Nithish Singh

Sentence Semantic similarity is a crucial task in natural language processing (NLP) that involves determining how similar two sentences or phrases are in meaning. There are different types of semantic similarity measures that can be used in NLP.

One important aspect of NLP is chunking, which involves the extraction of meaningful phrases or chunks from text data. Chinking is a related technique that involves the exclusion of certain words or phrases from a chunk.

Polarity and subjectivity are two important concepts in natural language processing (NLP) that help machines understand the sentiment and emotions conveyed in human language. In this article, we'll explore these two concepts in depth and how they are used in NLP.

In this article, we will discuss the process of Kneser-Ney Smoothing and Absolute Discounting, its implementation, pros and cons, use cases, and real-time applications etc.

In this article, we will look at the different approaches to tokenization and their pros and cons in Natural Language Processing (NLP).

In this article, we will explore what N-gram models are, how they work, their advantages and disadvantages, and finally, we'll provide an example of how to implement an N-gram model.

In this article, we have explored about Lemmatization approaches in NLP in depth and presented Lemmatization approaches in Python with code examples.