Organizational Element Identification in Argumentative Discourse
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Solution Overview
Problem
Existing technologies fail to effectively identify and distinguish organizational elements in argumentative or persuasive discourse, which are crucial for understanding the structure and content of arguments, particularly in automated essay scoring and political science analysis.
Innovation Solution
A rule-based or probabilistic sequence model is developed to identify organizational elements in annotated text, using textual patterns and word frequency features to differentiate between claims, evidence, and organizational elements, and apply this model to new, unlabeled text to recognize organizational structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a rule-based or probabilistic sequence model is developed to identify organizational elements, then the accuracy of identifying organizational elements is improved, but the device complexity increases
Solution Approach 1:
The system segments the complex task of identifying organizational elements into distinct processing stages: text preprocessing, feature extraction (textual patterns and word frequency), rule-based filtering, and probabilistic sequence modeling. This segmentation allows each component to specialize in a specific aspect, improving overall accuracy while managing complexity through modular design.
Solution Approach 2:
The patent introduces an intermediary layer of textual patterns and word frequency features that mediate between the raw text input and the final organizational element identification. This intermediary feature set acts as a bridge, transforming unstructured text into structured representations that the model can process effectively, thereby improving measurement precision without directly increasing the core model's complexity.
2Reliability
If annotations are used to distinguish organizational elements from claims and evidence, then the reliability of discourse analysis is improved, but the loss of time in text processing increases
Solution Approach 1:
The system performs preliminary actions by pre-processing the text to extract textual patterns and word frequency features before the main identification process. This preliminary feature extraction prepares the data in advance, making the subsequent organizational element identification more efficient and reliable while reducing the time penalty associated with detailed annotation during the main processing phase.
Solution Approach 2:
The model is designed to be self-sufficient in identifying organizational elements without requiring extensive manual annotation of every text instance. Once trained on annotated data, the system autonomously applies the learned patterns and probabilistic sequences to new texts, maintaining high reliability while minimizing the time investment required for processing new discourse.
Data Source
AI summary
In accordance with the teachings described herein, systems and methods are provided for identifying organizational elements in argumentative or persuasive discourse. A text that has been annotated is received. The annotated text includes argumentative or persuasive discourse that includes claims and evidence and organizational elements configured to organize the claims and evidence. Annotations of the annotated text distinguish the organizational elements from the claims and evidence. A rule set or a feature set is identified from the annotated text, where the rule set or the feature set includes textual patterns or word frequency features related to the organizational elements of the annotated text. A model is built based on the annotations and on the rule set or the feature set. The model is configured to identify organizational elements in a new text. The model is applied to the new text.