Semantic Hashtag Extraction via Intent Classification
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Solution Overview
Problem
Current natural language processing (NLP) systems fail to consistently and accurately analyze large collections of structured and unstructured text documents to understand the full meaning, including intent and sentiment, due to the complexity of human language and inherent deficiencies in NLP systems, leading to inaccurate topic extraction and increased reliance on manual monitoring of vast data volumes.
Innovation Solution
A computerized method and system that breaks down large text chunks into smaller sentences, identifies and removes domain noise, tags sentences with domain-specific attributes, defines a semantic ontology using a graph database, and applies machine learning techniques to extract insights by classifying data objects as noisy or non-noisy and identifying intents to generate recommended actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If NLP systems analyze large collections of text documents to understand full meaning including intent and sentiment, then the accuracy and reliability of topic extraction improves, but the complexity of processing and false positives increase due to the complexity of human language
Solution Approach 1:
The patent segments text documents into smaller units (sentences, phrases, tokens) and processes them through multiple specialized stages including syntax parsing, semantic analysis, and intent classification. This segmentation allows the system to handle complex language understanding by breaking it down into manageable components rather than attempting to analyze entire documents at once.
Solution Approach 2:
The patent introduces intermediary processing layers between raw text input and final topic extraction output. These intermediaries include syntax parsers that structure text, semantic analyzers that extract meaning, and intent classifiers that identify speaker goals. Each intermediary layer refines the data progressively, improving accuracy while managing complexity through specialized sub-systems.
2Reliability
If NLP systems attempt to understand full meaning including intent and sentiment, then the quality of insights extracted improves, but the processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing text documents through syntax parsing, tokenization, and basic semantic analysis before the main topic extraction process. This preliminary structuring of data creates a foundation that speeds up subsequent analysis by having key information already organized and identified, reducing the time needed for comprehensive understanding.
Solution Approach 2:
The patent implements continuous processing pipelines where text documents flow through multiple analysis stages simultaneously rather than completing one stage before starting the next. Multiple processors can work in parallel on different aspects of the same document (syntax, semantics, sentiment), maintaining continuous useful action that improves throughput while preserving comprehensive analysis quality.
3Measurement precision
If manual monitoring is used to compensate for NLP system deficiencies, then the accuracy of topic extraction improves, but the productivity and efficiency decrease due to increased manual effort
Solution Approach 1:
The patent implements self-service mechanisms where the NLP system automatically identifies and corrects its own errors through feedback loops. The system includes validation stages that check extracted topics against the original text, intent verification processes that confirm speaker goals are correctly identified, and automatic refinement capabilities that learn from mistakes without requiring manual intervention for every correction.
Solution Approach 2:
The patent incorporates feedback mechanisms where the output of each processing stage is evaluated and fed back into the system for refinement. Error detection algorithms identify incorrect topic extractions or misinterpreted intents, and the system automatically adjusts its analysis parameters or re-processes problematic sections. This closed-loop feedback maintains high accuracy while minimizing the need for manual monitoring.
Data Source
AI summary
A method for extracting semantic hashtags representing topics in one or more domain-specific documents, each topic relevant to achieving a goal of a domain-specific entity includes a processor executing a routine to split a domain-specific document into data objects, the data objects comprising sentences and paragraphs, using grammar rules specific to the domain-specific entity; applying an unsupervised learning model to classify the data objects as noisy and non-noisy for the domain-specific entity; discarding the noisy data objects; applying a supervised learning model to identify, based on a pre-defined set of intents, an intent of each non-noisy data object; tagging each non-noisy data object with its intent; applying the intent to an ontology graph base to identify a corresponding semantic hashtag; annotating each non-noisy data object with its semantic hashtag; and using one or more annotated non-noisy data objects, generating, for the domain-specific entity, a recommended action for achieving the goal.