The value of text data to businesses cannot be underestimated. Businesses are today leveraging vast amounts of unstructured text data generated from various sources such as social networks to expand and improve their operations. Text data in itself is not useful until valuable information is extracted out of it and implemented to the applications intended for it. The science of extracting meaningful information and learning from text data is known as Natural Language Processing (NLP). Find top 12 NLP Interview Questions and answers.
NLP is a branch of artificial intelligence (AI) that analyzes how machines understand and process human language with the aim of developing systems that can extract useful information from text data for applications such as translation, text classification, sentiment analysis, market intelligence, customer support, and many others.
Commonly asked NLP interview questions
The foundation of NLP is in AI therefore to pursue a specialization in NLP, you need to first consider taking both foundational and advanced artificial intelligence courses. Beyond learning theory and practicing, the real challenge comes in acing the interview questions. To help you prepare for NLP Interview Questions, we have compiled 12 common entry-level NLP Interview Questions.
What is NLP?
NLP stands for Natural Language Processing. NLP is a field of computer science, artificial intelligence, and machine learning that is involved with creating computer systems that make sense out of and analyze human language with the aim of carrying out tasks like translation.
Give examples of use-cases of NLP
NLP has several applications for example,
- Google Translate for converting written or spoken text into different languages.
- Siri, a digital voice assistant integrated into Apple iOS devices to take in and act on voice commands for tasks like alarm setting and traffic inquiries.
- Chatbots that provide virtual customer support
- The sentimental analysis used to classify sentiments as positive or negative.
Explain the steps used in solving an NLP pipeline made up of?
- Text gathering. This involves the collection of data that is relevant to the intended task. Data is often collected from existing datasets or scrapped from the web.
- Text cleaning and preprocessing. Cleaning text and then transforming it into a form that is analyzable is important for building accurate Machine Learning algorithms. Preprocessing includes removing tags, punctuations, and special characters in a text to remain with pure text. Various preprocessing techniques include tokenization, lower casing, stop words removal, stemming, lemmatization.
- Feature engineering. Feature engineering is the process through which the best possible features of an NLP algorithm are extracted from preprocessed data. Features are the defined input parameters of an algorithm that are applied to produce the desired output, in many cases solutions.
- Model training. Building and training one or more models using the identified features. This is done using techniques like regression or neural nets.
- Model evaluation. Model evaluation involves comparing models against predefined metrics to choose the best.
- Model deployment. The best model is then selected and deployed to the production pipeline.
- Monitoring. After deployment, the model’s performance is then monitored regularly and updated when the need arises.
How is feature extraction done in NLP?
There are several sources of text data, all of which carry some insight into the business depending on the business needs. For instance, social media, web scrapping, and news outlets. Feature extraction is the process by which predefined features from a set are converted into a format that is readable by an NLP algorithm. Feature extraction is useful in sentiment analysis, spam filtering, or document classification.
- TF-IDF. TF-IDF is a numerical statistic that indicates the importance of a word to a document in a set
- Bag of words. Extracts words used in a text and classifies them based on their frequency of use.
- Word2vec. This algorithm uses neural networks to learn associations between words in a large text corpus.
- Latent semantic indexing. A mathematical technique used for extracting hidden contextual relationships between words in unstructured data. Words may not share the same synonyms or keywords, but within the same context, they could be having the same meaning.
What is the significance of TF-IDF?
TF-IDF stands for term frequency-inverse document frequency. TF-IDF is a numerical statistic that indicates the importance of a word to a document in a set. The importance of a word is calculated by finding the ratio of the frequency of the term against the total number of terms in the document. Documents with similar words in information retrieval will have similar vectors.
Keyword normalization is the process of converting a keyword into its base form. Explain the two techniques used for keyword normalization.
Keyword normalization is done during preprocessing. Here, the two techniques used are:
- Stemming. This is the technique used to remove prefixes and suffixes from a word to extract the base form of the word.
- Lemmatization. Lemmatization goes beyond stemming to extract the meaningful base form of a word, in other words, the root form or the lemma of a word.
What are the components of NLP?
- Entity extraction. The process of segmenting a sentence to extract entities such as a person, organization events, etc.
- Syntactic analysis. This is the process of analyzing a sentence to extract specific meaning from it based on grammar and order of words.
- Pragmatic analysis. This is the process of extracting useful information from a text.
What does NER (Named Entity Recognition) mean in NLP?
Also referred to as entity chunking, NER is a method used to identify key information (entities) in a text and classify it into categories. This is done to classify specific entities like people, locations, organizations, things, and others that belong to the same context and from which information can be extracted. These entities are usually denoted by proper names. NER has found wide application in customer support systems, recommendation systems, search engines, and human resources among others to improve processes.
Explain tokenization in NLP
Tokenization is the process of splitting text into smaller units known as tokens for the purposes of clearly and accurately understanding the context or meaning of a text for modeling purposes. Tokenization can either be splitting sentences or words. Tokens are classified as words, characters, or subwords. Tokens are then used to build vocabulary for NLP tasks.
What are the different metrics used to evaluate the performance of NLP models?
Evaluating a model is an important part of building an accurate NLP model. Metrics used to evaluate models in NLP are:
- Confusion matrix. A N x N matrix where N represents the number of target classes. Each row in a confusion matrix represents an actual class while the columns represent a predicted class. This matrix compares actual values against those predicted by the model.
- Precision metric. Also known as a positive predictive value, refers to the number of true positives expressed as a percentage of all the relevant results i.e true positive + false positive. The precision metric cannot be used together with the recall metric. One has to be traded off for the other.
- Recall metric. Also known as a sensitivity value, is the number of relevant results that were retrieved expressed as a fraction/percentage of all the relevant results.
- F-1 score. This is the harmonic mean of precision and recall results. This metric is used in problems where both precision and recall results are required.
Parsing in NLP is the process used to determine the syntactic structure, content, and meaning of a text. It is fundamental to any NLP application. Explain the dependency parsing technique.
Dependency parsing is the process of analyzing the grammatical structure of a sentence based on the relationships between the headwords and dependent words. Here, tags are assigned to the relationships between two words in a sentence. These tags are known as dependency tags.
What is perplexity and what is its function in NLP?
Perplexity refers to the measure of how well a probability model predicts sample data. Perplexity is a way of evaluating language models such that a low perplexity is an indication that a model is good at predicting the sample. On the other hand, high perplexity shows that a model is bad at predicting a sample.
Being that there are far too many natural languages across the globe each with unique meaning and syntax for its words, the NLP Interview Questions process can be a little bit difficult especially when one is just starting. However, with time and practice, you will gain confidence and expertise over time.