Named Entity Recognition (NER) in Python with Spacy
Natural Language Processing deals with text data. The amount of text data generated these days is enormous. And, this data if utilized properly can bring many fruitful results. Some of the most important Natural Language Processing applications are Text Analytics, Parts of Speech Tagging, Sentiment Analysis, and Named Entity Recognition. The vast amount of text data contains a huge amount of information. An important aspect of analyzing these text data is the identification of Named Entities. In this article we will be discussing Named Entity Recognition in python!
Table of contents
What is a Named Entity?
A named entity is basically a real-life object which has proper identification and can be denoted with a proper name. Named Entities can be a place, person, organization, time, object, or geographic entity.
For example, named entities would be Roger Federer, Honda city, Samsung Galaxy S10. Named entities are usually instances of entity instances. For example, Roger Federer is an instance of a Tennis Player/person, Honda City is an instance of a car and Samsung Galaxy S10 is an instance of a Mobile Phone.
Named Entity Recognition in Python
Python Named Entity Recognition is the process of NLP which deals with identifying and classifying named entities. The raw and structured text is taken and named entities are classified into persons, organizations, places, money, time, etc. Basically, named entities are identified and segmented into various predefined classes.
NER systems are developed with various linguistic approaches, as well as statistical and machine learning methods. It has many applications for project or business purposes.
NER model first identifies an entity and then categorizes the entity into the most suitable class. Some of the common types of Named Entities will be:
1. Organisations :
NASA, CERN, ISRO, etc
Mumbai, New York, Kolkata.
1 Billion Dollars, 50 Great Britain Pounds.
15th August 2020
Elon Musk, Richard Feynman, Subhas Chandra Bose.
An important thing about NER models is that their ability to understand Named Entities depends on the data they have been trained on. There are many applications of NER.
NER can be used for content classification, the various Named Entities of a text can be collected, and based on that data, the content themes can be understood. In academics and research, NER can be used to retrieve data and information faster from a wide variety of textual information. NER helps a lot in the case of information extraction from huge text datasets.
NER Using Spacy
Spacy is an open-source Natural Language Processing library that can be used for various tasks. It has built-in methods for Named Entity Recognition. Spacy has a fast statistical entity recognition system.
We can use spacy very easily for NER tasks. Though often we need to train our own data for business-specific needs, the spacy model general performs well for all types of text data.
Let us get started with the code, first we import spacy and proceed.
import spacy from spacy import displacy NER = spacy.load("en_core_web_sm")
Now, we enter our sample text which we shall be testing. The text has been taken from the Wikipedia page of ISRO.
raw_text="The Indian Space Research Organisation or is the national space agency of India, headquartered in Bengaluru. It operates under Department of Space which is directly overseen by the Prime Minister of India while Chairman of ISRO acts as executive of DOS as well."
Now, we print the data on the NEs found in this text sample.
for word in text1.ents: print(word.text,word.label_)
The Indian Space Research Organisation ORG the national space agency ORG India GPE Bengaluru GPE Department of Space ORG India GPE ISRO ORG DOS ORG
So, now we can see that all the Named Entities in this particular text are extracted. If, we are facing any problem regarding what type a particular NE is, we can use the following method.
Output: ‘Companies, agencies, institutions, etc.’
Output: ‘Countries, cities, states’
Now, we try an interesting visual, which shows the NEs directly in the text.
I will leave the Kaggle Link in the end, so that the readers can try out the code themselves. Coming to the visual, the Named Entities are properly mentioned in the text, with contrasting colors, which make data visualization quite easy and simple. There is another type of visual, which explores the full dataset as a whole. Please refer to the Kaggle link in the end.
Let us try the same tasks with some tests containing more Named Entities.
raw_text2=”The Mars Orbiter Mission (MOM), informally known as Mangalyaan, was launched into Earth orbit on 5 November 2013 by the Indian Space Research Organisation (ISRO) and has entered Mars orbit on 24 September 2014. India thus became the first country to enter Mars orbit on its first attempt. It was completed at a record low cost of $74 million.”
for word in text2.ents: print(word.text,word.label_)
The Mars Orbiter Mission PRODUCT MOM ORG Mangalyaan GPE Earth LOC 5 November 2013 DATE the Indian Space Research Organisation ORG ISRO ORG Mars LOC 24 September 2014 DATE India GPE first ORDINAL Mars LOC first ORDINAL $74 million MONEY
Here, we get more types of named entities. Let us identify what type they are.
Output: ‘Objects, vehicles, foods, etc. (not services)’
Output: ‘Non-GPE locations, mountain ranges, bodies of water’
Output: ‘Absolute or relative dates or periods’
Output: ‘ “first”, “second”, etc.’
Output: ‘Monetary values, including unit’
Now, we analyze the text as a whole in the form of a visual.
Here, we the various Named Entities in contrasting colors, so we understand the overall nature of the text.
NER of a News Article
We shall web scrape data from a news article and do NER on the text data gathered from there.
We shall use Beautiful Soup for web scraping purposes.
from bs4 import BeautifulSoup import requests import re
Now, we will use the URL of the news article.
html_content = requests.get(URL).text
soup = BeautifulSoup(html_content, "lxml")
Now, we get the body content.
Now, we use regex to clean the text.
body= body.replace('n', ' ') body= body.replace('t', ' ') body= body.replace('r', ' ') body= body.replace('xa0', ' ') body=re.sub(r'[^ws]', '', body)
Let us now have a look at the text.
' View in App Bitcoin was down by 6 and was trading at Rs 2728815 after hitting days high of Rs 2900208 Source Reuters Reported By ZeeBiz WebTeam Written By Ravi Kant Kumar Updated Sat Jun 12 20210646 pm Patna ZeeBiz WebDesk RELATED NEWS Cryptocurrency Latest News Today June 14 Bitcoin leads crypto rally up over 12 after ELON MUSK TWEET Check Ethereum Polka Dot Dogecoin Shiba Inu and other top coins INR price World India updates Bitcoin law is only'
Now, let us proceed with Named Entity Recognition.
Well, the visual formed is very large, but there are some interesting parts which I want to cover.
Named Entity Recognition (NER) is a crucial technique in natural language processing and can be implemented in Python using various libraries such as spaCy, NLTK, and StanfordNLP. Our Blackbelt course on NER in Python likely provides in-depth knowledge and practical skills in implementing NER using Python libraries. Mastering NER is beneficial for various applications such as sentiment analysis, chatbots, and information extraction from unstructured text data.
Frequently Asked Questions
A. Named Entity Recognition (NER) is a natural language processing technique that identifies and classifies named entities in text into predefined categories, such as people, organizations, and locations. For example, in the sentence “John works at Google in New York”, NER would identify “John” as a person, “Google” as an organization, and “New York” as a location.
A. Name-based Entity Recognition is a type of NER that specifically focuses on identifying and extracting named entities that are people or organizations. It helps in social media analysis or news articles, where identifying individuals and organizations is important.
A. The three steps in named entity recognition are:
1. Tokenization, which involves breaking the text into individual words or phrases.
2. Part-of-speech tagging, which assigns a grammatical tag to each word.
3. Entity recognition, which identifies and classifies the named entities in the text.
A. NER uses machine learning algorithms to analyze text and identify patterns that indicate the presence of named entities. These algorithms are trained on large datasets of annotated text, where human annotators have labeled the named entities in the text. When presented with new text, the NER algorithm applies the patterns it has learned to identify and classify the named entities in the text.
Leave a Reply Your email address will not be published. Required fields are marked *