537 research outputs found
A Hierarchical Framework for Relation Extraction with Reinforcement Learning
Most existing methods determine relation types only after all the entities
have been recognized, thus the interaction between relation types and entity
mentions is not fully modeled. This paper presents a novel paradigm to deal
with relation extraction by regarding the related entities as the arguments of
a relation. We apply a hierarchical reinforcement learning (HRL) framework in
this paradigm to enhance the interaction between entity mentions and relation
types. The whole extraction process is decomposed into a hierarchy of two-level
RL policies for relation detection and entity extraction respectively, so that
it is more feasible and natural to deal with overlapping relations. Our model
was evaluated on public datasets collected via distant supervision, and results
show that it gains better performance than existing methods and is more
powerful for extracting overlapping relations.Comment: To appear in AAAI 1
Global Normalization of Convolutional Neural Networks for Joint Entity and Relation Classification
We introduce globally normalized convolutional neural networks for joint
entity classification and relation extraction. In particular, we propose a way
to utilize a linear-chain conditional random field output layer for predicting
entity types and relations between entities at the same time. Our experiments
show that global normalization outperforms a locally normalized softmax layer
on a benchmark dataset.Comment: EMNLP 201
Joint Extraction of Entities and Relations Using Reinforcement Learning and Deep Learning
We use both reinforcement learning and deep learning to simultaneously extract entities and relations from unstructured texts. For reinforcement learning, we model the task as a two-step decision process. Deep learning is used to automatically capture the most important information from unstructured texts, which represent the state in the decision process. By designing the reward function per step, our proposed method can pass the information of entity extraction to relation extraction and obtain feedback in order to extract entities and relations simultaneously. Firstly, we use bidirectional LSTM to model the context information, which realizes preliminary entity extraction. On the basis of the extraction results, attention based method can represent the sentences that include target entity pair to generate the initial state in the decision process. Then we use Tree-LSTM to represent relation mentions to generate the transition state in the decision process. Finally, we employ Q-Learning algorithm to get control policy π in the two-step decision process. Experiments on ACE2005 demonstrate that our method attains better performance than the state-of-the-art method and gets a 2.4% increase in recall-score
An attentive neural architecture for joint segmentation and parsing and its application to real estate ads
In processing human produced text using natural language processing (NLP)
techniques, two fundamental subtasks that arise are (i) segmentation of the
plain text into meaningful subunits (e.g., entities), and (ii) dependency
parsing, to establish relations between subunits. In this paper, we develop a
relatively simple and effective neural joint model that performs both
segmentation and dependency parsing together, instead of one after the other as
in most state-of-the-art works. We will focus in particular on the real estate
ad setting, aiming to convert an ad to a structured description, which we name
property tree, comprising the tasks of (1) identifying important entities of a
property (e.g., rooms) from classifieds and (2) structuring them into a tree
format. In this work, we propose a new joint model that is able to tackle the
two tasks simultaneously and construct the property tree by (i) avoiding the
error propagation that would arise from the subtasks one after the other in a
pipelined fashion, and (ii) exploiting the interactions between the subtasks.
For this purpose, we perform an extensive comparative study of the pipeline
methods and the new proposed joint model, reporting an improvement of over
three percentage points in the overall edge F1 score of the property tree.
Also, we propose attention methods, to encourage our model to focus on salient
tokens during the construction of the property tree. Thus we experimentally
demonstrate the usefulness of attentive neural architectures for the proposed
joint model, showcasing a further improvement of two percentage points in edge
F1 score for our application.Comment: Preprint - Accepted for publication in Expert Systems with
Application
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