Multimodal NER via Attention-Weighted Bi-LSTM
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Solution Overview
Problem
Named entity recognition (NER) systems face challenges in identifying entities from noisy user-generated data, such as social media posts, due to inconsistent syntax, misspelled words, and lack of metadata, making it difficult to accurately extract named entities from sparse and multimodal messages.
Innovation Solution
A bi-directional long short-term memory unit (Bi-LSTM) word/character-based NER model is implemented, combined with a modality attention module that incorporates visual context and attenuates irrelevant modalities to focus on primary information, enhancing the extraction of adaptive contexts and tokens in multimodal messages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional information extraction schemes are used, then they work well when terms are input correctly, but they fail to extract terms from sparse noisy data such as social media posts with misspelled words
Solution Approach 1:
The patent combines multiple modalities (text, image, audio) into a unified neural network model that processes them jointly. The multi-modal neural network integrates features from different data types to improve entity recognition accuracy on noisy social media posts, allowing the system to leverage complementary information from various modalities to overcome limitations of individual modalities.
Solution Approach 2:
The patent transforms input data into different representation spaces through neural network processing. The model applies various transformations including embedding layers, feature extraction, and attention mechanisms that change the parameter representation of input data, enabling the system to handle noisy inputs by mapping them to more robust feature spaces.
2Measurement precision
If multimodal data is processed, then recognition accuracy improves, but system complexity increases
Solution Approach 1:
The patent divides the processing of each modality into separate neural network branches that extract features independently before combining them. The model segments the multimodal input processing into distinct pathways for text, image, and audio, each with specialized feature extraction layers, reducing the complexity of processing all modalities simultaneously while maintaining integration benefits.
Solution Approach 2:
The patent introduces an attention mechanism as an intermediary that selectively weights and integrates features from different modalities. The attention module acts as a mediator that determines which modalities and features are most relevant for entity recognition, simplifying the integration process by dynamically prioritizing important information while downweighting less relevant inputs.
3Adaptability or versatility
If visual context is incorporated, then extraction of adaptive contexts improves, but processing time increases
Solution Approach 1:
The patent performs preliminary feature extraction and encoding of visual context before the main entity recognition process. The model pre-processes images and audio to extract relevant features and store them in compressed representations, so that during inference, the integration of visual context requires minimal additional processing time while still providing adaptive context extraction.
Data Source
AI summary
A caption of a multimodal message (e.g., social media post) can be identified as a named entity using an entity recognition system. The entity recognition system can use an attention-based mechanism that emphasis or de-emphasizes each data type (e.g., image, word, character) in the multimodal message based on each datatypes relevance. The output of the attention mechanism can be used to update a recurrent network to identify one or more words in the caption as being a named entity.