From chaos to clarity: why your business needs document classification

Documents, emails, chat logs, and social media posts: most of the textual data businesses are generating today is both unstructured and voluminous.

Such unstructured data usually contain valuable insights to help businesses make better-informed decisions, improve customer experiences, and gain a competitive edge. However, the sheer volume and complexity of unstructured data can make it difficult to extract meaningful insights.

This is where NLP services come in. By leveraging the power of natural language processing and machine learning, businesses can automatically analyze and classify their avalanches of texts, making it easier to find relevant information and gain insights that can drive business value. One of the most widespread applications of NLP is document classification.

What is document classification?

Document classification categorizes documents or parts of documents (e.g., chapter, paragraph) into categories based on their content, structure, or other characteristics. This type of information retrieval involves analyzing texts to determine their topic or theme and assigning them to one or more categories or classes.

Document classification allows businesses to efficiently manage large volumes of documents, extract valuable insights, and automate decision-making processes. Additionally, organizing documents into categories enables quick identification of and access to accurate, relevant and up-to-date information.

There are different methods and techniques for document classification, including rule- and ML- (machine learning) based classifications, as well as hybrid approaches.

How document classification works

Document classification uses a set of predefined rules or machine learning algorithms to analyze a document’s content, structure, or other characteristics and assign it to one or more predefined categories or classes.

Rule-based document classification

In rule-based classification, the rules are defined by human experts or based on domain knowledge. The rules extract specific features or keywords from the documents and assign them to predefined categories. For example, a rule-based system for classifying news articles might use keywords such as “politics,” “sports,” and “entertainment” to categorize articles into different topics.

Machine learning document classification

When using the machine learning approach, the classification model is typically trained using a supervised learning approach. The training set is labeled by human experts or annotated automatically. Via pre-labeled document examples, the model thus learns to identify patterns and features in the training set and uses them to classify new, unlabeled documents.

Machine learning-based document classification uses natural language processing (NLP) techniques to extract features and information from text data. NLP, a branch of artificial intelligence, deals with the interactions between computers and natural human languages, such as English, Spanish, Chinese, et cetera.

NLP techniques enable machine learning algorithms to process, understand, and generate human language. These techniques involve breaking down text data into smaller units, such as words or phrases, and using statistical methods to identify patterns and relationships between these units.

After extracting these features from the text, NLP techniques use them as input for the machine learning algorithm. Next, the machine learning algorithm learns to associate these features with predefined categories or classes, enabling it to classify new, unseen documents based on their content.

Comparison of the automatic document classification techniques

A comparison of the most common document classification methods reveals that each method of document classification has its strengths and weaknesses:

• Rule-based classification

  A rule-based classification relies on human expertise to define the rules used to classify documents. It is typically used for simple classification tasks with clear and well-defined rules. Although easy to implement, rule-based classification requires a lot of manual effort and is unsuitable for complex or large-scale classification tasks. Also, updating and adding new classes requires increasingly more time because they cannot conflict with previous ones.

• Machine learning-based classification

  A machine learning-based classification system uses algorithms to learn patterns and relationships from labeled data automatically. It is adequate for complex and large-scale classification tasks. Also, ML simply enables much higher performance levels, and it is much more robust over time. Still, it requires labeled training data and expertise in machine learning.

• Hybrid methods

  Hybrid methods combine rule-based and machine learning-based techniques to improve classification accuracy and reduce the amount of labeled data required.

These methods typically use rules to pre-process the data and extract features, which are then used as input for the machine learning algorithm.Each method of document classification has its strengths and weaknesses. Here is a comparison of the most common document classification methods:

Machine learning-based classification is the most effective method for complex and large-scale document classification tasks. However, rule-based classification is suitable for simple classification tasks, and hybrid approaches can improve accuracy and reduce the amount of labeled data required. The choice of method depends on the specific needs of the business, including the amount of data available, the complexity of the classification task, and the available expertise and resources.

Seven benefits of document classification powered with natural language processing

NLP-powered document classification provides several benefits to businesses, including:

1. Improved efficiency: Automatic document classification can automatically organize and classify large volumes of documents, reducing the time and effort required for manual classificaiton.
2. Increased accuracy: NLP techniques can extract essential features and information from text data, such as keywords, topics, and sentiment, which can improve the accuracy of document classification.
3. Better decision-making: NLP-powered document classification can help businesses make better-informed decisions by providing insights into the content of the documents and identifying basic patterns and trends.
4. Enhanced customer experience: Automatic document classification can help businesses quickly identify and respond to customer inquiries and issues, improving the overall customer experience.
5. Increased productivity: NLP-powered document classification can automate repetitive manual tasks, freeing employees to focus on higher-value activities.
6. Cost savings: Automated document classification can help reduce costs by automating tasks and reducing the need for manual processing.
7. Better regulatory compliance: NLP-powered document classification can help businesses comply with regulatory requirements by identifying and classifying documents according to specific criteria.

Overall, businesses can classify documents with NLP-powered tools, gaining competitive advantages by improving efficiency, accuracy, decision-making, customer experience, productivity, cost savings, and regulatory compliance.

Use cases for document classification

NLP-powered document classification provides advanced capabilities that can be applied to various use cases in different industries and functions. Here are some examples:

• Customer support: NLP-powered document classification can automatically categorize and prioritize customer inquiries and complaints, helping businesses quickly identify and respond to customer inquiries and issues, improving the response time and the overall customer experience, which leads to higher overall customer satisfaction, and, thus, customer retention.
• Content categorization/Topic modeling: NLP-powered document classification can identify, extract and analyze topics from large volumes of text data, such as research papers, and customer feedback. This can help businesses gain insights into emerging trends and topics and stay up to date with the latest developments in their industry, assisting businesses in enhancing information retrieval and knowledge management.
• Error classification by document descriptions. Suppose you have tons of error descriptions and they’re not classified or only partially classified. In that case, document classification can locate similar already-resolved errors and automate responses.
• Fraud detection: NLP-powered document classification can identify patterns and anomalies in financial documents, such as invoices, receipts, and transaction records. For example, government agencies can use document classification to categorize and classify legal documents, tax forms, and regulatory filings, improving regulatory compliance, reducing the processing time of detecting fraudulent activity, and preventing financial losses.
• Compliance monitoring: NLP-powered document classification can monitor regulatory filings and identify non-compliant or fraudulent documents. This can help businesses ensure compliance with regulatory requirements and avoid legal penalties.
• Customer sentiment analysis: Automatic document classification can analyze customer feedback, reviews, and social media posts, identifying sentiment and opinions about a product or service. This can help businesses understand customer needs and preferences and improve their products and services accordingly.

Steps for implementing document classification in a business

Implementing document classification in a business involves several key steps:

1. Identify the business problem

   The first step is to identify the business problem that can be solved by machine learning models. This could be anything from improving customer service to reducing fraud or compliance monitoring. It makes sense to already involve an AI partner to verify if this is a good candidate for AI or if the problem has been formulated in a good way for AI.

2. Gather and prepare the data

   Once the business problem has been identified, the next step is to gather and prepare the data to be used for training and testing the document classification model. This may involve collecting data from various sources, such as customer feedback, financial documents, or regulatory filings.

3. Train the document classification model

   Once the text classification technique has been chosen (experts can determine what makes sense through an iterative process), the next step is to train the model on a labeled data set. This involves providing the model with examples of documents that have already been classified and allowing it to learn from those examples.

4. Test and validate the model

   After the model has been trained, it needs to be tested and validated to ensure that it is accurate and reliable. This involves testing the model on a set of documents that haven’t been used for training and comparing the model’s predictions to the actual classifications.

5. Deploy the model

   Once the model has been tested and validated, it can be deployed in the business to classify new documents automatically. This may involve integrating the model with existing systems or creating a new system to handle the classification process. Additionally, as machine learning requires dedicated underlying infrastructure, if you don’t have one, then it has to be created.

6. Monitor and update the model

   Finally, it is crucial to monitor the performance of the document classification model and update it as needed. This may involve retraining the model on new data or modifying it to improve its accuracy or efficiency.

Implementing document classification in a business can be complex, and it is vital to have the right expertise and resources to ensure success. However, with the right approach and tools, text classification can provide significant benefits to a business, including improved efficiency, reduced costs, and better decision-making.

Challenges and considerations for document classification and the ways to overcome them

There are several challenges and considerations that businesses should keep in mind when implementing document classification. Here are some of the most common challenges and ways to overcome them:

Data privacy and security

Document classification often involves sensitive or confidential data, which can raise concerns about data privacy and security. To overcome this challenge, businesses should implement appropriate data security and privacy protocols, such as data encryption and access controls, to ensure that their classified data is protected. There might be a need to anonymize the data before it can be processed by other algorithms.

Quality of data

One of the biggest challenges in text classification is ensuring the quality of the data used to train the model. Incomplete, inconsistent, or inaccurate data can lead to a model that produces unreliable or biased results. To overcome this challenge, businesses should invest in data cleaning and quality assurance processes to ensure that their data is accurate and reliable.

Cost and time

Text classification can be a time-consuming and resource-intensive process, which can be a challenge for businesses with limited budgets or tight timelines. To overcome this challenge, companies should carefully evaluate the costs and benefits of document classification and choose the most appropriate tools and techniques based on their needs and resources.

Business needs and objectives

A document classification system should be driven by the specific needs and goals of the business, rather than being implemented as a one-size-fits-all solution. To overcome this challenge, companies should carefully define their objectives and requirements for document classification, and choose the most appropriate tools and techniques based on their specific needs.

Integration with existing systems

Document classification often needs to be integrated with existing business systems, such as customer relationship management (CRM) or enterprise resource planning (ERP) systems. To overcome this challenge, businesses should carefully evaluate the integration requirements for their text classification solution and work with vendors and IT professionals to ensure that the solution can be seamlessly integrated with their existing systems.

Instead of conclusion: the best tips on successful AI document classification

Having delivered over 100 successful artificial intelligence projects, MindTitan experts see every day that NLP-enhanced data classification can provide significant efficiency, productivity, and profitability growth. As you move forward your business goals with the help of AI, here are some questions to keep in mind:

• Have you thought through your business case: Why do you need document classification? What is the business case behind the classification? What is the business problem that it helps to resolve? (To learn more about answering these questions, please read our guide on machine learning canvas.)
• What integrations do you need to run the business case?
• What is the applicable prediction accuracy you want to achieve? (i.e., 85% of documents are classified correctly.)
• How will the tool be used by your customers, employees or other systems?