Chinese Intent Classification via Integrated Word-Character Embeddings
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Solution Overview
Problem
Chinese intent classification is challenging due to the logographic nature of the Chinese language, where words are composed of characters that have independent meanings, and pre-trained word embeddings struggle to represent low-frequency domain-specific words and fail to adapt to contextual information.
Innovation Solution
A method that dynamically integrates character features into word embeddings using ensemble techniques, incorporating character or character n-gram vectors to enhance representation, and employs a contextual information extraction module to leverage sentence context for improved intent classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If pre-trained word embeddings are used for Chinese intent classification, then the classification process is simple, but the representation accuracy of low-frequency domain-specific words is poor
Solution Approach 1:
The patent segments Chinese words into their constituent characters and extracts character-level features using CNN. This segmentation allows the model to capture morphological information from characters, which is particularly effective for low-frequency words where character-level patterns can provide meaningful representations even when the entire word is rare.
Solution Approach 2:
The patent creates a composite representation by combining word-level embeddings with character-level embeddings. This composite approach integrates both the semantic information from pre-trained word embeddings and the morphological information from character features, resulting in a more robust representation that works well for both frequent and low-frequency words.
2Productivity
If static pre-trained word embeddings are used, then the model training is fast, but the adaptation to contextual information is poor
Solution Approach 1:
The patent introduces dynamic elements by adding character-level feature extraction that adapts to each specific input sentence. The CNN-based character feature extractor processes the actual characters in each sentence, allowing the model to dynamically adapt to contextual information rather than relying solely on static pre-trained embeddings.
Solution Approach 2:
The patent adds a new dimension to the representation by incorporating character-level features alongside word-level embeddings. This additional dimensional information provides contextual adaptability without significantly increasing training complexity, as the character features are extracted through efficient CNN operations.
3Measurement precision
If character-level features are extracted and integrated with word embeddings, then the representation accuracy improves, but the computational complexity increases
Solution Approach 1:
The patent replaces complex manual feature engineering with automated CNN-based character feature extraction. The CNN automatically learns relevant character-level patterns through convolution operations, eliminating the need for hand-crafted features while maintaining computational efficiency through optimized neural network operations.
Solution Approach 2:
The patent changes the parameters of the embedding representation by combining word embeddings with character embeddings. This parameter enhancement enriches the feature space without proportionally increasing computational complexity, as the character embeddings are generated through efficient local convolution operations rather than full-sentence processing.
Data Source
AI summary
An intent classification method and a system for realizing the intent classification method are provided. The intent classification method can be used for determining an intent class of a sentence input in a language containing information-bearing characters, such as Chinese. The method specifically comprises a step of obtaining at least one set of word embeddings and character embeddings from the sentence input to thereby generate at least one set of integrated word-character embeddings, and a step of determining an intent class corresponding to the sentence input based on the at least one set of integrated word-character embeddings. Herein, each of the at least one set of integrated word-character embeddings corresponds to one of the at least one set of word embeddings and character embeddings.