Factoid Extraction Neural Network for Natural Language Queries
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Solution Overview
Problem
Conventional AI systems fail to accurately identify and extract crucial information such as place, time, reason, or manner from user queries, limiting their understanding of user intent and response generation in natural language interactions.
Innovation Solution
A method and device that create an input vector for each target word in a sentence, including POS, word embeddings, dependency labels, and semantic role labels, processed through a trained neural network with bidirectional LSTM, LSTM, and Softmax layers to assign factoid tags and extract associated text, providing a comprehensive response to user queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional AI systems use basic intent identification methods, then the system complexity remains low, but the accuracy of extracting crucial information such as place, time, reason, or manner deteriorates
Solution Approach 1:
The system segments the natural language processing task into multiple specialized components: intent identification, factoid extraction, and context capture. Each component is handled by dedicated neural network layers (bidirectional LSTM for sequence modeling, attention mechanism for key feature selection, dense layers for classification), allowing complex information extraction without overwhelming system complexity
Solution Approach 2:
The patent introduces semantic role labeling as an additional dimensional layer beyond basic intent classification. By tagging words with semantic roles (agent, patient, location, time, reason, manner) in addition to intent categories, the system captures crucial information without requiring a complete redesign of the architecture
2Loss of information
If conventional systems only identify user intent, then the processing speed remains high, but the completeness of understanding user query deteriorates
Solution Approach 1:
The system performs preliminary processing by pre-computing word embeddings and part-of-speech tags for all words in the input sentence before the main classification step. The bidirectional LSTM pre-processes the entire sequence to capture contextual relationships, so that when factoid extraction occurs, the system already has structured information ready, maintaining processing speed while improving completeness
Solution Approach 2:
The neural network architecture serves multiple functions simultaneously: the bidirectional LSTM layer performs both sequence modeling and contextual understanding, the attention mechanism identifies both key words and their relationships, and the dense layers perform both intent classification and factoid extraction. This multi-functionality reduces the need for separate processing stages
Data Source
AI summary
A method an system for extracting factoid associated words from natural language sentences is disclosed. The method includes creating an input vector that includes a plurality of parameters for each target word in a sentence. For a target word, the plurality of parameters includes a Part of Speech (POS) vector, a word embedding, a word embedding for a head word of the target word, a dependency label, and a semantic role label. The method includes processing for each target word, the input vector through a trained neural network and assigning one or more factoid tags to each target word in the sentence. The method includes extracting text associated with factoids from the sentence based on the one or more factoid tags. The method further includes providing a response to the sentence inputted by the user based on the text associated with the factoids.