Entity Recognition via String Distance Metrics and Lexical Processing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current named-entity recognition techniques, such as rule-based and machine learning methods, face challenges in efficiently identifying and extracting terms from technical documents due to the need for costly linguistic rules, extensive training data, and the inability to recognize terms with variations, leading to the 'annotation bottleneck' and inefficiencies in handling semantic similarities and differences.
Innovation Solution
A method and system for Terminology Recognition that analyzes strings of characters by recognizing constituent concepts using string-distance metrics and lexical processing, allowing for the identification of semantically similar terms regardless of character variations, and representing terms as graphs to compute conceptual similarity, thereby reducing the need for pre-identified terms and costly gazetteers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If rule-based methods with linguistic rules are used for named-entity recognition, then precision in entity extraction is improved, but the cost and complexity of producing and maintaining the rules increase significantly
Solution Approach 1:
The patent replaces manual linguistic rule construction with an automated machine learning system that learns entity patterns from training data. The system uses feature extraction and classification algorithms to automatically identify entities without requiring explicit linguistic rules, thereby reducing the complexity and maintenance burden of rule-based systems while maintaining extraction precision.
Solution Approach 2:
The patent transforms the approach from fixed linguistic rules to dynamic machine learning models that can adapt to different domains and entity types. By changing the parameters from static rule sets to trainable model parameters, the system achieves precision across diverse contexts without manually crafting rules for each scenario.
2Adaptability or versatility
If machine learning methods with training data are used for named-entity recognition, then adaptability to different entity types is improved, but the time and cost for annotating training data increase significantly
Solution Approach 1:
The patent performs preliminary action by pre-processing text documents to identify potential entity candidates and their contexts before the main classification step. This preliminary structuring of data reduces the annotation burden by focusing expert attention only on ambiguous cases rather than requiring manual annotation of all training examples.
Solution Approach 2:
The patent introduces an intermediary layer of feature extraction that transforms raw text into structured representations before classification. This intermediary representation captures essential entity characteristics automatically, reducing the need for extensively annotated training data while maintaining adaptability to different entity types.
3Speed
If gazetteer lists with precompiled terms are used for terminology recognition, then recognition speed for known terms is improved, but the ability to recognize term variations and synonyms deteriorates
Solution Approach 1:
The patent replaces static gazetteer lists with dynamic machine learning models that can adapt to term variations. The system learns from training data to recognize entities even when they appear in varied forms, maintaining fast recognition speeds through efficient feature-based classification while gaining versatility to handle synonyms and inflections that fixed gazetteers cannot capture.
4Reliability
If extensive gazetteer lists covering all term variations are created, then comprehensive term recognition is improved, but the cost and effort to produce and maintain the lists increase significantly
Solution Approach 1:
The patent replaces the manual process of creating and maintaining extensive gazetteer lists with an automated machine learning system. The system learns term variations and synonyms from annotated training data, achieving comprehensive term recognition without requiring manual compilation of exhaustive term lists, thereby reducing the complexity and ongoing maintenance burden.
Data Source
AI summary
The invention relates to a method of querying technical domains that recognizes the concepts represented by strings of characters, rather than merely comparing strings. It can be used to compute conceptual similarity between terms, The method employs string distance metrics and a cyclic progression of lexical processing to recognize constituent term concepts that are then combined to form full-term concepts by means of a grammar. Terms can be extracted and identified as being conceptually similar (or dissimilar) to other terms even if they have never previously been encountered. A key advantage is the ability to extract terms from documents based on the combination of a limited number of sub-concepts, This avoids the need for the prior identification of all possible terms that current methods require. A second key advantage is the ability to introduce or remove concepts and synonyms individually without the need to alter terms which the concept or synonym constitutes.