In this paper, we present LaTeX-Numeric - a high-precision fully-automated scalable framework for extracting E-commerce numeric attributes from product text like product description. Most of the past work on attribute extraction is not scalable as they rely on manually curated training data, either with or without the use of active learning. We rely on distant supervision for training data generation, removing dependency on manual labels. One issue with distant supervision is that it leads to incomplete training annotation due to missing attribute values while matching. We propose a multi-task learning architecture to deal with missing labels in the training data, leading to F1 improvement of 9.2% for numeric attributes over single-task architecture. While multi-task architecture benefits both numeric and non-numeric attributes, we present automated techniques to further improve the numeric attributes extraction models. Numeric attributes require a list of units (or aliases) for better matching with distant supervision. We propose an automated algorithm for alias creation using product text and attribute values, leading to a 20.2% F1 improvement. Extensive experiments on real world dataset for 20 numeric attributes across 5 product categories and 3 English marketplaces show that LaTeX-Numeric achieves a high F1-score, without any manual intervention, making it suitable for practical applications. Finally, we show that the improvements are language-agnostic and LaTeX-Numeric achieves 13.9% F1 improvement for 3 Romance languages.

本文提出了LaTeX-Numeric，用于从产品文本（例如产品描述）中提取电子商务数值属性的高精度全自动可扩展框架。该框架通过远程监督进行训练数据生成，摆脱了手动标签的依赖，并提出了一种多任务学习架构来处理训练数据中的缺失标签，从而提高了数值属性的 F1 值。同时，提出了自动技术以进一步改进数值属性提取模型，其中包括单位/别名列表的自动创建等方面的改进技术，最终证明这些改进是与语言无关的，并取得了良好的提取效果。

LaTeX-Numeric: 面向电商数字属性的语言无关文本属性提取