Automated Token Annotation for NLP Training Data
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Solution Overview
Problem
Conventional methods for annotating tokens in natural language processing (NLP) are time-intensive and require human intervention, limiting their scope and utility, as they rely on pre-defined tokens and lack inbuilt intelligence for automated annotation of training datasets for deep learning models.
Innovation Solution
A method and system that utilize machine learning or deep learning techniques to segment corpus instances, derive entities, determine word vectors, and label tokens based on frequency, enabling automatic annotation without human involvement, using a system with processors and memory to perform these tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods are used for annotating tokens, then human intervention ensures accuracy, but the process becomes time-intensive and effort-intensive
Solution Approach 1:
The system performs automatic token annotation using NLP engines and machine learning models, eliminating the need for manual human annotation. The patent describes an automated process where the computer system segments corpus, identifies entities, determines word vectors, and labels tokens without human intervention, thereby reducing annotation time while maintaining accuracy through intelligent algorithms
Solution Approach 2:
The patent replaces the mechanical human annotation process with an automated computational system. The manual labeling task is substituted with an NLP engine that uses machine learning models (RNN, LSTM, CNN) to automatically segment text, identify entities, and annotate tokens, transforming a labor-intensive manual process into an efficient automated system
2Manufacturing precision
If human intervention is used for designing entities and values, then annotation quality is maintained, but the process requires significant effort and time
Solution Approach 1:
The system automatically performs entity design and value assignment through its NLP engine and machine learning models. The patent describes how the computer system autonomously segments corpus into instances, identifies entities within those instances, determines word vectors, and assigns labels to tokens without requiring human designers to manually create entities and values
Solution Approach 2:
The patent replaces the manual process of designing entities and values with an automated computational approach. The system uses trained machine learning models to automatically extract entities from text and determine their values, substituting human cognitive effort with algorithmic processing while maintaining annotation quality
3Adaptability or versatility
If conventional annotation methods are used, then pre-defined tokens can be labeled, but the scope is limited and requires subsequent manual labeling
Solution Approach 1:
The patent implements a universal annotation system that can handle multiple annotation tasks through a single integrated NLP engine. The system performs corpus segmentation, entity identification, word vector determination, and token labeling in one automated process, making it adaptable to various annotation needs without requiring separate manual processes for different annotation types
Solution Approach 2:
The patent replaces the limited conventional annotation approach with a comprehensive automated system. Instead of relying on pre-defined tokens that require subsequent manual labeling, the system uses machine learning models to automatically identify and annotate various token types (including named entities, common nouns, verbs, adjectives) throughout the entire corpus in one pass
Data Source
AI summary
This disclosure relates to method and system for annotating tokens for natural language processing (NLP). In one embodiment, the method may include segmenting a plurality of corpus based on each of a plurality of instances, deriving a plurality of entities for each of the plurality of instances based on at least one of a machine learning technique or a deep learning technique, determining a word vector for each of the plurality of entities associated with each of the plurality of instances, and labelling a plurality of tokens for each of the plurality of instances. It should be noted that the plurality of tokens associated with the plurality of entities may be identified based on a frequency of each of the plurality of entities.