NER Model Using Label-Word Relation Matrix and Curriculum Learning
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Solution Overview
Problem
Named Entity Recognition (NER) models face challenges in accurately identifying target entities due to confusion from highly related words, domain-specific terms, and compounded labels, which affect learning and classification in natural language processing tasks.
Innovation Solution
The encoder-decoder framework employs enhanced label embeddings and curriculum learning, utilizing a label-word relation matrix to transfer semantic emphasis and incorporate domain-specific knowledge, and decomposing labels to distinguish between entity types and boundaries, thereby improving the recognition of target entities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If label embedding techniques with attention mechanism are applied to NER task, then the model can capture semantic relatedness between words and labels, but highly related words (synonyms and contextual words) confuse the model into incorrectly applying labels
Solution Approach 1:
The label space is segmented into two distinct components: entity type labels (e.g., PERSON, LOCATION) and boundary labels (B-, I-, O-). This segmentation allows the model to separately learn entity type classification and boundary detection, preventing confusion between highly related words that should not receive entity labels and actual target entities.
Solution Approach 2:
The patent introduces an intermediary attention mechanism that computes attention scores between words and label embeddings, then uses these scores to selectively enhance or suppress word representations. This intermediary layer allows the model to distinguish between words that are semantically related but should not be labeled versus words that are actual target entities.
2Adaptability or versatility
If pre-trained models are applied to domain-specific text, then general language understanding is available, but domain-specific terms confuse the model
Solution Approach 1:
The model dynamically adjusts the embedding parameters for domain-specific terms by computing attention-based enhancements to the pre-trained embeddings. Words with high attention scores from domain-specific label embeddings receive parameter adjustments that better reflect their domain-specific meanings, allowing the model to adapt general language understanding to domain-specific contexts.
3Loss of information
If compound labels (B-Person, I-Person, etc.) are used for sequence labeling, then entity boundaries and types are encoded, but models cannot learn whether individual words contribute to boundary detection or entity type classification
Solution Approach 1:
The compound labels are segmented into separate entity type labels and boundary labels. By providing these as distinct training targets, the model can independently learn which words contribute to entity type classification and which contribute to boundary detection, eliminating the confusion present in compound label approaches.
Data Source
AI summary
A system and method are disclosed for training a NER model configured to perform an NER task. The system and method advantageously utilize a label-word relation matrix to incorporate label semantic information into the attended text embedding. The system and method augment and enhance the design of the label-word relation matrix derived from label embeddings, which brings multiple benefits. In addition to the enhanced label-word relation matrix, the system and method further incorporate a novel training strategy that fits with the label embedding technique. With these improvements upon conventional NER systems, the system and method are effective for both open-domain and closed domain NER tasks.