work well. The key is ensuring the exercise reflects the type of NLP work the role will involve, even if the specific domain differs.

How should we balance time constraints with comprehensive evaluation?

These exercises are designed to be completed within 60-90 minutes, but complex NLP tasks often take longer in real-world settings. Focus the evaluation on the candidate's approach, decision-making process, and code quality rather than expecting a complete, production-ready solution. Consider allowing candidates to explain what additional steps they would take given more time.

Should these exercises be conducted remotely or in-person?

Both approaches can work effectively. Remote exercises allow candidates to use their familiar development environment but require screen sharing and clear communication channels. In-person exercises facilitate direct observation and immediate feedback but may introduce additional stress. Choose the format that best aligns with your company's work environment and the role's requirements.

Applied Natural Language Processing is a rapidly evolving field that requires a unique combination of theoretical knowledge, practical implementation skills, and business acumen. By incorporating these work samples into your hiring process, you'll gain deeper insights into candidates' capabilities and identify those who can truly deliver value through NLP applications. Remember that the goal is not just to find candidates who can complete these exercises, but those who demonstrate thoughtful approaches, adaptability, and the ability to translate complex NLP concepts into business solutions.

For more resources to enhance your hiring process, check out Yardstick's AI Job Description Generator, AI Interview Question Generator, and AI Interview Guide Generator.

Build a complete interview guide for Applied NLP skills by signing up for a free Yardstick account