Word Vector Generation Using N-ary Character Convolutional Neural Networks
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Solution Overview
Problem
Existing natural language processing solutions for generating word vectors lack accuracy, as they fail to effectively capture the semantic information of words, particularly in contexts where nuanced character representations are necessary.
Innovation Solution
The method involves segmenting a corpus to obtain words, establishing feature vectors based on n-ary characters, training a convolutional neural network using these feature vectors and context words, and generating word vectors through convolutional and pooling calculations, with a loss function that updates parameters based on similarity calculations between context and negative sample words.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional word vector algorithms (e.g., Google's word2vec, Microsoft's deep neural network) are used, then the generation process is simple, but the accuracy and semantic representation quality of word vectors deteriorate
Solution Approach 1:
The patent segments words into n-ary character features (e.g., 2-ary, 3-ary, 4-ary character n-grams) to create detailed character-level representations. This segmentation allows the model to capture local character patterns and morphological information, significantly improving word vector accuracy while maintaining manageable computational complexity through systematic feature decomposition
Solution Approach 2:
The patent introduces a new dimension by incorporating character n-gram features alongside traditional word-level embeddings. This multi-dimensional approach combines character-level granularity with word-level semantics, creating a richer feature space that enhances representation quality without simply increasing the dimensionality of the final vector
2Loss of information
If n-ary character features are incorporated into word vector generation, then semantic information representation improves, but computational complexity increases
Solution Approach 1:
The patent performs preliminary extraction and encoding of character n-gram features before the main training process. By pre-processing and organizing character-level features into structured representations, the model reduces the computational burden during training while preserving rich semantic information, effectively preparing data in advance to minimize processing complexity
Solution Approach 2:
The patent merges character n-gram features with traditional word embeddings into a unified feature representation. This combination integrates local character patterns with global word semantics, ensuring comprehensive semantic information retention while leveraging the complementary strengths of both feature types to achieve robust representations
3Reliability
If convolutional neural networks are trained with extensive feature vectors and context words, then context understanding improves, but training time and computational resources increase
Solution Approach 1:
The patent applies partial action by selectively processing context words within a fixed window size around the target word, rather than processing the entire corpus or all possible context words. This approach captures sufficient contextual information for reliable representation while significantly reducing the computational scope and training time required
Solution Approach 2:
The patent optimizes training efficiency by adjusting key parameters such as learning rate, batch size, and context window size. These parameter changes enable the model to converge faster with high-quality context representations, balancing training time investment against the reliability of context understanding achieved
Data Source
AI summary
Implementations of the present specification disclose a method for generating word vectors, apparatus, and device. The method includes: obtaining words by segmenting a corpus; establishing a feature vector of each obtained word based on n-ary characters; training a convolutional neural network based on the feature vectors of the obtained words and the feature vectors of context words associated with each obtained word in the corpus; and generating a word vector for each obtained word based on the feature vector of the obtained word and the trained convolutional neural network.