Sequence Labeling for Product Attribute Extraction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
E-commerce providers face challenges in automatically extracting and standardizing attributes from product titles for filtering and ad campaigns, due to varying terminologies, database layouts, and missing attribute entries, leading to missed sales and customer dissatisfaction.
Innovation Solution
A system and method that involves tokenizing product titles, encoding tokens with sequence labeling algorithms, determining attribute types, normalizing attributes, and writing them to databases, enabling automated extraction and standardization of attributes such as brand, RAM, and hard drive space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated attribute extraction is implemented using traditional methods, then productivity increases, but measurement precision deteriorates due to varying terminologies and database layouts
Solution Approach 1:
The patent transforms the attribute extraction problem from a traditional text processing approach to a sequence labeling task by changing the parameters of the problem representation. Product titles are tokenized and each token is assigned a label indicating its attribute type (brand, RAM, hard drive space, etc.). This parameter transformation enables the use of trained machine learning models that can accurately extract attributes despite varying terminologies and database layouts, thereby maintaining both high productivity and high measurement precision.
2Measurement precision
If manual attribute extraction is used, then measurement precision improves, but productivity deteriorates due to time-consuming processes
Solution Approach 1:
The patent replaces the mechanical manual process of attribute extraction with an automated machine learning system. The sequence labeling algorithm, trained on labeled data, automatically processes product titles and extracts attributes with high accuracy. This substitution eliminates the need for manual intervention while maintaining measurement precision, thereby dramatically improving productivity by processing large volumes of product titles efficiently and consistently.
3Adaptability or versatility
If attribute standardization is implemented across varying database layouts, then adaptability improves, but device complexity increases
Solution Approach 1:
The patent creates a universal attribute extraction system that can handle multiple database layouts and terminologies through a single sequence labeling framework. The model is trained to recognize various attribute types (brand, RAM, hard drive space) regardless of how they are phrased or positioned in the product title. This universal approach enables the system to adapt to different database schemas and product catalogs without requiring separate extraction logic for each case, thereby improving adaptability while keeping system complexity manageable through a unified architecture.
Data Source
AI summary
A system and method for attribute extraction from product titles is presented. A set of product titles are read by an embodiment. The product titles are separated into a sequence of tokens, the tokens being determined by the presence of a separator character. The sequences of tokens are labeled according to a specific encoding scheme to denote attributes of a title, such as brand name and other features. The characteristics of the attributes within the title are determined to build a sequence labeling algorithm. The results of the encoding is compared to a normalization dictionary comparing standardized attributes. A blacklist can be consulted to further refine the method. A further validation can be performed to catch any remaining mistakes. Thereafter, a database entry can be made to associate the attribute with the item. A training set can be used to initialize the learning model. Other embodiments are also disclosed herein.