Partial label learning (PLL) is an important problem that allows each
training example to be labeled with a coarse candidate set, which well suits
many real-world data annotation scenarios with label ambiguity. Despite the
promise, the performance of PLL often lags behind the supervised counterpart.
In this work, we bridge the gap by addressing two key research challenges in
PLL -- representation learning and label disambiguation -- in one coherent
framework. Specifically, our proposed framework PiCO consists of a contrastive
learning module along with a novel class prototype-based label disambiguation
algorithm. PiCO produces closely aligned representations for examples from the
same classes and facilitates label disambiguation. Theoretically, we show that
these two components are mutually beneficial, and can be rigorously justified
from an expectation-maximization (EM) algorithm perspective. Moreover, we study
a challenging yet practical noisy partial label learning setup, where the
ground-truth may not be included in the candidate set. To remedy this problem,
we present an extension PiCO+ that performs distance-based clean sample
selection and learns robust classifiers by a semi-supervised contrastive
learning algorithm. Extensive experiments demonstrate that our proposed methods
significantly outperform the current state-of-the-art approaches in standard
and noisy PLL tasks and even achieve comparable results to fully supervised
learning.

本文提出一个 PiCO 框架，包括对比学习模块和新型基于原型的标签消歧算法用于解决部分标签学习的问题。该框架可以对相同类别的样例进行紧密对齐并促进标签消歧，并在嘈杂的部分标签学习任务中表现出色，甚至可以与完全监督的学习相媲美。

PiCO+：面向部分标签学习的对比标签消歧技术

PiCO+: Contrastive Label Disambiguation for Robust Partial Label  Learning

The PICO framework (Population, Intervention, Comparison, and Outcome) is
usually used to formulate evidence in the medical domain. The major task of
PICO extraction is to extract sentences from medical literature and classify
them into each class. However, in most circumstances, there will be more than
one evidences in an extracted sentence even it has been categorized to a
certain class. In order to address this problem, we propose a step-wise disease
Named Entity Recognition (DNER) extraction and PICO identification method. With
our method, sentences in paper title and abstract are first classified into
different classes of PICO, and medical entities are then identified and
classified into P and O. Different kinds of deep learning frameworks are used
and experimental results show that our method will achieve high performance and
fine-grained extraction results comparing with conventional PICO extraction
works.

提出了一种基于疾病命名实体识别的 PICO 方法，使用深度学习模型从医学文献中提取出句子并将其分类，并在此基础上识别并分类出 P 和 O，并且通过实验结果展示了该方法可以取得比传统 PICO 提取方法更高的性能和细粒度的提取结果。