10 Natural language processing (NLTK) Interview Questions and Answers for python engineers

flat art illustration of a python engineer

1. What experience do you have working with NLTK?

During my time at XYZ Company, I worked extensively with the Natural Language Toolkit (NLTK) to develop a sentiment analysis model for customer reviews.

  • First, I gathered a dataset of 10,000 customer reviews from various sources, including social media, online forums, and customer service chats.
  • Then, I preprocessed the data using NLTK's tokenization, stop word removal, and stemming functions.
  • Next, I trained a machine learning algorithm using NLTK's Naive Bayes implementation and achieved an accuracy score of 87% on a test set.
  • I also utilized NLTK's named entity recognition function to identify and extract important keywords from the reviews, which helped improve the overall accuracy of the sentiment analysis model.

Overall, my experience working with NLTK has allowed me to develop a strong understanding of natural language processing techniques and their practical applications in the field of data analysis.

2. What natural language processing tasks have you worked on before?

As a natural language processing (NLP) specialist, I have worked on a variety of tasks that involve analyzing and processing human language. Some of the most prominent tasks I have worked on include:

  1. Named-entity recognition: I developed a model that identifies and extracts named entities from news articles with an accuracy of 94%.
  2. Sentiment analysis: I created a sentiment analysis tool that analyzes social media posts and reviews to determine the sentiment expressed by users. The tool achieves an accuracy of 86%.
  3. Document classification: I developed a model that automatically categorizes customer support tickets based on the type of issue they address. The model achieved an accuracy of 92%.
  4. Part-of-speech tagging: I built a model that tags words in a sentence with their respective parts of speech. The model has an accuracy of 95% on English text.
  5. Topic modeling: I developed a topic modeling algorithm that identifies topics in a collection of news articles. The algorithm achieves a coherence score of 0.8 on a scale of 0 to 1.

Overall, I have extensive experience working with NLP tools and techniques, and I am always eager to explore new applications and solve new challenges in this field.

3. Can you explain the difference between Stemming and Lemmatization?

Difference between Stemming and Lemmatization:

Stemming and lemmatization are two common techniques used in natural language processing. Both techniques aim to simplify words by reducing them to their base form. However, there are some differences between these two techniques. Let us take a closer look at them.

  • Stemming: Stemming is a technique that reduces a word to its root form by removing any suffixes. For example, if we stem the word "running," we would get "run." Stemmers do not consider the context of the word, and thus, some stemmed words might not be real words. This process is faster compared to lemmatization.

  • Lemmatization: Lemmatization is a technique that reduces a word to its base form by considering its context and morphological analysis. It maps different forms of a word, such as "running," "ran," and "runs," to its base form "run." Lemmatizers return real words, which makes this technique slower than stemming.

For example, let's consider the following sentence:

"The cats were playing with the mice"

If we stem this sentence using Porter Stemming, we would get:

"the cat were play with the mic"

If we lemmatize the same sentence, we would get:

"the cat be play with the mouse"

As we can see, stemming has resulted in some non-words, whereas lemmatization has given us real words. Therefore, it is important to choose a suitable technique based on the task and the data we are working on.

4. Have you worked with Part-of-speech tagging before? If so, can you give an example of how you implemented it into a project?

Yes, I have worked with Part-of-speech (POS) tagging before. In one project I worked on, we were building a sentiment analysis tool for customer product reviews. To accurately predict the sentiment of a review, we needed to extract the relevant features and sentiments expressed in it.

We utilized the NLTK library to perform POS tagging on the review texts. We first tokenized the texts and then tagged each token with their corresponding POS. We used the 'pos_tag' function provided by NLTK which applies an algorithm to each token to determine its POS.

Once we had the POS tagging done, we could extract features and sentiments more accurately. For example, we could extract all adjectives that were used to describe a product and use them to gauge its positive or negative sentiment. We also used POS tagging to extract nouns that referred to specific products or services and used them to provide recommendations to our clients to improve certain aspects of their products.

Our results showed an overall improvement in the sentiment analysis accuracy by 10% compared to our previous implementation that did not incorporate POS tagging.

5. What resources have you found useful in your NLP work?

When it comes to NLP work, staying up-to-date with the latest developments and best practices is critical. To ensure I'm always in the know, I've found the following resources to be incredibly valuable:

  1. Research papers: There are countless papers published every year on various topics within NLP, so staying current with the latest research is essential. I often refer to ACL Anthology and Arxiv.org to keep myself up-to-date. For instance, I recently read an article in ACL Anthology entitled "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding". The article helped me gain a deep understanding of how pre-training language models can drastically improve NLP tasks.
  2. Blogs: Several blogs regularly publish industry-leading insights on the latest language technological advancements. I particularly enjoy following the NLTK documentation and community as it is comprehensive and well-organized. They offer an extensive collection of recipes and examples implementation useful for development. Recently, I read an article entitled "How to solve 90% of NLP problems: a step-by-step guide". The post highlighted useful NLP libraries that can solve some of the most common NLP problems leading to faster deployment of ML models in production.
  3. Online courses: Taking online courses is a fantastic way to deepen my understanding of NLP concepts, and I have found Coursera and Udemy courses to be particularly useful. For example, a course I recently completed on "Natural Language Processing in Tensorflow" by Laurence Moroney was beneficial to my work. During the course, I was exposed to several fundamental techniques for analyzing and structuring textual data in a machine-readable language.
  4. Conferences and meetups: Attending industry conferences and meetups is a great way to network with other NLP experts and expand my skill set. I recently attended the NLP Summit where I met several industry experts and gained insights into how to optimize ML workflows for NLP applications by 35%.

By leveraging these resources, I have been able to stay at the forefront of NLP and continually improve my skills, resulting in more accurate models and higher-quality customer analyses.

6. How do you handle ambiguity and variance in natural language processing?

When handling ambiguity and variance in natural language processing, I use a combination of techniques to ensure accurate results. Firstly, I implement rule-based approaches to constrain the possibilities of interpretation. This involves creating a set of rules to filter out irrelevant or unlikely options. For example, if a sentence contains the words "apple" and "orange," a rule may specify that the sentence must refer to fruit, rather than technology or colors.

Secondly, I utilize machine learning techniques to handle the more complex cases of ambiguity and variance. This involves training models with large amounts of data to recognize patterns and make informed predictions. For example, I trained a model to classify movie reviews as positive or negative based on the language used. After testing the model on a validation set, it achieved an accuracy of 85%.

Another approach I use is to incorporate human feedback into the system. By collecting feedback from humans on certain phrases or sentences, I can analyze the patterns in their responses and adjust the algorithms accordingly. For instance, I conducted a survey to gauge the sentiment of five different variations of the sentence "I love my job." The results showed that the phrase "I absolutely adore my job" was consistently interpreted as the most positive.

  • Rule-based approaches to constrain the possibilities of interpretation
  • Machine learning techniques trained with large amounts of data to recognize patterns and make informed predictions
  • Incorporating human feedback into the system to analyze patterns and adjust algorithms accordingly

Using these techniques has allowed me to handle ambiguity and variance in a way that produces accurate results. For instance, when working on a project analyzing customer feedback for a restaurant chain, my system accurately classified 95% of the feedback as either positive or negative, allowing the company to make informed decisions on how to improve their services.

7. Can you describe what a stop word is and how you handle them in natural language processing?

Stop words refer to words which are commonly used in a language and are usually removed from natural language processing applications as they do not have any impact on the meaning or context of a sentence. Examples of stop words in English include "the", "and", "a", "in", etc. In natural language processing, I handle stop words by firstly identifying them through the use of NLTK libraries. I would then remove them from the text prior to analysis. This is because they can cause noise in the data and reduce the accuracy of any results. For example, if I were analyzing a collection of job descriptions and wanted to determine the most in-demand skills, removing stop words like "the" and "a" would allow me to more accurately analyze the frequency of the job skills. Additionally, in some instances where stop words may be useful, such as sentiment analysis, I would not remove them but rather utilize them to accurately represent the tone or emotion in the text. In a recent project where I was analyzing customer reviews for a company, removing stop words increased the accuracy of the sentiment analysis by approximately 10%. This resulted in a more precise understanding of customer sentiment towards the company.

8. Have you worked with named-entity recognition before? If so, could you give an example of how you implemented it into a project?

Yes, I have worked with named-entity recognition before. In a previous project, I worked on a sentiment analysis tool that classified movie reviews as positive, negative, or neutral. To improve the accuracy of the tool, I implemented named-entity recognition to identify and classify the names of actors, directors, and movies mentioned in the reviews.

  1. First, I used NLTK's built-in named-entity recognition tool to identify and tag named entities in the text.
  2. Next, I used regular expressions to determine which named entities were actors, directors, or movies.
  3. Then, I created a set of rules to classify the sentiment of the review based on the presence and sentiment of the named entities.
  4. Finally, I trained and tested the model on a dataset of movie reviews to evaluate its accuracy.

The implementation of named-entity recognition improved the overall accuracy of the sentiment analysis tool by 10%, resulting in an accuracy rate of 85%.

9. What challenges have you faced while working on an NLP project?

One of the biggest challenges I faced while working on an NLP project was dealing with data noise. In one project, we were tasked with predicting sentiment analysis for customer reviews of a popular product. However, we found that many of the reviews contained irrelevant text, such as the user's purchase history or details about shipping that had nothing to do with the product itself.

To solve this problem, we first tried manually cleaning the data, but this was time-consuming and not scalable. We then decided to use text preprocessing techniques such as stopword removal, stemming, and lemmatization. However, these techniques had their own challenges as they often removed important words from the reviews, which affected the overall accuracy of our model.

We eventually settled on using a combination of techniques and were able to increase the accuracy of our sentiment analysis model from 70% to 90%. This was achieved by using a combination of stopword removal, stemming, and lemmatization, along with a custom algorithm to identify and remove irrelevant text.

Another challenge we faced was dealing with different languages. Many of the reviews we received were in languages other than English, such as Spanish, French, Chinese, and Arabic. We solved this problem by using a combination of translation APIs and customized language models. This allowed us to easily translate the foreign language reviews into English and apply our sentiment analysis model to them.

  • Combating data noise through preprocessing techniques and developing custom algorithms.
  • Finding a solution to process information from different languages through translating API's.

10. How do you determine which algorithms to use for a specific task in NLP?

When determining which algorithm to use for a specific task in NLP, I always start by understanding the requirements and constraints of the task at hand.

  1. I begin by preprocessing the data, such as tokenization, stemming or lemmatization, and identifying the language used, in order to determine the appropriate input representation for the algorithm.

  2. Next, I consider the type of NLP task that needs to be performed such as sentiment analysis, named entity recognition or machine translation.

  3. Based on the type of task, I select an algorithm that has previously shown good performance on that specific task. For example, when working on sentiment analysis, I could use a Naive Bayes or a Support Vector Machine algorithm.

  4. After selecting the algorithm, I experiment with different hyperparameters to optimize the model's performance. For instance, in case of using an SVM, I could experiment with different kernel functions, such as linear or polynomial kernels.

  5. I evaluate the selected algorithm using various metrics such as precision, recall, or F1 score, to determine its performance on the given task. Based on the results, I decide whether to use the same algorithm or switch to a different one.

  6. In some cases, I may also consider using an ensemble of algorithms to achieve higher accuracy. For example, for a named entity recognition task, I could combine the output of a rule-based algorithm with that of a Neural Network based algorithm.

  7. Finally, I ensure that the chosen algorithm can be scalable and computationally efficient, should it be necessary to apply it to large datasets.

Using this systematic approach, I was able to achieve an F1 score of 95% on sentiment analysis of customer reviews, and a precision of 92% for named entity recognition of a dataset of research articles.


By now, you've prepared yourself with the essential knowledge to ace your Natural Language Processing interview. However, before you apply for your dream job, don't forget to write an outstanding cover letter that showcases your skills and passion for the role. Our guide on writing a cover letter for Python Engineers provides a step-by-step approach that will help you stand out from other candidates. Additionally, having an impressive CV is equally important. Our guide on how to write a resume for Python Engineers will help you highlight your relevant experience and achievements. Finally, now that you've honed your skills, feel free to check out Remote Rocketship's backend developer job board, where you can find a list of remote Python engineer jobs waiting for you! Good luck and happy job hunting!

Find remote Python engineer jobs here

Looking for a remote tech job? Search our job board for 30,000+ remote jobs
Search Remote Jobs
Built by Lior Neu-ner. I'd love to hear your feedback — Get in touch via DM or lior@remoterocketship.com
Jobs by Title
Remote Account Executive jobsRemote Accounting, Payroll & Financial Planning jobsRemote Administration jobsRemote Android Engineer jobsRemote Backend Engineer jobsRemote Business Operations & Strategy jobsRemote Chief of Staff jobsRemote Compliance jobsRemote Content Marketing jobsRemote Content Writer jobsRemote Copywriter jobsRemote Customer Success jobsRemote Customer Support jobsRemote Data Analyst jobsRemote Data Engineer jobsRemote Data Scientist jobsRemote DevOps jobsRemote Ecommerce jobsRemote Engineering Manager jobsRemote Executive Assistant jobsRemote Full-stack Engineer jobsRemote Frontend Engineer jobsRemote Game Engineer jobsRemote Graphics Designer jobsRemote Growth Marketing jobsRemote Hardware Engineer jobsRemote Human Resources jobsRemote iOS Engineer jobsRemote Infrastructure Engineer jobsRemote IT Support jobsRemote Legal jobsRemote Machine Learning Engineer jobsRemote Marketing jobsRemote Operations jobsRemote Performance Marketing jobsRemote Product Analyst jobsRemote Product Designer jobsRemote Product Manager jobsRemote Project & Program Management jobsRemote Product Marketing jobsRemote QA Engineer jobsRemote SDET jobsRemote Recruitment jobsRemote Risk jobsRemote Sales jobsRemote Scrum Master + Agile Coach jobsRemote Security Engineer jobsRemote SEO Marketing jobsRemote Social Media & Community jobsRemote Software Engineer jobsRemote Solutions Engineer jobsRemote Support Engineer jobsRemote Technical Writer jobsRemote Technical Product Manager jobsRemote User Researcher jobs