Correctly detecting the semantic type of data columns is crucial for data
science tasks such as automated data cleaning, schema matching, and data
discovery. Existing data preparation and analysis systems rely on dictionary
lookups and regular expression matching to detect semantic types. However,
these matching-based approaches often are not robust to dirty data and only
detect a limited number of types. We introduce Sherlock, a multi-input deep
neural network for detecting semantic types. We train Sherlock on $686,765$
data columns retrieved from the VizNet corpus by matching $78$ semantic types
from DBpedia to column headers. We characterize each matched column with
$1,588$ features describing the statistical properties, character
distributions, word embeddings, and paragraph vectors of column values.
Sherlock achieves a support-weighted F$_1$ score of $0.89$, exceeding that of
machine learning baselines, dictionary and regular expression benchmarks, and
the consensus of crowdsourced annotations.

本研究提出了 Sherlock，一种利用深度学习进行语义类型检测的多输入模型，并从 VizNet 语料库中训练出此模型，并取得了良好的检测效果，支持重量级 F1 得分高达 0.89。