Contextual Document Embeddings for Accurate Retrieval

Solution Overview

Problem

Existing document representation methods based on word frequencies fail to capture contextual information, leading to inaccuracies in document retrieval and recommendation, as they are sensitive to document length and do not account for the context of neighboring words.

Innovation Solution

A document search system that divides documents into sentences, uses a sentence encoder to generate contextual sentence embeddings, and combines these embeddings to create a document embedding, which captures the semantic context and is robust to variations in document length, allowing for accurate comparison and retrieval of similar documents.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of manufacture

If word frequency-based document representation is used, then the method is simple and computationally efficient, but it fails to capture contextual information and leads to inaccurate document retrieval

Engineering Contradiction:

Improvesimplicity of document representation methodVSAvoidaccuracy of document retrieval

Core Design Contradiction:

Ease of manufactureVSMeasurement precision

Solution Approach 1:

The document is segmented into sentences, and each sentence is independently encoded to generate contextual sentence embeddings. This segmentation allows the system to capture contextual information at the sentence level while maintaining computational efficiency. The sentence encoder processes each sentence separately, producing meaningful representations that reflect the document's semantic content and structure.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system transitions from traditional word-frequency based representation to contextual sentence embeddings using a sentence encoder model. This dimensional transformation converts discrete word counts into continuous vector representations that capture semantic context, relationships between words, and document meaning, thereby significantly improving retrieval accuracy while remaining computationally feasible.

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

2Measurement precision

If contextual sentence embeddings are generated for each sentence, then contextual information is captured accurately, but the computational complexity and processing time increase

Engineering Contradiction:

Improveaccuracy of contextual representationVSAvoidcomplexity of embedding generation process

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The document is segmented into sentences, and each sentence is independently encoded to generate contextual sentence embeddings. This segmentation allows the system to capture contextual information at the sentence level while maintaining computational efficiency. The sentence encoder processes each sentence separately, producing meaningful representations that reflect the document's semantic content and structure.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

After generating contextual sentence embeddings for all sentences in the document, the system merges these embeddings by computing their mean to produce a single document embedding. This merging step consolidates the contextual information from individual sentences into a comprehensive document representation, balancing computational complexity with representation accuracy.

Inventive Principle:

Principle #5Merging (Combining)

3Productivity

If traditional word frequency methods are used, then the system is fast and simple, but it is sensitive to document length and cannot account for neighboring word context

Engineering Contradiction:

Improvespeed of document processingVSAvoidrobustness to document length variations

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The document is segmented into sentences, and each sentence is independently encoded to generate contextual sentence embeddings. This segmentation allows the system to capture contextual information at the sentence level while maintaining computational efficiency. The sentence encoder processes each sentence separately, producing meaningful representations that reflect the document's semantic content and structure.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system changes the fundamental parameters of document representation from word frequencies to contextual sentence embeddings. This parameter transformation enables the system to capture semantic meaning, context, and relationships between words, making the representation robust to document length variations and improving both accuracy and reliability across documents of different sizes.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20240403339A1Document recommendation using contextual embeddings

Publication Date: 2024.12.05 ADOBE INC

AI summary

Systems and methods for generating contextual document embeddings and recommending similar articles based on the document embeddings are described. Embodiments are configured to receive a document query and encode a plurality of candidate sentences from a candidate document to obtain a plurality of contextual sentence embeddings. The contextual sentence embeddings each represent a semantic context of a corresponding sentence from the plurality of candidate sentences. Embodiments then generate a candidate document embedding by combining the plurality of contextual sentence embeddings and provide the candidate document in response to the document query based on the candidate document embedding.