Multi-Task Neural Network for Query Intent and Named Entity Recognition
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Solution Overview
Problem
Current Named Entity Recognition (NER) techniques fail to accurately determine query intent alongside named entities in unstructured text, leading to separate management and analysis of these tasks, which limits accuracy and responsiveness in applications like retail websites.
Innovation Solution
A neural network model is employed that incorporates both a named entity tag learning loss function and an intent classification loss function, allowing for concurrent determination of query intent and named entities, improving identification through shared layers and multitask learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate NER and intent classification models are used, then task specialization is improved, but system complexity and processing time increase
Solution Approach 1:
The patent combines NER and intent classification into a single unified neural network model that processes query text simultaneously for both tasks. The model shares embedding layers and uses a single prediction head that outputs both entity tags and intent classifications, eliminating the need for separate models while maintaining accuracy through multi-task learning with appropriate loss functions.
Solution Approach 2:
The unified model is designed to perform multiple functions: it simultaneously identifies named entities, classifies query intent, and leverages shared representations across tasks. The architecture enables the model to be universally applicable to both NER and intent classification tasks without requiring separate specialized models, reducing overall system complexity.
2Reliability
If separate NER and intent analysis are performed, then task independence is maintained, but processing speed and responsiveness decrease
Solution Approach 1:
The unified model enables continuous processing where NER and intent classification occur simultaneously in a single forward pass through the neural network. The shared layers continuously learn from both tasks together, eliminating the need for sequential processing and improving query responsiveness while maintaining accurate analysis through joint training with combined loss functions.
3Measurement precision
If intent information is integrated into NER, then identification precision improves, but model complexity increases
Solution Approach 1:
The model uses parameter changes in the loss function design to balance the complexity-precision tradeoff. By implementing a combined loss function that weights NER and intent classification losses appropriately, the model integrates intent information to improve entity identification precision while controlling complexity through parameter optimization rather than architectural complexity.
Data Source
AI summary
Methods and systems for performing multi-task learning of query intent and named entities are provided. One method includes receiving a query comprising query text. The method further includes providing the query text to a neural network model implemented on a computing system, the neural network model having a plurality of layers, wherein at least one layer comprises a plurality of loss functions including a named entity tag learning loss function and an intent classification loss function. The method also includes obtaining, from the neural network model, an identification of a named entity and a query intent derived from the query text. A query response may be formulated based, at least in part, on the named entity and query intent.