Below are few Frequently Asked NLP Interview Questions
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Natural Language Processing (NLP) Interview Questions
Natural Language Processing or NLP is an automated way to understand or analyze the natural languages and extract required information from such data by applying machine learning Algorithms.
Below are the few major components of NLP.
- Entity extraction: It involves segmenting a sentence to identify and extract entities, such as a person (real or fictional), organization, geographies, events, etc.
- Syntactic analysis: It refers to the proper ordering of words.
- Pragmatic analysis: Pragmatic Analysis is part of the process of extracting information from text.
Pragmatic Analysis: It deals with outside word knowledge, which means knowledge that is external to the documents and/or queries. Pragmatics analysis that focuses on what was described is reinterpreted by what it actually meant, deriving the various aspects of language that require real-world knowledge.
According to The Stanford Natural Language Processing Group :
A Part-Of-Speech Tagger (POS Tagger) is a piece of software that reads text in some language and assigns parts of speech to each word (and other token), such as noun, verb, adjective, etc.
PoS taggers use an algorithm to label terms in text bodies. These taggers make more complex categories than those defined as basic PoS, with tags such as “noun-plural” or even more complex labels. Part-of-speech categorization is taught to school-age children in English grammar, where children perform basic PoS tagging as part of their education.
Dependency Parsing is also known as Syntactic Parsing. It is the task of recognizing a sentence and assigning a syntactic structure to it. The most widely used syntactic structure is the parse tree which can be generated using some parsing algorithms. These parse trees are useful in various applications like grammar checking or more importantly it plays a critical role in the semantic analysis stage.
tf–idf or TFIDF stands for term frequency–inverse document frequency. In information retrieval TFIDF is is a numerical statistic that is intended to reflect how important a word is to a document in a collection or in the collection of a set.
NLP Terminology is based on the following factors:
- Weights and Vectors: TF-IDF, length(TF-IDF, doc), Word Vectors, Google Word Vectors
- Text Structure: Part-Of-Speech Tagging, Head of sentence, Named entities
- Sentiment Analysis: Sentiment Dictionary, Sentiment Entities, Sentiment Features
- Text Classification: Supervised Learning, Train Set, Dev(=Validation) Set, Test Set, Text Features, LDA.
- Machine Reading: Entity Extraction, Entity Linking,dbpedia, FRED (lib) / Pikes
Natural Language Processing can be used for
- Semantic Analysis
- Automatic summarization
- Text classification
- Question Answering
Some real-life example of NLP is IOS Siri, the Google assistant, Amazon echo.
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