118 research outputs found
A Joint Model for Definition Extraction with Syntactic Connection and Semantic Consistency
Definition Extraction (DE) is one of the well-known topics in Information
Extraction that aims to identify terms and their corresponding definitions in
unstructured texts. This task can be formalized either as a sentence
classification task (i.e., containing term-definition pairs or not) or a
sequential labeling task (i.e., identifying the boundaries of the terms and
definitions). The previous works for DE have only focused on one of the two
approaches, failing to model the inter-dependencies between the two tasks. In
this work, we propose a novel model for DE that simultaneously performs the two
tasks in a single framework to benefit from their inter-dependencies. Our model
features deep learning architectures to exploit the global structures of the
input sentences as well as the semantic consistencies between the terms and the
definitions, thereby improving the quality of the representation vectors for
DE. Besides the joint inference between sentence classification and sequential
labeling, the proposed model is fundamentally different from the prior work for
DE in that the prior work has only employed the local structures of the input
sentences (i.e., word-to-word relations), and not yet considered the semantic
consistencies between terms and definitions. In order to implement these novel
ideas, our model presents a multi-task learning framework that employs graph
convolutional neural networks and predicts the dependency paths between the
terms and the definitions. We also seek to enforce the consistency between the
representations of the terms and definitions both globally (i.e., increasing
semantic consistency between the representations of the entire sentences and
the terms/definitions) and locally (i.e., promoting the similarity between the
representations of the terms and the definitions)
Exploiting the Matching Information in the Support Set for Few Shot Event Classification
The existing event classification (EC) work primarily focuseson the
traditional supervised learning setting in which models are unableto extract
event mentions of new/unseen event types. Few-shot learninghas not been
investigated in this area although it enables EC models toextend their
operation to unobserved event types. To fill in this gap, inthis work, we
investigate event classification under the few-shot learningsetting. We propose
a novel training method for this problem that exten-sively exploit the support
set during the training process of a few-shotlearning model. In particular, in
addition to matching the query exam-ple with those in the support set for
training, we seek to further matchthe examples within the support set
themselves. This method providesmore training signals for the models and can be
applied to every metric-learning-based few-shot learning methods. Our extensive
experiments ontwo benchmark EC datasets show that the proposed method can
improvethe best reported few-shot learning models by up to 10% on accuracyfor
event classificationComment: Pacific-Asia Conference on Knowledge Discovery and Data Mining
(PAKDD) 202
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