What is Named Entity Recognition and Classification (NERC) for NLP?

NLP is very popular these days due to its wide variety of applications. If you are familiar with Natural Language Processing, you must know about different NLP tasks, one of which is Semantic Analysis.

 

One of the techniques of semantic analysis is named entity extraction, which is used to extract entities from a block of data. 

 

In this article, we understand the Named Entity Recognition and Classification (NERC) technique, which is a very important topic in the pipeline of NLP. 

 

Named Entity Recognition and Classification (NERC)

 

Named Entity Recognition and Classification (NERC) is a method for extracting information units such as names, such as those of people, organizations, and places, as well as quantitative expressions such as time, date, money, and per cent expressions, from unstructured text. 

 

The objective is to provide viable, domain-independent algorithms for automatically detecting named items with high accuracy.

 

How does NERC work?

 

Consider the following statement as an example:

 

“Sundar Pichai, the CEO of Google Inc. is walking in the streets of California”

 

We can distinguish three categories of things based on the preceding sentence:

 

("person": "Sundar Pichai"), ("organisation": "Google Inc."), and ("state": "California").

 

To achieve the same thing with computers, however, we must first assist them in recognizing entities so that they can categorize them. As a result, we may use machine learning and Natural Language Processing to do this.

 

Let's look at how both of these things play a part in NER implementation utilizing computers:

 

• NLP is a branch of linguistics that investigates the structure and laws of language and develops intelligent systems capable of extracting meaning from text and voice.

 

• Machine learning is a technique for assisting machines in learning and improving through time.

Named Entity Recognition is a two-step process. First, a named entity is detected, and then it is categorized. The first step entails identifying a word or a series of words that together create an entity. Each word stands for a token. 

 

The development of entity categories is the second phase. Person, organization, time, and so on are some of the most prevalent entity types. A model requires training data to understand what is and is not a relevant item, as well as how to categorize them. The more task-relevant the training data, the more accurate the model will be in completing the job. 

 

The Data:

 

According to towards data science, The data is a Kaggle-sourced feature engineered corpus tagged with IOB and POS tags. 

 

If you're wondering what an IOB is, it's a common tagging format for tokens in which the I- prefix before a tag indicates that the tag is inside a chunk, the B- prefix before a tag indicates that the tag is the beginning of a chunk, and the O tag indicates that the token belongs to none of the chunks (outside). 

 

Some essential information about entities is provided below:

 

• geo = Geographical Entity

• org = Organization

• per = Person

• gpe = Geopolitical Entity

• tim = Time indicator

• art = Artifact

• eve = Event

• nat = Natural Phenomenon

 

(Also learn: Top 10 Data Extraction Tools)

 

Different Blocks of NER model:

 

A Named Entity Recognition model is comprised of three blocks. They are:

 

1. Noun Phrase Identification:

 

This stage involves using dependency parsing and part of speech tagging to extract all noun phrases from a text.

 

2. Phrase Classification:

 

We sorted all of the extracted noun phrases from the previous stage into their appropriate groups in this step. 

 

Google Maps can help you clear up any confusion about where you are. and the free datasets from DBpedia and Wikipedia may be used to identify individual or corporate names. Aside from that, we can also create lookup tables and dictionaries by merging information from other sources.

 

3. Entity Disambiguation:

 

When entities are misclassified, it's beneficial to add a validation layer on top of the findings. This may be accomplished through the usage of knowledge graphs. Google knowledge graph and IBM Watson are two examples of popular knowledge graphs.

 

Python libraries for NERC:

 

Some python tools to ingest data include the following libraries, as said by district data labs:

 

1. Pdfminer comes with a command-line utility named "pdf2txt.py" that extracts text from PDF files.

 

2. Subprocess is a standard library module that allows us to call the command line utility "pdf2txt.py" from within our code.

 

3. NLTK stands for Natural Language Tool Kit, and it is one of Python's most popular platforms for analyzing natural language data.

 

4. String strips non-printable characters from the output of the text retrieved from our journal article PDFs using variable replacements and value formatting.

 

5. Unicode data enables Latin Unicode letters to cleanly degrade into ASCII. This is a crucial feature since some Unicode characters are difficult to extract.

 

(Must read: NLP Libraries with Python)

 

Open-source NERC tools:

 

There are three widely used NERC open-source tools. Here's a quick rundown of each.

 

1. Natural Language ToolKit (NLTK):

 

The chunk package in NLTK chunks the specified list of tagged tokens using NLTK's suggested named entity chunker. Parts of speech (POS) tags are used to tokenize and tag a string. 

 

After that, the NLTK chunker finds non-overlapping groupings and assigns them to an entity class. The chunk package in NLTK chunks the specified list of tagged tokens using NLTK's suggested named entity chunker. 

 

Parts of speech (POS) tags are used to tokenize and tag a string. After that, the NLTK chunker finds non-overlapping groupings and assigns them to an entity class.

 

2. Stanford’s NER:

 

Stanford NER, or Stanford Named Entity Recognizer, is a Java implementation of linear-chain Conditional Random Field (CRF) sequence models that act as a Named Entity Recognizer. 

 

Named Entity Recognition (NER) recognizes and labels sequences of words in a text that are the names of objects, such as people and companies, genes, and proteins. Nitin Madnani created an interface to Stanford NER for NLTK.

 

3. Polyglot:

 

Polyglot is a multilingual (i.e. language) natural language pipeline that enables large multilingual (i.e. language) applications. 

 

It supports 165 languages for tokenization, 196 languages for language detection, 40 languages for named entity recognition, 16 languages for part of speech tagging, 136 languages for sentiment analysis, 137 languages for word embeddings, 135 languages for morphological analysis, and 69 languages for transliteration. It's a powerful natural language processing tool.

 

Applications of NERC:

 

Named entity recognition (NER) will make it simple to recognize essential elements in a document, such as people's names, places, brands, monetary values, and so on. 

 

Extracting the major elements from a text also aids in the organization of unstructured data and the detection of significant information, which is critical when working with huge datasets.

 

Some applications of NER, as stated by Analytics Vidhya are:

 

1. Customer support:

 

Consider the use case of customer support issues, where we are dealing with an increasing quantity of tickets and may employ named entity recognition algorithms to answer client requests more quickly. 

 

Automating repeated customer support duties, such as classifying clients' complaints and inquiries, saves you important time from a commercial standpoint. 

 

As a consequence, it aids in increasing customer satisfaction and improving resolution rates. We may also utilize entity extraction to extract useful information, such as product names or serial numbers, making it easy to route tickets to the most appropriate agent or team to handle the issue.

 

2. Customer insights:

 

Online reviews are a wonderful source of consumer feedback for practically all product-based businesses, since they may give extensive insights into what customers like and hate about your items, as well as the areas of your business that need to be improved for business growth. As a result, we can utilize NER algorithms to aggregate all consumer comments and identify reoccurring issues.

 

For example, we may utilize the NER system to identify locations that are frequently referenced in bad customer feedback, leading you to focus on a certain office branch.

 

3. Recommendation system:

 

Many current apps, such as Netflix, YouTube, and Facebook, rely on recommendation systems to provide the best possible client experience. Many of these systems rely on named entity recognition, which can provide recommendations based on a user's previous search history.

 

For example, if you view a lot of educational videos on YouTube, you'll receive more education-related suggestions.

 

4. Summarizing resumes:

 

Recruiters spend several hours of their day combing over resumes and looking for the appropriate applicant when they are hiring new staff. Each CV has almost identical information, but they are arranged and formatted differently, making this a typical example of unstructured data.

 

So, using an entity extractor, recruitment teams can quickly extract the most relevant information about candidates, including personal information such as name, address, phone number, date of birth, and email, as well as information about their training and experience such as certifications, degrees, company names, skills, and so on.

 

To sum up, It's simple to get started with NER if you think your company or project will benefit from it. We have previously reviewed several good open-source libraries in this post. We'll finish the blog on this note. This has been an introduction to the NERC.

(Read next: What Is Text Summarization in NLP?)