Machine learning and statistics with python

 Introduction: One of the main application of spacy is to perform named entity recognition or ner activity with it.  Named entity recognition stands for the NLP task where we detect named entity or entities such as organization, person, location etc different entity names. Spacy provides built-in methods to detect named entity recognition. In this article, we will go through the basics of detecting the different ner and using the spacy ner features. What is NER and what are the different labels? NER or named entity recognition, is the procedure to detect named entities using natural language processing algorithms. In spacy, universal ner labels are detected by default. So there are the following ner in this ner label list: (1) PERSON: this represents any kind of person. This includes fictional and real people. fictional basically refers to cartoon, movie or book characters etc. (2) NORP: nationalities or religious or political groups. This refers to basically a type of co...

 Introduction:   In text mining algorithms, as well as nlp based data modeling, word similarity is a very common feature. Word similarity in nlp context refers to semantic similarity between two words, phrases or even two documents. We will discuss how to calculate word similarity using spacy library. what is similarity in NLP and how is it calculated? In NLP, lexical similarity refers between two texts refer to the degree by which the texts has same literal and semantic meaning. i.e. how much similar the texts mean; is calculated by similarity metrics in NLP. There are many different ways to create word similarity features; but the core logic is mostly same in all cases. The core logic in all these cases is to create two representative vectors of the two items; using either universal vectors created from pre-trained models like word2vec, glove, fasttext, bart and others; or using the present document and using different methods like tf-idf match, pagerank procedures, etc....