Natural Language Processing Interview questions Part – 1
1 – What are some of the common NLP tasks?
NLP is the process of understanding a sentence and then generating a response. It has been used in many different industries to help humans do their jobs more efficiently.
Some of the common NLP tasks are:
Speech recognition: This is when a computer converts spoken words into text. This technology has been around for decades and gone through many iterations to improve accuracy. Today’s speech recognition has come a long way and can be used to identify phone numbers, voice commands on smart devices, text dictation, and more
Text to speech conversion: This is when a computer generates speech from written text. The results of the conversion may sound robotic, but it’s still a major leap forward for those with disabilities who can’t easily read or write.
Machine translation: This is when a computer translates one language into another language Google Translate is an example of machine translation.
Text Summarization: Based on a large corpus, this is used to give a short summary that gives an idea of the entire text in the document.
Language Modeling: Based on the history of previous words, this helps uncover what the further sentence will look like. A good example of this is the auto-complete sentences feature in Gmail.
Question Answering: This helps prepare answers automatically based on a corpus of text, and on a question that is posed.
Information Retrieval: This helps in fetching relevant documents based on a user’s search query.
Information Extraction: This is the task of extracting relevant pieces of information from a given text, such as calendar events from emails.
Speech synthesis: This is when a computer generates human sounding speech from text input.
2 – What are the different approaches used to solve NLP problems?
The approaches used to solve NLP problems are as follows:
1) Machine Learning: This is a subfield of artificial intelligence that deals with algorithms that can learn from data and improve their performance. It is a way of getting computers to act like humans.
2) Deep Learning: This is the most popular approach among the NLP community because it has been shown to be better than other approaches in many cases.
3) Semantic Parsing: This approach uses rules to identify relationships between words and phrases in natural language sentences.
4) Statistical Parsing: This approach relies on statistical analysis of text corpora, which are collections of texts or sentences written by humans, to identify patterns and construct parsers for language understanding tasks.
3 – How do Conversational Agents work?
A conversational agent is a computer program that can understand and respond to human speech. It can be an artificial intelligence, such as a chatbot, or it can be a human being who is acting as a conversational interface.
Conversational agents are able to understand natural language and use it to generate appropriate responses. They make use of natural language processing (NLP) in order to interpret the meaning behind what the user is saying. There are two main types of NLP: statistical machine translation (SMT) and rule-based machine translation (RBMT). SMT relies on statistical algorithms that learn from the patterns found in large amounts of text data. RBMT relies on rules that have been pre-programmed into the system by an expert translator or linguist.
4 – What are the steps involved in pre-processing data for NLP?
The preprocessing of data is the first step in Natural Language Processing. It is often the most time-consuming step. The goal of preprocessing is to get a clean and structured data set that can be used for training and testing.
In order to achieve this, there are four steps involved:
1) Cleaning the text: removing punctuation, numbers, stopwords, etc.
2) Part-of-speech tagging: assigning a part of speech to each word in a sentence (verb, noun, adjective).
3) Lemmatization: replacing inflected words with their root form.
4) Stemming: reducing words to their root form.
5 – What is the meaning of Text Normalization in NLP?
Text normalization is a process that can be used to remove the irregularities from text data. It is helpful in many NLP tasks. For example, it can be used to convert an unstructured text into a structured one.
The process of text normalization involves removing the irregularities from text data, which includes different types of errors such as spelling mistakes and punctuation issues. It also includes converting an unstructured text into a structured one by adding information about parts of speech and word sense disambiguation. This helps in many NLP tasks because it enables machine learning algorithms to work on more accurate data sets and improve their performance.