Natural Language Processing Interview questions Part – 2
1 – What is an ensemble method in NLP?
Ensemble methods are a group of machine learning algorithms that work together to solve a problem. They are typically used when the machine learning algorithm is not able to solve the problem on its own or when it has not been trained enough.
An ensemble method is a way to combine the predictions from a set of base models. The idea is that when you combine the predictions of several base models, you get a better prediction than any individual model.
The use of ensemble methods in NLP has been increasing as they have proven to be an effective way of solving problems.
2 – What do you mean by TF-IDF in Natural language Processing?
TF-IDF is a statistical measure to evaluate the importance of words in a document. It is calculated as the product of two values:
Term Frequency (TF): TF is a measure of how often a word occurs in a document, which can assist in identifying patterns or topics in the writing.
Inverse Document Frequency (IDF): What is the probability that a word would occur in any random document?
3 – What is POS tagging in NLP?
POS tagging is a process where the system assigns a part-of-speech tag to each word in the sentence. POS tagging is the process of assigning part-of-speech tags to words in a sentence.
This is done by using an algorithm to identify which words are nouns, verbs, adjectives, adverbs etc. The purpose of POS tagging is to help computers understand what meaning words have and how they are being used in context.
4 – What is Latent Semantic Indexing (LSI) in NLP?
Latent Semantic Indexing (LSI) is a technique that is used by search engines to measure the similarity of words in different documents. It can be thought of as an extension of vector space models, which are used for modeling text data. The LSI model builds on this idea and extends it to include latent semantic structures that can be represented as matrices.
5 – What do you mean by masked language modelling?
Masked language modelling is a technique that is used to identify the meaning of words by using a dictionary and context. It is different from traditional natural language processing because it does not focus on individual words, but rather on the meaning of entire sentences.
This technique can be used in many ways and has many applications. For example, it can be used in an email or chatbot to detect spam messages.