With the surge of high amounts of unstructured text data, there is a growing demand for NER techniques that can efficiently process it and extract meaningful information – and that is where Natural Language Processing (NLP) steps in. Among the various tasks that can be obtained via NLP, Named Entity Recognition is one way to extract significant information from text. Some of the popular tools available in NLP with predefined models and techniques are Spacy and NLTK, and today we will explore these libraries for Named Entity Recognition (NER).
An Introduction to NER
Named Entity Recognition is an information extraction task that identifies and classifies named entities into predefined categories such as person names, organizations, locations, medical codes, time expressions, quantities, percentages, etc. It forms the foundation of many NLP applications, such as chatbots, content recommendation, and information retrieval, among others.
Example
Sentence: “Microsoft, based in Redmond, was not founded by Steve Jobs.”
A NER model will identify and classify the below entities.
- Microsoft – Organization
- Redmond – Location
- Steve Jobs – Person
NER – Industry use cases
- Information Extraction: Businesses often must deal with large amounts of unstructured text data, such as emails, customer reviews, or social media posts. NER can extract essential details from this data, like names of people, organizations, locations, or product names.
- Customer Support: NER can automate some aspects of customer support; for example, it can be used to identify critical information in a customer’s message, such as the product they are asking about or the issue they are experiencing. They can then be rerouted to proper customer care support.
- Sentiment Analysis: NER is often used with sentiment analysis to understand how customers feel about specific products, services, or aspects of a business.
- Market Intelligence: Companies can use NER to monitor news articles, blog posts, or other news sources to keep track of important events related to their business.
Understanding Spacy and NLTK
Written in Python and Cython (C++ wrapper for Python), Spacy is an open-source Python library for advanced natural language processing, providing an efficient platform for tasks such as Parts of Speech (POS) tagging, sentence segmentation, and Named entity recognition.
NLTK, the Natural Language Toolkit, is a leading library for coding Python programs to analyze different human languages. It offers easy-to-use interfaces to numerous corpora and lexical resources.
Comparing Spacy and NLTK for NER
Spacy and NLTK are both excellent libraries, each with its strengths. Spacy is faster and has a more sophisticated API, making integrating into production applications easier. Its NER is based on a neural network model, giving it high accuracy.
NLTK, on the other hand, is excellent for teaching and research. Its NER uses a chunking parser, which is more transparent and allows you to understand how the entity was recognized. However, it is slower and less accurate than Spacy.
NER with Spacy
First, we will see how to download and install Spacy in Python.
Next, we will see a basic NER example using Spacy’s pre-trained model.
In the script, nlp(text) is used to proc
ess the text. doc.ents will return the identified entities, and entity.label_ gives the category of the entity.
Output:
NER with NLTK
NLTK is typically used in academia and research and is also a compelling NLP library with many functionalities.
Here, we first tokenize the text into words and then add part-of-speech tags. The ne_chunk function then uses these tags to recognize named entities.
Output:
Conclusion
Named Entity Recognition is an essential aspect of Natural Language Processing that allows machines to understand and categorize real-world objects within a text. Both Spacy and NLTK provide powerful tools for performing NER, each with its unique attributes. When it comes to choosing between the two, it largely depends on the requirements. If speed, accuracy, and ease of use are critical for your project, Spacy might be the better choice. However, if transparency and learning are your primary focus, then NLTK might be more beneficial.
One thing to note is that NER models can constantly be improved and trained according to the specific context of your application. Spacy offers options for training your NER models with custom entities, and NLTK provides a more transparent model, allowing you to customize the NER process more directly. While the landscape of Natural Language Processing is vast and complex, libraries like Spacy and NLTK make it considerably more approachable, and these libraries encapsulate the intricacies of NLP tasks, like Named Entity Recognition, behind simple and intuitive interfaces.
The study and application of NLP techniques are revolutionizing how we interact with machines, making them more human-like in their understanding of language. With libraries like Spacy and NLTK, the possibilities are endless. As we continue to explore and innovate in this domain, we bring the future of human-machine interaction a step closer.