Server Entity Classification Using Conditional Random Fields and Support Vector Machines
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Solution Overview
Problem
Existing methods, such as conditional random field and support vector machine models, face difficulties in distinguishing and classifying entities of the same type in natural language queries due to inherent ambiguities and inefficiencies in processing context, leading to challenges in identifying and extracting specific entities.
Innovation Solution
A server system that employs a conditional random field model to identify entities in an input query and generates a normalized query by replacing entities with generic identifiers, followed by applying a support vector machine model to determine sub-types, allowing for efficient classification of entities by extracting features within predetermined window sizes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conditional random field model is used to identify entities, then entity identification is achieved, but difficulty in distinguishing entities of the same type occurs
Solution Approach 1:
The patent divides the classification process into two distinct stages: first using conditional random field to identify entities at a general level, then using support vector machine to distinguish between different types of the same entity. This segmentation allows each model to specialize in its strength while collectively solving the overall classification problem.
Solution Approach 2:
The patent introduces an intermediary normalization step that transforms the original query into a normalized query with generic identifiers, which then serves as input for the support vector machine. This intermediary representation enables the second model to focus specifically on distinguishing entity types without the complexity of initial entity identification.
2Measurement precision
If support vector machine analyzes entire input query, then classification into categories is achieved, but efficiency in identifying and extracting entities decreases
Solution Approach 1:
The patent segments the processing workflow so that entity identification and entity type classification are performed in separate stages by different models. The conditional random field handles entity identification first, then the support vector machine handles type classification on normalized input, improving overall efficiency compared to analyzing the entire query at once.
Solution Approach 2:
The patent performs preliminary entity identification using conditional random field before applying the support vector machine for classification. This preliminary action prepares the data in advance, allowing the support vector machine to focus specifically on type distinction rather than dealing with the full complexity of the original query.
3Adaptability or versatility
If natural language query is processed, then query understanding is attempted, but ambiguities requiring clarification increase processing difficulty
Solution Approach 1:
The patent introduces a normalized query as an intermediary representation that bridges the gap between ambiguous natural language and structured classification. By transforming the original query into a normalized form with generic identifiers, the system reduces ambiguity while maintaining the ability to handle diverse natural language inputs.
Solution Approach 2:
The patent changes the parameter representation of entities from their original natural language forms to normalized generic identifiers. This parameter transformation simplifies the classification task by standardizing entity representations while preserving the essential information needed for distinction.
Data Source
AI summary
A server, method, and non-transitory computer readable medium for ranking a plurality of data sources are provided. The server includes a network interface, a memory storage unit and a processor. The method involves receiving an input query, identifying entities of the input query using conditional random fields, generating a normalized query and applying a support vector machine to the normalized query. The non-transitory computer readable medium is encoded with programming instructions to direct a processor to carry out the method.