Live Chat Named Entity Recognition for Faster Data Retrieval
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Solution Overview
Problem
Live agents in chat sessions often struggle to identify and locate important data within dialogues, requiring time-consuming searches and resource-intensive database queries, which hampers efficiency and wastes computing resources.
Innovation Solution
Implementing a natural language processing (NLP) system with named entity recognition (NER) using machine learning (ML) models and rule-based engines to automatically identify, highlight, and provide additional data for named entities in chat dialogues, allowing real-time assistance to live agents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If live agents manually search for data in chat dialogues and execute database queries, then they can retrieve required information, but it consumes excessive time and computing resources
Solution Approach 1:
The NLP system performs preliminary processing of chat dialogues by automatically identifying and extracting named entities (such as account numbers, transaction IDs, product names) before agents need to search for them. This advance preparation eliminates the need for manual searching during customer interactions, directly reducing search time while maintaining accurate data identification.
2Measurement precision
If live agents manually search texts and execute database queries, then they can locate relevant data, but it wastes computing resources
Solution Approach 1:
The system implements self-service by automatically extracting named entities from chat dialogues without requiring agent intervention. The NLP system independently identifies, classifies, and retrieves relevant data, eliminating the need for agents to manually execute database queries. This automation significantly reduces computing resource consumption while maintaining accurate data retrieval.
3Measurement precision
If live agents manually identify and lookup data in chat dialogues, then they can provide accurate information, but it reduces service efficiency
Solution Approach 1:
The NLP system performs preliminary identification and extraction of named entities from chat dialogues before agents need to provide information. By pre-processing the dialogue text and highlighting relevant entities, the system maintains information accuracy while dramatically improving service efficiency, allowing agents to focus on customer interaction rather than data searching.
4Ease of operation
If no automated system is used, then chat dialogues remain unprocessed, but agents must manually handle all data identification tasks
Solution Approach 1:
The NLP system acts as an intermediary between chat dialogues and agents. It automatically processes dialogue text, identifies named entities, and presents extracted information to agents in a user-friendly format. This intermediary layer reduces agent workload by handling routine data identification tasks while maintaining high data processing speed through automated analysis.
Data Source
AI summary
There are provided systems and methods for named entity recognition in chat dialogues for customer relationship management systems. A service provider, such as an electronic transaction processor for digital transactions, may provide live chat service channels for assistance through live agents and chatbot services. When interacting with these channels, a user may engage in a chat dialogue with live agents. This may include lines of texts corresponding to the exchanged messages and may include named entities for particular types or categories of words that refer to a particular object or thing. To identify these named entities, a natural language processor may utilize machine learning and other engines for named entity recognition in customer relationship management systems to highlight the named entities in live service chats. Agents of the systems may view content that identify the named entities and interact with the named entities to view descriptions.