Named Entity Recognition in NLP

In this article, we’ll dive into the various concepts related to NER, explain the steps involved in the process, and understand it with some good examples. Named Entity Recognition (NER) is a critical component of Natural Language Processing (NLP) that has gained significant attention and research interest in recent years. It involves identifying and categorizing named entities, such as people, organizations, locations, dates, and other relevant information that can be used for various applications, including information retrieval, sentiment analysis, question-answering, and more.

Named Entity Recognition (NER)

Named Entity Recognition (NER) is a key task in Natural Language Processing (NLP) that involves the identification and classification of named entities in unstructured text, such as people, organizations, locations, dates, and other relevant information. NER is used in various NLP applications such as information extraction, sentiment analysis, question-answering, and recommendation systems.

Key concepts related to NER

Before we get into the technicalities, it’s important to understand some of the basic concepts related to NER. Here are some key terms that you should be familiar with:

• Named Entity: Any word or group of words that refer to a specific person, place, organization, or other object or concept.
• Corpus: A collection of texts used for language analysis and training of NER models.
• POS Tagging: A process that involves labeling words in a text with their corresponding parts of speech, such as nouns, verbs, adjectives, etc.
• Chunking: A process that involves grouping words together into meaningful phrases based on their part of speech and syntactic structure.
• Training and Testing Data: The process of training a model with a set of labeled data (called the training data) and evaluating its performance on another set of labeled data (called the testing data).

Steps involved in NER

Now, let’s take a look at the various steps involved in the NER process:

• Tokenization: The first step in NER involves breaking down the input text into individual words or tokens.
• POS Tagging: Next, we need to label each word in the text with its corresponding part of speech.
• Chunking: After POS tagging, we can group the words together into meaningful phrases using a process called chunking.
• Named Entity Recognition: Once we have identified the chunks, we can apply NER techniques to identify and classify the named entities in the text.
• Evaluation: Finally, we can evaluate the performance of our NER model on a set of testing data to determine its accuracy and effectiveness.

Some Mathematical Concepts related to NER-

The NER process involves various mathematical concepts, including probability theory, machine learning, and deep learning. Here’s a brief overview of some of the mathematical techniques used in NER:

1. Hidden Markov Models (HMM): HMMs is a statistical model used for sequence classification tasks, such as NER. They involve representing the sequence of words in a text as a sequence of states, where each state represents a particular named entity or other objects. By analyzing the probabilities of each state, we can identify the most likely named entities in the text.
2. Conditional Random Fields (CRF): CRFs are a type of graphical model used for sequence labeling tasks, such as NER. They involve modeling the conditional probability of each tag given the entire sequence of words, allowing us to identify the most likely named entities in a given text.
3. Deep Learning: Deep learning techniques, such as neural networks, are increasingly being used for NER tasks, allowing us to identify and classify named entities in a more accurate and efficient manner.

Use of NER in NLP

NER has numerous applications in NLP, including information extraction, sentiment analysis, question-answering, recommendation systems, and more. Here are some common use cases of NER:

• Information Extraction: NER can be used to extract relevant information from large volumes of unstructured text, such as news articles, social media posts, and online reviews. This information can be used to generate insights and make informed decisions.
• Sentiment Analysis: NER can be used to identify the sentiment expressed in a text towards a particular named entity, such as a product or service. This information can be used to improve customer satisfaction and identify areas for improvement.
• Question Answering: NER can be used to identify the relevant entities in a text that can be used to answer a specific question. This is particularly useful for chatbots and virtual assistants.
• Recommendation Systems: NER can be used to identify the interests and preferences of users based on the entities mentioned in their search queries or online interactions. This information can be used to provide personalized recommendations and improve user engagement.

Advantages of NER

Here are some of the advantages of using NER in NLP:

• Improved Accuracy: NER can improve the accuracy of NLP applications by identifying and classifying named entities in a text more accurately and efficiently.
• Speed and Efficiency: NER can automate the process of identifying and classifying named entities in a text, saving time and improving efficiency.
• Scalability: NER can be applied to large volumes of unstructured text, making it a valuable tool for analyzing big data.
• Personalization: NER can be used to identify the interests and preferences of users based on their interactions with a system, allowing for personalized recommendations and improved user engagement.

Disadvantages of NER

Here are some of the disadvantages of using NER in NLP:

• Ambiguity: NER can be challenging to apply in cases where there is ambiguity in the meaning of a word or phrase. For example, the word “Apple” can refer to a fruit or a technology company.
• Limited Scope: NER is limited to identifying and classifying named entities in a text and cannot capture the full meaning of a text.
• Data Requirements: NER requires large volumes of labeled data for training, which can be expensive and time-consuming to collect and annotate.
• Language Dependency: NER models are language-dependent and may require additional training for use in different languages.

Performing NER in NLP

Necessary requirements:

Import nltk
nltk.download('averaged_perceptron_tagger')
nltk.download('maxent_ne_chunker')
nltk.download('words')

Code showing the NER using nltk library-

Python3

import nltk

# Define the text to be analyzed
text = "GeeksforGeeks is a recognised platform for online learning in India"

# Tokenize the text into words
tokens = nltk.word_tokenize(text)

# Apply part-of-speech tagging to the tokens
tagged = nltk.pos_tag(tokens)

# Apply named entity recognition to the tagged words
entities = nltk.chunk.ne_chunk(tagged)

# Print the entities found in the text
for entity in entities:
    if hasattr(entity, 'label') and entity.label() == 'ORGANIZATION':
        print(entity.label(),'-->', ''.join(c[0] for c in entity))
    elif hasattr(entity, 'label') and entity.label() == 'GPE':
        print(entity.label(), '-->',''.join(c[0] for c in entity))

Output:

ORGANIZATION --> GeeksforGeeks
GPE --> India

In this code, we first define the text to be analyzed and tokenize it into words using nltk.word_tokenize(text). We then apply part-of-speech tagging to the tokens using nltk.pos_tag(tokens). Finally, we apply named entity recognition to the tagged words using nltk.chunk.ne_chunk(tagged).

The output of this code for the sample text “GeeksforGeeks is a recognized platform for online learning in India” is:

ORGANIZATION --> GeeksforGeeks
GPE --> India

This shows that NLTK was able to recognize “GeeksforGeeks” as an organization and “India” as a geographic location.