108,119 research outputs found
Substituting Data Annotation with Balanced Updates and Collective Loss in Multi-label Text Classification
Multi-label text classification (MLTC) is the task of assigning multiple
labels to a given text, and has a wide range of application domains. Most
existing approaches require an enormous amount of annotated data to learn a
classifier and/or a set of well-defined constraints on the label space
structure, such as hierarchical relations which may be complicated to provide
as the number of labels increases. In this paper, we study the MLTC problem in
annotation-free and scarce-annotation settings in which the magnitude of
available supervision signals is linear to the number of labels. Our method
follows three steps, (1) mapping input text into a set of preliminary label
likelihoods by natural language inference using a pre-trained language model,
(2) calculating a signed label dependency graph by label descriptions, and (3)
updating the preliminary label likelihoods with message passing along the label
dependency graph, driven with a collective loss function that injects the
information of expected label frequency and average multi-label cardinality of
predictions. The experiments show that the proposed framework achieves
effective performance under low supervision settings with almost imperceptible
computational and memory overheads added to the usage of pre-trained language
model outperforming its initial performance by 70\% in terms of example-based
F1 score.Comment: Proc. Conf. Lifelong Learning Agents (CoLLAs), 202
A review of multi-instance learning assumptions
Multi-instance (MI) learning is a variant of inductive machine learning, where each learning example contains a bag of instances instead of a single feature vector. The term commonly refers to the supervised setting, where each bag is associated with a label. This type of representation is a natural fit for a number of real-world learning scenarios, including drug activity prediction and image classification, hence many MI learning algorithms have been proposed. Any MI learning method must relate instances to bag-level class labels, but many types of relationships between instances and class labels are possible. Although all early work in MI learning assumes a specific MI concept class known to be appropriate for a drug activity prediction domain; this ‘standard MI assumption’ is not guaranteed to hold in other domains. Much of the recent work in MI learning has concentrated on a relaxed view of the MI problem, where the standard MI assumption is dropped, and alternative assumptions are considered instead. However, often it is not clearly stated what particular assumption is used and how it relates to other assumptions that have been proposed. In this paper, we aim to clarify the use of alternative MI assumptions by reviewing the work done in this area
Supervised Collective Classification for Crowdsourcing
Crowdsourcing utilizes the wisdom of crowds for collective classification via
information (e.g., labels of an item) provided by labelers. Current
crowdsourcing algorithms are mainly unsupervised methods that are unaware of
the quality of crowdsourced data. In this paper, we propose a supervised
collective classification algorithm that aims to identify reliable labelers
from the training data (e.g., items with known labels). The reliability (i.e.,
weighting factor) of each labeler is determined via a saddle point algorithm.
The results on several crowdsourced data show that supervised methods can
achieve better classification accuracy than unsupervised methods, and our
proposed method outperforms other algorithms.Comment: to appear in IEEE Global Communications Conference (GLOBECOM)
Workshop on Networking and Collaboration Issues for the Internet of
Everythin
On Classification with Bags, Groups and Sets
Many classification problems can be difficult to formulate directly in terms
of the traditional supervised setting, where both training and test samples are
individual feature vectors. There are cases in which samples are better
described by sets of feature vectors, that labels are only available for sets
rather than individual samples, or, if individual labels are available, that
these are not independent. To better deal with such problems, several
extensions of supervised learning have been proposed, where either training
and/or test objects are sets of feature vectors. However, having been proposed
rather independently of each other, their mutual similarities and differences
have hitherto not been mapped out. In this work, we provide an overview of such
learning scenarios, propose a taxonomy to illustrate the relationships between
them, and discuss directions for further research in these areas
- …