Channel-Specific Error Adapters for Noisy Text Normalization
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Solution Overview
Problem
Current text normalization methods fail to effectively address multi-channel communication scenarios, particularly in contact centers, where varying degrees of creativity and domain-specific terms lead to challenges in data collection and identification of out-of-vocabulary words and erroneous words across different communication channels.
Innovation Solution
A system and method for text normalization in multiple noisy channels, utilizing a channel-specific error-type adapter framework that includes error-type adapters for spelling, abbreviation, and other error types, optimized for specific channels, and incorporating an interpolated language model and static lexicons to correct out-of-vocabulary entries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single general text normalization method is used across all channels, then the system complexity is low, but the normalization accuracy deteriorates due to channel-specific noise and error patterns
Solution Approach 1:
The patent segments the text normalization system into channel-specific adapters, each tailored to handle noise and errors characteristic of specific communication channels (e.g., SMS, email, chat). This segmentation allows each adapter to specialize in channel-specific patterns while maintaining overall system modularity, thereby improving normalization accuracy without excessive complexity increase.
Solution Approach 2:
The patent implements local quality by creating customized error models and lexicons for different communication channels. Each channel receives localized processing with channel-specific parameters and error patterns, allowing the system to adapt to local characteristics of each channel while maintaining a unified overall framework.
2Measurement precision
If channel-specific error models are implemented for each communication channel, then the normalization accuracy improves, but the device complexity increases due to multiple adapters and models
Solution Approach 1:
The patent implements universality by designing a modular adapter framework where each channel-specific adapter follows a common interface and structure. This allows the system to handle multiple channels with specialized models while maintaining a unified processing architecture, reducing the practical complexity despite having multiple adapters.
Solution Approach 2:
The patent applies nesting by organizing channel-specific adapters within a hierarchical framework where general normalization components are nested within specialized channel adapters. This nested structure allows reusable core functionality to be shared across channels while maintaining channel-specific customization, thereby managing complexity through systematic organization.
3Adaptability or versatility
If domain-specific terms and creative language are allowed in text entries, then the vocabulary coverage improves, but the difficulty of detecting and measuring erroneous words increases
Solution Approach 1:
The patent applies preliminary action by building channel-specific lexicons and error models before processing actual text entries. These pre-built resources contain domain-specific terms and channel-specific error patterns, allowing the system to quickly identify and correct errors in specialized vocabulary without increasing detection difficulty during actual processing.
Solution Approach 2:
The patent introduces channel-specific lexicons and error models as intermediary components between the raw text input and the normalization process. These intermediaries bridge the gap between domain-specific vocabulary and general error detection algorithms, enabling accurate error identification in specialized language contexts.
Data Source
AI summary
Systems and methods for text normalization in a plurality of noisy channels receive a text entry and channel origin data of the text entry; determine whether the text entry matches an in-vocabulary (IV) entry or whether the text entry is an out-of-vocabulary (OOV) entry; if the text entry is determined to have a matching IV entry, output the matching IV entry, and if the text entry is determined to be an OOV entry, implement a channel-specific error-type adapter framework based on the channel origin data, wherein the channel-specific error-type adapter framework is optimized for a specific channel from which the text entry originated; normalize the text entry using the channel-specific error-type adapter framework; and output one or more candidate normalized forms of the text entry.