Automating Email Processing with AI-Powered Named Entity Recognition for Efficient Data Labeling

Content by: Gaurav Mittal

In today’s data-driven business environment, organizations face significant challenges in managing the vast volume of unstructured email communications. Companies heavily rely on emails for handling customer service requests, sales inquiries, support tickets, and other critical operations. However, the unstructured email content which varies in style, tone, and complexity – creates obstacles for systematic organization and analysis.

The primary challenge lies in the manual processing of these emails. For instance, insurance service agents are required to manually review thousands of daily emails and extract critical information such as claims details, policy numbers, and customer inquiries. This labor-intensive process requires agents to read, interpret, and manually enter information into their database in structured formats. This approach is not only time-consuming, but also prone to human error, leading to data inconsistencies and delayed response times.

Furthermore, the unstructured nature of emails presents additional complexities in data extraction. Valuable information is often buried within lengthy email threads, intermingled with non-essential elements like greetings, disclaimers, and email signatures. This format makes it particularly challenging to extract insights that are very important for business growth.

The solution lies in converting this unstructured content into a structured format, enabling organizations to effectively categorize, label, and organize their data. This transformation makes information readily accessible for integration with various business systems, such as customer relationship management (CRM) platforms and analytics tools. Once structured, the data becomes an asset for business intelligence. Organizations can analyze patterns in customer feedback, track common complaints, and identify emerging trends. This systematic approach to data management enables companies to understand customer preferences better and proactively address issues before they escalate, ultimately leading to improved customer satisfaction and more efficient operations.

In this article, I’ll present an innovative solution that uses UiPath’s automation tool capabilities combined with ML model – Named Entity Recognition (NER). This approach will help streamline data labeling processes and enhance information extraction accuracy, transforming email content at runtime and facilitating the shift from unstructured to structured data.

System Architecture

Email Processing with UiPath: UiPath bots can read incoming emails, extract the email content, and route it to the next processing stage. UiPath’s workflow automation capabilities also make it easy to extract attachments, analyze metadata, and categorize the emails based on predefined rules.

Text Preprocessing: Once emails are extracted, they pass through a preprocessing pipeline to prepare the content for entity recognition. This pipeline includes HTML cleanup, removing elements like email signatures and disclaimers, and standardizing text. These steps ensure the email content is clean and suitable for natural language processing. Key components include:

– HTML cleanup and normalization of text to remove artifacts.

– Removal of irrelevant information like signatures and legal disclaimers.

– Segmentation into sentences for more structured analysis.

– Token normalization to standardize variations in words and phrases.

1. Structured Data Extraction with NER: By integrating NER models, the company can identify and label critical entities such as dates, customer names, policy numbers, and claim amounts within the unstructured email text. The NER model classifies and organizes these elements into structured data that can be directly entered into the company’s systems.
2. Improved Efficiency and Decision-Making: With structured data extracted and organized, the company can run analytics on common claim issues, track response times, and even flag potential fraud cases more efficiently.

This combined approach not only cuts down processing time but also improves accuracy and reduces operational costs. It’s an ideal solution for businesses dealing with large volumes of unstructured data, enabling them to transform email content into valuable, actionable insights.

By integrating UiPath with a preprocessing pipeline and NER, this system architecture enables organizations to efficiently transform unstructured email content into actionable, structured data.

Code for Step-by-step runtime flow:

Step 1: Create ML spacy model using the trained dataset, generate *.pkl file, and load

Step 2: Feed email’s preprocessed text in the text_to_process variable, model will generate output –

• Pass email text from UiPath:

Use an Invoke Python Method activity in UiPath to pass the email text.

• Extract entities:

In the Python script, use the loaded spaCy model to process the text and extract the named entities.

• Serialize entities:

Convert the extracted entities into a JSON string using the json.dumps() function in Python.

• Output JSON:

Return the JSON string to UiPath using the Out argument of the Invoke Python Method activity.

• Deserialize JSON: Use the Deserialize JSON activity in UiPath to convert the JSON string back into a structured data type (e.g., a dictionary or a list).
• Process entities: Use the extracted entities in your UiPath workflow as needed.

Conclusion

In conclusion, integrating UiPath’s automation tool with Named Entity Recognition (NER) models provides a robust solution for efficiently processing unstructured email content and generating structured, labeled datasets. This approach significantly minimizes manual effort and boosts accuracy in extracting and categorizing entities. By reducing processing time per email to under 30 seconds and improving data extraction accuracy to 95%, organizations can shift their focus from data entry to more strategic tasks, like resolving claims issues. This automation utility can enhance customer satisfaction by providing faster response times.