Hierarchical Annotated Data Model for Sequential Classification
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Solution Overview
Problem
Traditional data training and classification methods fail to effectively model large-scale structure in sequential data, leading to ambiguities and mis-classification, especially when dealing with shorter pieces of text or sequences where exact phrasing and transition words are crucial.
Innovation Solution
A system and method for training and classifying data using annotated data models, where annotations represent critical characteristics, identifiers are generated from these annotations, and feature weights and transition weights are computed to build a probabilistic model for accurate classification of un-annotated data sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional n-gram features are used to represent data, then classification works well when a lot of data is available, but sequential information and large-scale structure are discarded leading to ambiguities and mis-classification
Solution Approach 1:
The patent segments the sequence data into hierarchical levels (e.g., words, phrases, sentences, paragraphs) and processes each level separately while preserving the hierarchical structure. This allows the system to maintain sequential information at multiple granularities rather than discarding it through flat n-gram representation.
Solution Approach 2:
The patent introduces a hierarchical dimension to the data representation, transforming flat sequential data into a multi-level hierarchical structure. This dimensional transformation allows preservation of both local (n-gram) and global (large-scale structure) sequential information simultaneously.
2Adaptability or versatility
If rule-based systems are used with frequent itemset algorithms, then rules can be learned from data, but the systems still fail to model large-scale sequence structure effectively
Solution Approach 1:
The patent implements a nested hierarchical structure where smaller units (words) are contained within larger units (phrases), which are contained within even larger units (sentences, paragraphs). This nesting preserves the hierarchical relationships and large-scale structure that flat rule-based systems cannot capture.
Solution Approach 2:
The patent uses dynamic programming algorithms that efficiently compute hierarchical relationships by breaking down the complex sequence modeling into manageable sub-problems. This dynamic approach allows the system to adapt to varying sequence lengths and structures while maintaining computational feasibility.
3Loss of information
If hidden Markov models or conditional random fields are used, then sequential information can be extracted, but these methods focus on extracting information rather than classifying the sequence as a whole
Solution Approach 1:
The patent merges the strengths of sequential information extraction methods (HMM, CRF) with sequence-level classification capabilities. By combining hierarchical feature extraction with holistic sequence classification, the system achieves both detailed sequential understanding and accurate overall classification.
Solution Approach 2:
The patent introduces hierarchical features as an intermediary between raw sequential data and final classification. These hierarchical features serve as a bridge that preserves sequential information while enabling effective sequence-level classification, mediating between extraction and classification objectives.
4Reliability
If existing sequential classification methods are used, then some sequence structure can be modeled, but it requires a rough model to be supplied or learned and it is difficult to learn good models automatically
Solution Approach 1:
The patent performs preliminary hierarchical segmentation and feature extraction automatically from the raw data before classification. This preliminary action prepares the data in a structured format that enables effective classification without requiring complex manual model specification, automating the model construction process.
Solution Approach 2:
The system automatically learns hierarchical structures and relationships from the data itself without requiring external expertise or manual model design. The algorithms self-organize the sequential data into hierarchical patterns, making the system self-sufficient in model construction.
Data Source
AI summary
A mechanism for training and classifying data is disclosed. The method includes receiving a data set having at least a first annotation and at least a second annotation. The first annotation and the second annotation represent characteristics within the data set. The method also includes determining a first identifier from the first annotation and a second identifier from the second annotation and associating the first identifier to the second identifier to generate a joined identifier. The method also includes computing feature weights and transition weights for the annotated data set based on the at least a first identifier, at least a second identifier, and at least a joined identifier and transitions between each of the first, the second and the joined identifiers. The method further includes receiving a second un-annotated data set and classifying the second data set based on the computed feature weights and the transition weights.