Document Scoring Pipeline for Selective Indexing
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Solution Overview
Problem
Conventional indexing services waste resources and are time-consuming as they index all available documents, which is problematic in limited resource environments and inefficient.
Innovation Solution
A document scoring pipeline that selectively indexes high-quality documents by training a model to assign intrinsic relevance scores, using a feature combiner to extract page, host, and domain features, and a trainer to generate a document scoring model for efficient indexing and search result ranking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all available documents are indexed, then complete document coverage is achieved, but resource consumption and indexing time increase significantly
Solution Approach 1:
The system performs preliminary scoring of documents before indexing to identify high-quality documents that should be indexed. The document scoring model evaluates documents in advance, assigning scores based on quality metrics, so that only documents meeting a threshold are selected for indexing. This preliminary action resolves the contradiction by preparing the document set beforehand, enabling selective indexing that maintains coverage of important documents while reducing overall indexing volume and time.
2Reliability
If all available documents are indexed, then comprehensive search results are provided, but computational resources are wasted on low-quality documents
Solution Approach 1:
The system applies different quality standards to different documents by scoring each document individually. Instead of treating all documents uniformly, the document scoring model evaluates local quality characteristics of each document (such as content quality, source reliability, formatting) and assigns differential scores. This enables selective indexing where high-quality documents receive full indexing treatment while low-quality documents are excluded, thereby maintaining search result completeness for valuable content while eliminating computational waste on poor-quality documents.
3Productivity
If a document scoring model is trained and deployed, then indexing efficiency improves, but system complexity increases
Solution Approach 1:
The document scoring model serves as an intermediary component between the document input and the indexing process. Rather than directly modifying the core indexing service, the scoring model acts as a filtering layer that preprocesses documents and selects which ones should proceed to indexing. This intermediary approach improves indexing efficiency by reducing the volume of documents processed while adding minimal complexity, as the scoring model can be trained offline and deployed as a separate service that interfaces with the existing indexing infrastructure.
Data Source
AI summary
One or more techniques and/or systems are provided for implementing a pipeline used to generate, train, test, and implement a document scoring model for assigning document scores to documents. Features from various sources are combined to create a joined page level feature set, a joined domain level feature set, and a host level feature set. Numerical features and content features are extracted from ground truth documents and random documents. The numerical features are joined with the joined feature sets to create a set of joined features. The document scoring model is trained using the set of joined features and a training technique. A document is scored with a document score using the document scoring model based upon the content features and the set of joined features with document scores obtained during training.