Conditional Random Field Query Parser for Unstructured Geographic Search
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Solution Overview
Problem
Existing methods for parsing unstructured local web queries, particularly geographic queries, are limited by the lack of query level grammars and labeled data, leading to brittle and inefficient parsing solutions that fail to effectively extract relevant information for general and local web searches.
Innovation Solution
A data-driven approach using conditional random field (CRF) models is employed to learn query parsers from large-scale class-based logs, enabling the derivation of robust and flexible parsers that can handle unstructured geographic queries by leveraging high-level language models and class transition features, thereby overcoming the limitations of traditional syntactic rules and labeled data requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If simple syntactic rules are used for parsing geographic queries, then the parsing process is simple and fast, but the parsing accuracy and robustness deteriorate
Solution Approach 1:
The patent replaces traditional mechanical syntactic rule-based parsing with a machine learning model (conditional random field) that automatically learns parsing patterns from data. This substitution enables the system to achieve high parsing accuracy without relying on hand-crafted syntactic rules, thereby resolving the contradiction between simple parsing mechanics and accurate parsing results.
Solution Approach 2:
The patent changes the fundamental parameter of parsing from rule-based deterministic processing to statistical probability-based processing using conditional random fields. By transitioning from fixed syntactic rules to learned probabilistic models, the system achieves both robustness and accuracy while maintaining reasonable processing speed through efficient feature engineering and model optimization.
2Measurement precision
If query level grammars and labeled data are used to train semantic parsers, then parsing accuracy improves, but data preparation complexity and cost increase
Solution Approach 1:
The patent performs preliminary action by pre-processing query logs to extract relevant features and patterns before training the conditional random field model. This includes creating feature representations from raw query data, which simplifies the subsequent training process and reduces the need for extensive manual labeling, thereby lowering data preparation complexity while maintaining parsing accuracy.
Solution Approach 2:
The system implements self-service by automatically learning parsing patterns directly from unstructured query logs without requiring manual annotation of training data. The conditional random field model extracts features and learns semantic patterns autonomously from the available log data, eliminating the need for expensive human labeling efforts while achieving high parsing accuracy.
3Ease of manufacture
If traditional syntactic rule-based parsing is used, then labeled data requirements are reduced, but adaptability to new semantic classes deteriorates
Solution Approach 1:
The patent introduces dynamics by replacing static syntactic rules with a dynamic machine learning model that can adapt to new semantic classes. The conditional random field model learns from data and can be retrained or fine-tuned to accommodate new query types and semantic categories, providing both ease of implementation through automated learning and high adaptability to emerging semantic classes.
Solution Approach 2:
The system achieves universality by designing a conditional random field framework that can handle multiple semantic classes and query types through a unified model architecture. The same model structure processes diverse geographic queries, local search queries, and general web queries, demonstrating multi-functionality and high adaptability without requiring separate parsing systems for different query categories.
4Adaptability or versatility
If more semantic labels and query types are supported, then parser versatility improves, but training data requirements and processing complexity increase
Solution Approach 1:
The patent applies segmentation by breaking down complex query parsing into distinct feature components and semantic categories. The conditional random field model processes different aspects of queries (geographic entities, search terms, query structure) as separate features that are then integrated to produce comprehensive parsing results. This segmentation approach enables support for multiple semantic labels while managing model complexity through modular feature engineering.
Data Source
AI summary
A system and method is provided which may comprise parsing an unstructured geographic web-search query into a field-based format, by utilizing conditional random fields, learned by semi-supervised automated learning, to parse structured information from the unstructured geographic web-search query. The system and method may also comprise establishing semi-supervised conditional random fields utilizing one of a rule-based finite state machine model and a statistics-based conditional random field model. Systematic geographic parsing may be used with the one of the rule-based finite state machine model and the statistics-based conditional random field model. Parsing an unstructured local geographical web-based query in local domain may be done by applying a learned model parser to the query, using at least one class-based query log from a form-based query system. The learned model parser may comprise at least one class-level n-gram language model-based feature harvested from a structured query log.