Named entity recognition and relation classification are key stages for
extracting information from unstructured text. Several natural language
processing applications utilize the two tasks, such as information retrieval,
knowledge graph construction and completion, question answering and other
domain-specific applications, such as biomedical data mining. We present a
survey of recent approaches in the two tasks with focus on few-shot learning
approaches. Our work compares the main approaches followed in the two
paradigms. Additionally, we report the latest metric scores in the two tasks
with a structured analysis that considers the results in the few-shot learning
scope.

最近的方法调查了命名实体识别和关系分类这两个任务，重点关注少样本学习方法，并比较了两个任务中的主要方法，报告了少样本学习范围内的最新指标得分。

近期具重点于少样本学习方法的命名实体识别与关系分类方法综述

A Survey on Recent Named Entity Recognition and Relation Classification  Methods with Focus on Few-Shot Learning Approaches

Feature selection plays a critical role in biomedical data mining, driven by
increasing feature dimensionality in target problems and growing interest in
advanced but computationally expensive methodologies able to model complex
associations. Specifically, there is a need for feature selection methods that
are computationally efficient, yet sensitive to complex patterns of
association, e.g. interactions, so that informative features are not mistakenly
eliminated prior to downstream modeling. This paper focuses on Relief-based
algorithms (RBAs), a unique family of filter-style feature selection algorithms
that have gained appeal by striking an effective balance between these
objectives while flexibly adapting to various data characteristics, e.g.
classification vs. regression. First, this work broadly examines types of
feature selection and defines RBAs within that context. Next, we introduce the
original Relief algorithm and associated concepts, emphasizing the intuition
behind how it works, how feature weights generated by the algorithm can be
interpreted, and why it is sensitive to feature interactions without evaluating
combinations of features. Lastly, we include an expansive review of RBA
methodological research beyond Relief and its popular descendant, ReliefF. In
particular, we characterize branches of RBA research, and provide comparative
summaries of RBA algorithms including contributions, strategies, functionality,
time complexity, adaptation to key data characteristics, and software
availability.

本文对生物医学数据挖掘中的特征选择进行了研究，介绍了一种名为 “Relief-based algorithms” 的有效的特征选择方法，该方法可在考虑复杂关联性的同时在计算效率上实现有效平衡。