Coreference-Aware NER Framework for Consistent Entity Prediction
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Solution Overview
Problem
Traditional named entity recognition (NER) models fail to effectively utilize coreferential relations, leading to globally inconsistent semantic type predictions due to treating input text as a linear sequence of words without considering non-sequential structural information.
Innovation Solution
A coreference-aware representation learning framework is introduced, incorporating a coreference component and regularization term into the CNN-BiLSTM-CRF model to leverage global structural information and ensure consistent label predictions across coreferential entities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional neural sequence labeling models (BiLSTM-CRF) are used to process input sentences, then the model can achieve reasonable NER performance, but it fails to utilize coreferential relations leading to globally inconsistent semantic type predictions
Solution Approach 1:
The system performs coreference resolution as a preliminary step before NER labeling. A coreference parser identifies coreferential clusters of entity mentions across the document, and this coreference information is prepared in advance and integrated into the NER model's feature representation, allowing the model to make consistent predictions across all mentions of the same entity.
Solution Approach 2:
The patent introduces an intermediary coreference component that bridges the gap between raw text and the NER model. This component extracts coreferential relations and transforms them into features that can be consumed by the BiLSTM-CRF model, thereby mediating the information flow and enabling the model to utilize coreference information without fundamentally changing its architecture.
2Device complexity
If the model treats input text as a linear sequence of words, then the model architecture remains simple and computationally efficient, but it cannot capture non-sequential structural information such as coreferential relations
Solution Approach 1:
The system segments the NER task into two distinct components: (1) a coreference resolution module that identifies coreferential clusters and extracts non-sequential structural information, and (2) a sequence labeling module (BiLSTM-CRF) that processes the text linearly. This segmentation allows each component to specialize - the coreference module captures global structural information while the sequence labeling module maintains computational efficiency.
Solution Approach 2:
The patent adds another dimension to the traditional linear sequence modeling by incorporating coreference information that operates at the document level rather than the sentence level. This dimensional extension allows the model to capture long-range dependencies and non-sequential relations without abandoning the efficient linear processing approach.
3Reliability
If coreference information is integrated into the NER model, then globally consistent predictions can be achieved, but the model complexity and computational requirements increase
Solution Approach 1:
The coreference component acts as an intermediary that processes coreference information separately and transforms it into features suitable for the existing BiLSTM-CRF model. This approach integrates coreference awareness without fundamentally restructuring the proven sequence labeling architecture, thereby maintaining relative simplicity while achieving global consistency.
Solution Approach 2:
The patent applies coreference information locally at the feature level rather than globally restructuring the entire model. The coreference features are incorporated as additional inputs or modifiers to the existing BiLSTM-CRF pipeline, allowing the model to leverage coreference information where needed without increasing overall architectural complexity.
Data Source
AI summary
Previous neural network models that perform named entity recognition (NER) typically treat the input sentences as a linear sequence of words but ignore rich structural information, such as the coreference relations among non-adjacent words, phrases, or entities. Presented herein are novel approaches to learn coreference-aware word representations for the NER task. In one or more embodiments, a “CNN-BiLSTM-CRF” neural architecture is modified to include a coreference layer component on top of the BiLSTM layer to incorporate coreferential relations. Also, in one or more embodiments, a coreference regularization is added during training to ensure that the coreferential entities share similar representations and consistent predictions within the same coreference cluster. A model embodiment achieved new state-of-the-art performance when tested.