Dependency Tree Pipeline for NLP Text Classification
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Solution Overview
Problem
Current Natural Language Processing (NLP) systems face scalability issues, memory limitations, and complexity in managing multiple NLP models for text classification, particularly when classifying large text blocks that exceed memory capacity and require chaining classifiers at different granularities, without a defined method for coordinating these classifiers.
Innovation Solution
A natural language processing system that includes a processor, data reader, dependency tree generator, and pipeline-based execution engine, which converts input data streams into logical blocks, generates dependency trees to define processor dependencies, and executes NLP processors in a pipeline order to classify content at various granularities in a single pass, allowing classifiers to depend on each other's outputs for accurate classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple NLP models are applied to the same text block for reprocessing, then classification accuracy is improved, but memory capacity utilization increases excessively
Solution Approach 1:
The patent segments the text block into smaller units (sentences, paragraphs, sections) and applies different NLP models at different granularities. This allows multiple models to process the same content without requiring all models to hold the entire text block in memory simultaneously, thus reducing memory capacity utilization while maintaining classification accuracy.
Solution Approach 2:
The patent performs preliminary processing at lower granularities (sentence-level, paragraph-level classification) before performing document-level classification. This preliminary action allows subsequent models to work with pre-processed data, reducing the memory requirements for handling large text blocks while improving overall classification accuracy through multi-stage processing.
2Adaptability or versatility
If multiple NLP models are chained together for multi-level classification, then classification comprehensiveness is improved, but system complexity increases
Solution Approach 1:
The patent segments the classification system into independent modules operating at different granularities (sentence-level, paragraph-level, document-level). Each module can be independently configured and executed, making the system more versatile for different classification tasks while reducing overall complexity through modular architecture.
Solution Approach 2:
The patent implements a dynamic execution engine that can adaptively select and execute different NLP models based on the specific classification task and data characteristics. This dynamic approach allows the system to maintain high comprehensiveness by switching between models as needed, while managing complexity through on-demand model selection rather than requiring all models to be active simultaneously.
3Productivity
If NLP processors execute in parallel, then processing speed is improved, but coordination complexity increases
Solution Approach 1:
The patent segments the processing tasks into independent units at different granularities that can be executed in parallel. The execution engine manages these segmented tasks through a standardized interface, allowing parallel processing to improve speed while minimizing coordination complexity through modular task management.
Solution Approach 2:
The patent introduces an execution engine as an intermediary layer between the NLP models and the data processing pipeline. This intermediary manages the coordination between parallel-executing models, handling data flow, dependency resolution, and result aggregation, thus improving processing speed through parallelism while containing coordination complexity within the execution engine.
4Reliability
If hard-coded pipelines are used for text processing, then processing reliability is improved, but adaptability to different tasks decreases
Solution Approach 1:
The patent transforms the static hard-coded pipelines into a dynamic execution engine that can adaptively configure processing steps based on task requirements. The engine maintains reliable execution through standardized interfaces and defined execution semantics, while improving adaptability by dynamically selecting and ordering NLP models according to the specific classification task and data characteristics.
Data Source
AI summary
Embodiments of a system and a method for classifying blocks of text at varying, possibly simultaneous, and possibly interacting levels of scope (e.g., sentence, paragraph, section, document) are disclosed. A system includes a processor coupled to: a data reader to receive an input comprising of a data stream, and convert the data stream into one or more logical data blocks of varying scopes. The system includes a dependency tree generator to create a dependency tree to define the scope and dependencies of each of a plurality of natural language processors (NL Processors) with respect to each other such that the dependency tree identifies when an input of a NL Processor (depender) depends on an output of another NL Processor (dependee). Each NL Processor is configured based on a type of at least one logical data block of the one or more logical blocks to which it applies. The system includes a pipeline generator, which automatically generates a pipeline of natural language processors from the dependency tree such that all dependee classifiers are executed and completed on all of their text blocks before all of their depender classifiers.