Bidirectional LSTM Entity Recognition with Shared Fully Connected Layers
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Solution Overview
Problem
Existing named-entity recognition (NER) systems face challenges in accurately identifying entities in text due to their reliance on brittle pattern matching techniques, which fail to handle contextual information and semantically similar words, leading to inefficiencies and errors, especially in contact center applications where context awareness is crucial.
Innovation Solution
A system utilizing a Long Short-Term Memory (LSTM) neural network with 100 memory cells processes sentences by performing forward and backward passes, concatenating results, and using shared fully connected neurons to capture contextual information through features like word embeddings, parts-of-speech encoding, and fixed-size ordinally forgetting encoding, enabling context-aware entity prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If pattern matching techniques are used for named-entity recognition, then the system is simple to implement, but the accuracy of entity identification deteriorates due to inability to handle contextual information
Solution Approach 1:
The patent replaces traditional mechanical pattern matching techniques with a neural network-based system that processes contextual information. The LSTM neural network captures sequential dependencies and contextual relationships in text, enabling accurate entity identification without relying on rigid pattern matching rules.
Solution Approach 2:
The patent transforms the approach by changing from discrete pattern matching to continuous contextual representation. By using word embeddings and contextual vectors, the system captures semantic relationships and contextual nuances, significantly improving entity recognition accuracy while maintaining implementation feasibility through standardized neural network architectures.
2Device complexity
If traditional NER systems are used, then the device complexity is low, but the ability to handle contextual ambiguity deteriorates leading to errors
Solution Approach 1:
The patent replaces simple rule-based systems with neural network models that inherently handle contextual ambiguity. The bidirectional LSTM architecture processes text in both forward and backward directions, capturing contextual relationships from all directions, which significantly improves reliability in ambiguous contexts.
Solution Approach 2:
The patent adds dimensional depth to the NER system by incorporating bidirectional contextual processing. The forward and backward passes create a multi-dimensional view of contextual information, allowing the system to resolve ambiguities by considering relationships from multiple temporal directions simultaneously.
3Measurement precision
If context-aware processing is implemented, then entity recognition accuracy improves, but the computational complexity and processing time increase
Solution Approach 1:
The patent performs preliminary processing by pre-computing forward and backward contextual representations. These pre-computed vectors are then combined and processed through the fully connected layer, allowing the system to efficiently handle contextual analysis without excessive processing time during inference.
Solution Approach 2:
The patent segments the contextual processing into distinct forward and backward passes, each handling specific directional dependencies. This segmentation allows parallel computation of contextual representations, improving processing efficiency while maintaining high accuracy in entity recognition.
4Loss of information
If bidirectional LSTM with feature concatenation is used, then contextual information capture improves, but the model complexity increases
Solution Approach 1:
The patent merges forward and backward LSTM representations by concatenating their outputs. This combination preserves all contextual information from both directions while using a shared fully connected layer to process the combined features, achieving efficient information retention without excessive model complexity.
Solution Approach 2:
The patent implements a universal fully connected layer that processes both forward and backward contextual features through the same parameters. This shared architecture maintains model efficiency and reduces complexity while effectively capturing contextual relationships from multiple directions.
Data Source
AI summary
A system and method are presented for model derivation for entity prediction. An LSTM with 100 memory cells is used in the system architecture. Sentences are truncated and provided with feature information to a named-entity recognition model. A forward and a backward pass of the LSTM are performed, and each pass is concatenated. The concatenated bi-directional LSTM encodings are obtained for the various features for each word. A fully connected set of neurons shared across all encoded words is obtained and the final encoded outputs with dimensions equal to the number of entities is determined.