Convolutional Neural Network Semantic Identification for Ambiguous Text
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Solution Overview
Problem
Existing semantic identification methods in customer service robots and question answering systems face accuracy issues due to ambiguity in word segmentation, leading to low accuracy in understanding user intentions.
Innovation Solution
A method using a convolutional neural network with varying kernel widths to convolve word vector matrices, perform maximum pooling, and combine identification features to improve semantic identification accuracy by extracting sentence keywords of different lengths, thereby reducing ambiguity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If general word segmentation methods are used to segment sentences into words, then the segmentation process is simple and fast, but ambiguity in certain words leads to low accuracy of word segmentation and semantic identification
Solution Approach 1:
The patent segments the identification process into multiple stages: first segmenting the sentence into words using word segmentation techniques, then further segmenting and analyzing n-grams (sequences of n words) to extract semantic features at multiple levels. This multi-level segmentation approach resolves ambiguity by examining word sequences in context rather than isolated words.
Solution Approach 2:
The patent transitions from one-dimensional word-level analysis to multi-dimensional analysis by incorporating n-gram sequences of different lengths (e.g., 2-grams, 3-grams, 4-grams). This adds dimensional depth to the semantic feature extraction, allowing the system to capture contextual relationships and resolve ambiguities that single-word analysis cannot detect.
2Measurement precision
If multiple n-gram sequences of different lengths are used to extract semantic features, then semantic identification accuracy is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent performs preliminary word segmentation and n-gram generation before semantic feature extraction. By pre-processing the sentence into segmented words and organizing them into n-gram sequences in advance, the system reduces computational complexity during the actual semantic analysis phase, as the structural work has already been completed.
Solution Approach 2:
The patent extracts semantic features from multiple n-gram sequences (2-grams, 3-grams, 4-grams and beyond), using more computational resources than strictly necessary for basic accuracy. This excessive action ensures comprehensive semantic coverage and resolves ambiguities by examining all possible word sequence lengths, accepting the computational cost for superior accuracy.
Data Source
AI summary
The present disclosure discloses a method and device for identifying information. The method for identifying information includes acquiring a word vector matrix of the information; for each of a plurality of convolutional kernel widths of a convolutional neural network, convolving each convolutional kernel corresponding to the width with the word vector matrix of the information to acquire a convolutional vector corresponding to each convolutional kernel, and performing a maximum pooling operation on each convolutional vector to acquire an identification feature corresponding to the width; combining identification features corresponding to various convolutional kernel widths to acquire an identification feature of the information; and identifying the information according to similarity of the identification feature of the information.