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Thien Nguyen

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5 papers

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AAAI Conference 2020 Conference Paper

A Joint Model for Definition Extraction with Syntactic Connection and Semantic Consistency

Amir Veyseh
Franck Dernoncourt
Dejing Dou
Thien Nguyen

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 interdependencies 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 interdependencies. 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). The extensive experiments on three benchmark datasets demonstrate the effectiveness of our approach. 1

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AAAI Conference 2020 Conference Paper

Multi-Task Learning for Metaphor Detection with Graph Convolutional Neural Networks and Word Sense Disambiguation

Duong Le
My Thai
Thien Nguyen

The current deep learning works on metaphor detection have only considered this task independently, ignoring the useful knowledge from the related tasks and knowledge resources. In this work, we introduce two novel mechanisms to improve the performance of the deep learning models for metaphor detection. The ﬁrst mechanism employs graph convolutional neural networks (GCN) with dependency parse trees to directly connect the words of interest with their important context words for metaphor detection. The GCN networks in this work also present a novel control mechanism to ﬁlter the learned representation vectors to retain the most important information for metaphor detection. The second mechanism, on the other hand, features a multi-task learning framework that exploits the similarity between word sense disambiguation and metaphor detection to transfer the knowledge between the two tasks. The extensive experiments demonstrate the effectiveness of the proposed techniques, yielding the state-ofthe-art performance over several datasets.

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AAAI Conference 2020 Conference Paper

Multi-View Consistency for Relation Extraction via Mutual Information and Structure Prediction

Amir Veyseh
Franck Dernoncourt
My Thai
Dejing Dou
Thien Nguyen

Relation Extraction (RE) is one of the fundamental tasks in Information Extraction. The goal of this task is to ﬁnd the semantic relations between entity mentions in text. It has been shown in many previous work that the structure of the sentences (i. e. , dependency trees) can provide important information/features for the RE models. However, the common limitation of the previous work on RE is the reliance on some external parsers to obtain the syntactic trees for the sentence structures. On the one hand, it is not guaranteed that the independent external parsers can offer the optimal sentence structures for RE and the customized structures for RE might help to further improve the performance. On the other hand, the quality of the external parsers might suffer when applied to different domains, thus also affecting the performance of the RE models on such domains. In order to overcome this issue, we introduce a novel method for RE that simultaneously induces the structures and predicts the relations for the input sentences, thus avoiding the external parsers and potentially leading to better sentence structures for RE. Our general strategy to learn the RE-speciﬁc structures is to apply two different methods to infer the structures for the input sentences (i. e. , two views). We then introduce several mechanisms to encourage the structure and semantic consistencies between these two views so the effective structure and semantic representations for RE can emerge. We perform extensive experiments on the ACE 2005 and SemEval 2010 datasets to demonstrate the advantages of the proposed method, leading to the stateof-the-art performance on such datasets.

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IJCAI Conference 2019 Conference Paper

Improving Cross-Domain Performance for Relation Extraction via Dependency Prediction and Information Flow Control

Amir Pouran Ben Veyseh
Thien Nguyen
Dejing Dou

Relation Extraction (RE) is one of the fundamental tasks in Information Extraction and Natural Language Processing. Dependency trees have been shown to be a very useful source of information for this task. The current deep learning models for relation extraction has mainly exploited this dependency information by guiding their computation along the structures of the dependency trees. One potential problem with this approach is it might prevent the models from capturing important context information beyond syntactic structures and cause the poor cross-domain generalization. This paper introduces a novel method to use dependency trees in RE for deep learning models that jointly predicts dependency and semantics relations. We also propose a new mechanism to control the information flow in the model based on the input entity mentions. Our extensive experiments on benchmark datasets show that the proposed model outperforms the existing methods for RE significantly.

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AAAI Conference 2018 Conference Paper

Graph Convolutional Networks With Argument-Aware Pooling for Event Detection

Thien Nguyen
Ralph Grishman

The current neural network models for event detection have only considered the sequential representation of sentences. Syntactic representations have not been explored in this area although they provide an effective mechanism to directly link words to their informative context for event detection in the sentences. In this work, we investigate a convolutional neural network based on dependency trees to perform event detection. We propose a novel pooling method that relies on entity mentions to aggregate the convolution vectors. The extensive experiments demonstrate the beneﬁts of the dependencybased convolutional neural networks and the entity mentionbased pooling method for event detection. We achieve the state-of-the-art performance on widely used datasets with both perfect and predicted entity mentions.

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