Named Entity Recognition NER works by locating and identifying the named entities present in unstructured text into the standard categories such as person names, locations, organizations, time expressions, quantities, monetary values, percentage, codes etc. Spacy comes with an extremely fast statistical entity recognition system that assigns labels to contiguous spans of tokens.
Parts of Speech tagging is the next step of the tokenization. Once we have done tokenization, spaCy can parse and tag a given Doc. spaCy is pre-trained using statistical modelling. This model consists of binary data and is trained on enough examples to make predictions that generalize across the language. Example, a word following “the” in English is most likely a noun.
spaCy is designed specifically for production use. It helps you build applications that process and “understand” large volumes of text. It can be used to build information extraction or natural language understanding systems, or to pre-process text for deep learning. In this article you will learn about Tokenization, Lemmatization, Stop Words and Phrase Matching operations using spaCy.
spaCy is an open-source Python library that parses and “understands” large volumes of text.
spaCy is the best way to prepare text for deep learning.
It interoperates seamlessly with TensorFlow, PyTorch, scikit-learn, Gensim and the rest of Python’s awesome AI ecosystem.
With spaCy, you can easily construct linguistically sophisticated statistical models for a variety of NLP problems.
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Boosting helps to improve the accuracy of any given machine learning algorithm. It is algorithm independent so we can apply it with any learning algorithms. It is not used to reduce the model variance.
Boosting involves many sequential
iterations to strengthen the model accuracy, hence it becomes computationally costly.