Advanced omics technologies and facilities generate a wealth of valuable data
daily; however, the data often lacks the essential metadata required for
researchers to find and search them effectively. The lack of metadata poses a
significant challenge in the utilization of these datasets. Machine
learning-based metadata extraction techniques have emerged as a potentially
viable approach to automatically annotating scientific datasets with the
metadata necessary for enabling effective search. Text labeling, usually
performed manually, plays a crucial role in validating machine-extracted
metadata. However, manual labeling is time-consuming; thus, there is an need to
develop automated text labeling techniques in order to accelerate the process
of scientific innovation. This need is particularly urgent in fields such as
environmental genomics and microbiome science, which have historically received
less attention in terms of metadata curation and creation of gold-standard text
mining datasets.
In this paper, we present two novel automated text labeling approaches for
the validation of ML-generated metadata for unlabeled texts, with specific
applications in environmental genomics. Our techniques show the potential of
two new ways to leverage existing information about the unlabeled texts and the
scientific domain. The first technique exploits relationships between different
types of data sources related to the same research study, such as publications
and proposals. The second technique takes advantage of domain-specific
controlled vocabularies or ontologies. In this paper, we detail applying these
approaches for ML-generated metadata validation. Our results show that the
proposed label assignment approaches can generate both generic and
highly-specific text labels for the unlabeled texts, with up to 44% of the
labels matching with those suggested by a ML keyword extraction algorithm.

本文提出了两种新颖的自动文本标注方法用于验证机器学习生成的未标记文本的元数据，特别适用于环境基因组学领域。我们的技术展示了利用未标记文本和科学领域的现有信息的两种新方法的潜力。结果表明，所提出的标签分配方法可以为未标记文本生成通用和高度特定的文本标签，其中有多达 44% 的标签与机器学习关键词提取算法建议的标签匹配。

ML 基础关键短语提取与验证的科学文本自动标注

Automated Annotation of Scientific Texts for ML-based Keyphrase  Extraction and Validation

Creating and collecting labeled data is one of the major bottlenecks in
machine learning pipelines and the emergence of automated feature generation
techniques such as deep learning, which typically requires a lot of training
data, has further exacerbated the problem. While weak-supervision techniques
have circumvented this bottleneck, existing frameworks either require users to
write a set of diverse, high-quality rules to label data (e.g., Snorkel), or
require a labeled subset of the data to automatically mine rules (e.g., Snuba).
The process of manually writing rules can be tedious and time consuming. At the
same time, creating a labeled subset of the data can be costly and even
infeasible in imbalanced settings. This is due to the fact that a random sample
in imbalanced settings often contains only a few positive instances.
To address these shortcomings, we present Darwin, an interactive system
designed to alleviate the task of writing rules for labeling text data in
weakly-supervised settings. Given an initial labeling rule, Darwin
automatically generates a set of candidate rules for the labeling task at hand,
and utilizes the annotator's feedback to adapt the candidate rules. We describe
how Darwin is scalable and versatile. It can operate over large text corpora
(i.e., more than 1 million sentences) and supports a wide range of labeling
functions (i.e., any function that can be specified using a context free
grammar). Finally, we demonstrate with a suite of experiments over five
real-world datasets that Darwin enables annotators to generate
weakly-supervised labels efficiently and with a small cost. In fact, our
experiments show that rules discovered by Darwin on average identify 40% more
positive instances compared to Snuba even when it is provided with 1000 labeled
instances.

介绍了一种交互式系统 Darwin，可以协助 weakly-supervised learning 文本数据的标注规则生成。通过候选规则的自动生成和标注者的反馈机制，Darwin 可以高效地生成规则，实验结果表明 Darwin 能够比已有的方法更有效地识别出正例。