34,089 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)
Joint RNN Model for Argument Component Boundary Detection
Argument Component Boundary Detection (ACBD) is an important sub-task in
argumentation mining; it aims at identifying the word sequences that constitute
argument components, and is usually considered as the first sub-task in the
argumentation mining pipeline. Existing ACBD methods heavily depend on
task-specific knowledge, and require considerable human efforts on
feature-engineering. To tackle these problems, in this work, we formulate ACBD
as a sequence labeling problem and propose a variety of Recurrent Neural
Network (RNN) based methods, which do not use domain specific or handcrafted
features beyond the relative position of the sentence in the document. In
particular, we propose a novel joint RNN model that can predict whether
sentences are argumentative or not, and use the predicted results to more
precisely detect the argument component boundaries. We evaluate our techniques
on two corpora from two different genres; results suggest that our joint RNN
model obtain the state-of-the-art performance on both datasets.Comment: 6 pages, 3 figures, submitted to IEEE SMC 201
Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extraction
Most existing event extraction (EE) methods merely extract event arguments
within the sentence scope. However, such sentence-level EE methods struggle to
handle soaring amounts of documents from emerging applications, such as
finance, legislation, health, etc., where event arguments always scatter across
different sentences, and even multiple such event mentions frequently co-exist
in the same document. To address these challenges, we propose a novel
end-to-end model, Doc2EDAG, which can generate an entity-based directed acyclic
graph to fulfill the document-level EE (DEE) effectively. Moreover, we
reformalize a DEE task with the no-trigger-words design to ease the
document-level event labeling. To demonstrate the effectiveness of Doc2EDAG, we
build a large-scale real-world dataset consisting of Chinese financial
announcements with the challenges mentioned above. Extensive experiments with
comprehensive analyses illustrate the superiority of Doc2EDAG over
state-of-the-art methods. Data and codes can be found at
https://github.com/dolphin-zs/Doc2EDAG.Comment: Accepted by EMNLP 201
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