DCoM：一种用于语义数据类型检测的深度列映射器
DCoM: A Deep Column Mapper for Semantic Data Type Detection
摘要：
语义数据类型检测是数据科学中一项非常重要的任务，可以实现数据自动清洗、模式匹配、数据发现、语义数据类型规范化和敏感数据识别等。现有的方法包括基于正则表达式或基于字典查找的方法，这些方法对于脏的以及看不见的数据不是鲁棒的，并且限于要预测的非常少数量的语义数据类型。现有的机器学习方法从数据中提取大量的工程特征，并为此目的构建逻辑回归、随机森林或前馈神经网络。在本文中，我们介绍了DCoM，这是一个基于多输入NLP的深度神经网络的集合，用于检测语义数据类型，其中不是从数据中提取大量特征，而是将列（或实例）的原始值作为文本提供给模型。我们对从VizNet语料库中提取的686，765个数据列进行训练DCoM，这些数据列具有78种不同的语义数据类型。DCoM优于其他当代结果，在同一数据集上具有相当显著的优势。
英文摘要：
Detection of semantic data types is a very crucial task in data science for automated data cleaning, schema matching, data discovery, semantic data type normalization and sensitive data identification. Existing methods include regular expression-based or dictionary lookup-based methods that are not robust to dirty as well unseen data and are limited to a very less number of semantic data types to predict. Existing Machine Learning methods extract large number of engineered features from data and build logistic regression, random forest or feedforward neural network for this purpose. In this paper, we introduce DCoM, a collection of multi-input NLP-based deep neural networks to detect semantic data types where instead of extracting large number of features from the data, we feed the raw values of columns (or instances) to the model as texts. We train DCoM on 686,765 data columns extracted from VizNet corpus with 78 different semantic data types. DCoM outperforms other contemporary results with a quite significant margin on the same dataset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
