NLP – What happens in Entity Extraction and its value

by | Apr 5, 2021

 5 min read

Overview of Named Entity Recognitionnamed-entity-recognition-definition

In NLP, entity extraction or named entity recognition (NER), expedites a search process in social media, emails, blogs, articles, or research papers by identifying, extracting, and determining all the appropriate tags for words or series of words in a text. 

If we defined it – Named Entity Recognition (NER) is a  natural language processing (NLP) technique that performs entity extraction by categorizing data through relevant entities or tags.

Why is NER important for text machine learning? Because entity extraction, identifies quickly in categories the information needed in large amounts of unstructured data enabling text analysis at a faster pace.

An Example of Entity Extraction using NLP

The tags or entities could be general categories such as names of people, individual companies, organizations, cities and places.

Let’s follow an example from one of our blog posts about intent classification in Chatbots:

“I am looking for a clinic located downtown.”

“intent”: “clinic_search

“entities”: {“place” : “clinic”}

In this example, the intent was “search.” The entity is relevant information during the interaction: searching for a clinic and preferably located downtown. The classifier categorizes data inputs similar to how humans classify objects.

This is similar to how humans recognize that anger is an emotion and roses are a type of flower. The role of the classifier is to categorize intents such as search, purchase, subscribe into different categories. In named entity extraction text analysis can be covered in two parts: entity detection and entity categorization.

For instance, Dr. John Foley, in his dissertation at the University of Massachusetts Amherst, “Poetry: Identification, Entity Recognition, and Retrieval,” explains several NER approaches for allusion recognition and its understanding to deepen reasoning about poetry. This is an excellent example of using NER to deepen understanding and expand literary works and artistic representation.

Traditional NER systems provided poor recognition opting to label every token on a page containing poetry and labeling prose when it was co-located with poetry to improve their machine learning model.Graphical-representation-of-our-poetry-NER-model

The study explored poetry-based entity recognition to deepen understanding and representation of poems. Also, they evaluated modern neural NER models on poetry data and concluded that cross-training on existing News-NER datasets is their “only” critical feature. Their news-based cross-training, in particular, contributes a promising approach for historical and literary NLP generalization.

Insights from their NER model can help identify poetry at a world level and possibly generate large amounts of ground truth from duplicate poems. In conclusion, poetry-specific phenomena can be studied using their developed NER system, metaphor, and simile, which could be identified and analyzed across millions of poems.

GPT-3, NLP and Entity Extraction

Some described the last year’s release of the Generative Pre-Trained Transformer 3 (GTP-3) as the largest, most powerful, and comprehensive tool for NLP and text analysis. Entrepreneur, Arram Sabeti has expressed “I feel I’ve seen the future and that full AGI might not be too far away” in his blog post after testing OpenAI GPt-3 API.arram-sabeti-on-twitter

Source: Arram Sabeti tweet

Developed by Open AI, the new language prediction model has a difference of 173.5 billion parameters compared to its predecessor GPT-2.

The AI, research, and deployment company have estimated that the cost of training of the GPT-3 could be around $4.6 million, and its source code is not made public compared to its predecessor.

In a few words, GPT-3 can automatically label data in multilingual documents and text datasets. Expanding natural language tasks such as named entity recognition by extracting information in more specific entities than traditional entities as mentioned (e.g., place, person, location).

NER is limited to how pre-trained machine learning or deep learning models use categories, entities, or tags that could be too general or too specific. Certainly, GPT -3 transcends limitations with its 175 million variables to tune and tweak its machine learning model, enabling conversations about almost anything.

Use Cases of NLP Performing

Entity Extractionchatbot-optimization

Chatbot Optimization: Named entity recognition’s function is to extract critical information. For chatbot development, precise annotations and quality training datasets progressively enhance their capability to learn and understand how to distinguish, categorize, and execute various actions such as classifying intent while interacting with a user.

NER is indeed a small piece of the puzzle to train a machine learning model. However, the NLP task expedites the extraction of key information in large amounts of data.

For performing sentiment analysis, NER supports marketing and customer success teams by identifying polarity quickly to improve customer satisfaction when a product or service is released. There are numerous examples of NER’s role from finance to eCommerce from traditional to new approaches, but the essence remains the same: it gets valuable information through entities or tags in unstructured or structured data. 

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