Category Archives: NLP

How to Perform Sentence Segmentation or Sentence Tokenization using spaCy | NLP Series | Part 5

Sentence Segmentation or Sentence Tokenization is the process of identifying different sentences among group of words. Spacy library designed for Natural Language Processing, perform the sentence segmentation with much higher accuracy. However, lets first talk about, how we as a human identify the start and end of the sentence? Mostly with the help of the punctuation, right? And in most of the cases we say a sentence ends with a dot ‘.’ character. So with this basic idea, we would say that we can split the string based on dot and get the different sentences. Do you think this logic would be enough to get all sentence tokens?

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Named Entity Recognition NER using spaCy | NLP | Part 4

Named Entity Recognition is the most important or I would say the starting step in Information Retrieval. Information Retrieval is the technique to extract important and useful information from unstructured raw text documents. 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.

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Parts of Speech Tagging and Dependency Parsing using spaCy | NLP | Part 3

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.

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A Quick Guide to Tokenization, Lemmatization, Stop Words, and Phrase Matching using spaCy | NLP | Part 2

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.

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Spacy Installation and Basic Operations | NLP Text Processing Library | Part 1

What is Spacy

spaCy is an open-source Python library that parses and “understands” large volumes of text.

(You can download the complete Notebook from here.)

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Important Use Cases of NLP

In today’s world we are generating large amount of data every second. while tweeting, chating, writing or even speaking, we are fabricating corpse of data. Most of the data is in textual and unstructured form. Hence to make this data understandable by computer, we need to process it. NLP technique helps us in processing the data and helps us to get useful insights from it.

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