Language Model Adaptation via Domain-Specific Text Filtering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing speech recognition systems face challenges in expanding and refining the recognition scope of basic language models to enhance prediction and recognition fidelity for specific subject matters, often being hindered by irrelevant phrases that decrease performance.
Innovation Solution
A method is introduced where a language model is adapted by acquiring relevant textual contents from a large source, discarding irrelevant data, and incorporating relevant terms into the model, using a computerized apparatus to query search engines and filter out non-relevant information based on semantic similarity, thereby enhancing the model's context-specific performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a language model is expanded with general textual contents from large sources, then the model's vocabulary and coverage are increased, but recognition accuracy for specific domains decreases due to irrelevant terms
Solution Approach 1:
The patent segments the language model into domain-specific components by filtering textual contents based on relevance to specific domains. The system divides general textual data into relevant and irrelevant portions, incorporating only domain-relevant terms while excluding unrelated content, thus maintaining model coverage while improving recognition accuracy for specific domains.
Solution Approach 2:
The patent applies local quality by tailoring the language model to specific domain contexts. Different domains receive customized filtering criteria and relevance assessments, ensuring that each domain-specific application receives text contents with appropriate quality and relevance characteristics for its particular context rather than a uniform approach.
2Quantity of substance
If the language model incorporates more textual terms from diverse sources, then the model's general knowledge increases, but performance on specific subject matters deteriorates due to noise from irrelevant phrases
Solution Approach 1:
The patent extracts only the relevant textual terms from diverse sources by implementing filtering mechanisms that identify and remove irrelevant phrases. The system takes out domain-specific terms from general textual corpora while discarding noise, thereby maintaining a manageable quantity of high-quality terms that improve recognition fidelity for specific subject matters.
Solution Approach 2:
The patent changes the parameter of term relevance by dynamically adjusting filtering criteria based on domain characteristics. The system modifies which textual terms are incorporated by changing relevance thresholds and domain-matching parameters, ensuring that the quantity of incorporated terms reflects their quality and relevance rather than simply maximizing term count.
3Quantity of substance
If the language model is adapted with unfiltered data from large sources, then data availability increases, but recognition reliability decreases due to inclusion of non-relevant information
Solution Approach 1:
The patent applies preliminary action by performing filtering and relevance assessment before incorporating textual contents into the language model. The system pre-processes data from large sources by removing irrelevant information in advance, ensuring that only reliable, domain-relevant data is made available for model adaptation, thus maintaining both data availability and recognition reliability.
Solution Approach 2:
The patent implements feedback mechanisms that evaluate the impact of incorporated textual terms on recognition performance. The system monitors recognition reliability and adjusts filtering criteria accordingly, using performance feedback to refine which data from large sources is incorporated, thereby maintaining reliability while preserving data availability.
Data Source
AI summary
A method for adapting a language model for a context of a domain, comprising obtaining textual contents from a large source by a request directed to the context of the domain, discarding at least a part of the textual contents that contain textual terms determined as irrelevant to the context of the domain, thereby retaining, as retained data, at least a part of the textual contents that contain textual terms determined as relevant to the context of the domain, and adapting the language model by incorporating therein at least a part of the textual terms of the retained data, wherein the method is performed on an at least one computerized apparatus configured to perform the method and equipped for communication with the large source, and an apparatus for performing the same.