Hierarchical Language Prior Model for NLP Adaptability
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Solution Overview
Problem
Current natural language processing technologies face limitations in achieving broad applicability and require intensive user supervision, with existing machine learning techniques being constrained to specific contexts and lacking in generating meaningful discussions with humans.
Innovation Solution
The development of a language prior model that uses parameterized representations of sentences and words, trained using unlabeled data, which assigns lexical structure weights and unit weights to facilitate operations and generate sentences, allowing for the creation of submodels that reflect word relationships and order, enabling applications beyond fact-gathering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing machine learning techniques are used to compile lists of known facts, then fact-gathering is successful, but applicability is constrained to specific contexts
Solution Approach 1:
The patent segments the language modeling task into hierarchical levels: lexical units (words), phrases, sentences, and paragraphs. Each level has its own submodel that processes information at that granularity. This segmentation allows the system to handle diverse NLP tasks by combining results from different hierarchical levels, thereby improving applicability while maintaining reliability through specialized processing at each level.
Solution Approach 2:
The hierarchical language model is designed to perform multiple NLP functions including machine translation, text summarization, question answering, and sentiment analysis within a single unified framework. By creating a universal model that can adapt to different tasks through its hierarchical structure and lexical structure weights, the system achieves broad applicability while maintaining task-specific reliability.
2Measurement precision
If voice recognition software is used for natural language processing, then recognition accuracy is improved, but user supervision time increases
Solution Approach 1:
The hierarchical language model is trained on large corpora of unlabeled text data, enabling it to learn language patterns, grammar, and semantics automatically without requiring user supervision or manual labeling. The model performs self-training by processing hierarchical structures from lexical units to paragraphs, achieving high recognition accuracy while eliminating the need for users to invest time in training or correcting the system.
3Adaptability or versatility
If hierarchical models with lexical structure weights are used, then language modeling capability is improved, but computational complexity increases
Solution Approach 1:
The hierarchical model divides computational processing into manageable segments at different levels: lexical unit processing, phrase processing, sentence processing, and paragraph processing. Each level handles a specific aspect of language understanding, which reduces the computational burden at any single level while maintaining overall modeling capability. The segmentation allows parallel processing and optimized resource allocation.
Solution Approach 2:
The patent introduces a hierarchical dimension to language modeling, organizing processing from fine-grained lexical units to coarse-grained paragraphs. This dimensional organization transforms the computational problem from a flat, monolithic structure into a multi-layered hierarchy, enabling more efficient computation by processing information at appropriate granularities and reducing redundant calculations.
Data Source
AI summary
The described implementations relate to natural language processing, and more particularly to training a language prior model using a model structure. The language prior model can be trained using parameterized representations of lexical structures such as training sentences, as well as parameterized representations of lexical units such as words or n-grams. During training, the parameterized representations of the lexical structures and the lexical units can be adjusted using the model structure. When the language prior model is trained, the parameterized representations of the lexical structures can reflect how the lexical units were used in the lexical structures.