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Five AI Models for Data Extraction That You Need to Consider for Your Business

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Source The impact of automated data extraction Companies collect and use data from multiple sources to strategize, build corporate plans, and do everything that can keep them at the top of their game. However, most of the data is unstructured, leading to the wastage of potentially helpful information. But if you consider gathering data, it […]

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The impact of automated data extraction

Companies collect and use data from multiple sources to strategize, build corporate plans, and do everything that can keep them at the top of their game. However, most of the data is unstructured, leading to the wastage of potentially helpful information. But if you consider gathering data, it will take a lot of time to analyze the same information. 

Enter automated data extraction. This data collection method can help you transform unstructured data into valuable data. By understanding machine learning for business, you can make the right choices for your company. An AI—powered data extraction method can bring numerous benefits to your organization. 

What do you need to know about automated data extraction?

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  1. Automated data extraction is a more efficient method for large amounts of data

If you decide to extract data manually, it will be time—consuming and cumbersome, leading to a monotonous way of working. Moreover, as documents pile up in your company, it becomes tiring to consolidate this information into what is relevant to your business. This is why most organizations look for more innovative methods of data extraction. 

  1. Automated data extraction can be customized

The most powerful systems in the world that run on machine learning and artificial intelligence understand documents the same way as humans. First, organizations can teach the data extraction software they use to extract only the required data. Then, you can make the software work in real—time to pull data whenever new data comes in. 

  1. Automated data extraction is simple

Using automated data extraction software is easier than it sounds. With a user—friendly interface, you can easily find and extract what you need. You need not have coding experience before using any data extracting software. 

Most interfaces have a point and click system, making it easier for the user to navigate. With an appropriate data extraction plan, you can decide your data needs and comfort level on the interface. 

How has artificial intelligence become helpful for data extraction?

The very vision of artificial intelligence was to create machines that mimic humans in their functions and abilities. However, language is considered the most important feature in humans, and artificial intelligence has advanced in understanding that. Therefore, AI has integrated language into Natural Language Processing(NLP). 

NLP comprises natural language understanding(human to machine) and natural language generation(machine to human). The surge in unstructured content in text, videos, and more has led to an increase in NLP demand. It can extract valuable data from text content such as feedback, customer surveys, etc.

Which AI models can be used to extract data?

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The following five AI techniques are commonly used for data extraction:

  1. Named Entity Recognition

The most common and helpful model for data extraction is Named Entity Recognition(NER). The NER model can find different entities present in the text, such as locations, persons, dates, etc. simply put, NER extracts valuable data and adds meaning to the text document. 

The different types of NER systems are:

  • Dictionary—based NER systems: In this NER approach, there is a dictionary containing existing vocabulary. The basic string matching algorithms then check if the given text matches with the entity as mentioned in the dictionary. The only limitation of this approach is that the dictionary must be updated repeatedly. 
  • Rule—based systems: In this model, there are a few pre—defined rules set for data extraction. Pattern—based rules and context—based rules are the two common types used. The former depends on the morphological way of words used, whereas the latter relies on the context of the term used in the document. 
  • Machine learning based systems: Systems related to machine learning for business use statistical—based models to detect entity names. A feature—based representation of the given data is created. The limitations of the other two models are erased as this approach can recognize an entity even with a spelling variation. 

There are two phases of an ML—based solution for NER. The first phase involves training the ML model on the annotated documents. In the next stage, the trained model can be used to annotate raw documents. The time taken to train the model will depend upon the complexity of the built model. 

  • Deep Learning approach: Deep Learning systems are used more than ever as they create state—of—the—art systems for NER. The DL technique works better than the other three models as the input data is mapped to a non—linear representation. Additionally, time and resources are saved as feature engineering isn’t required. 

The everyday use cases of the NER model can be seen in:

  • Customer Support: Every company has a customer support team to handle customer requests and complaints. These requests may be about the installation, maintenance, and other queries regarding the product. NER identifies and understands demands and automatically sends them to the respective support desk. 
  • Filtering resumes: So many resumes come in for a particular job. However, the recruiting team doesn’t have the time to go through each application. The NER model does the job of filtering out resumes through an automated system. 

This works when you mention specific vital skills needed for the specified role. The NER model is then trained to find the particular skill—sets from the entities. If your resume meets the requirements, it moves to the next stage.

  • Electronic Healthcare Data: NER model can be used to build a robust medical healthcare system. It can identify the symptoms from the patients’ data and diagnose the disease or the health issue they’re facing. Furthermore, their treatment can be rightly decided based on the correlated data. 
  1. Sentiment Analysis

Another NLP technique that is widely used is sentiment analysis. This method is most useful in cases where customer surveys, reviews, and social media comments are involved. You can understand people’s sentiments based on their opinions and feedback. 

The simplest method to understand the sentiment is a 3—point scale: positive/neutral/negative. For an in—depth analysis, you can add more categories and measure the feedback in a numeric score. This method will be helpful for brands as they can understand:

  • Critical aspects of a brand’s product or service that customers care about
  • The reactions and the underlying intentions of the users concerning those aspects
  1. Text Summarization
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As the name suggests, Text Summarization is an NLP technique that condenses large chunks of text. It is used commonly in news articles and research articles. By automating this task, you not only reduce the initial size of the text but preserve the key elements and meaning of the text. 

There are two common approaches to text summarization. The extraction approach creates a summary by extracting parts from the text. The abstraction approach creates a summary by generating fresh content that retains the original text’s meaning. 

  1. Aspect Mining

Aspect Mining identifies crosscutting concerns in the text. It provides insights enabling the classification of different aspects of the content, such as news and social media. When combined with sentiment analysis, it extracts the complete intent of the text. 

Aspect mining can be beneficial in opinion mining, where you can determine the central aspect of each review. In addition, this extracted data can be helpful for marketing and sales purposes. 

  1. Topic Modeling

Topic Modelling is a complicated NLP method that can identify natural topics in the text. The benefit of using topic modeling is that it is an unsupervised technique that doesn’t require model training or a labeled training dataset.

Latent Dirichlet Allocation(LDA) is an example of the topic model where the text of a document is organized into a particular topic. It builds a topic per document and then distributes words per topic model. These models are known as Dirichlet distributions. 

In closing

Establishing an AI—based data extraction system within your business may seem complicated with all the technical requirements. However, the five NLP models mentioned above are easy to set up and use. You don’t need any prior coding experience to get these software models up and running in your business.

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