Kiem-Hieu Nguyen


2024

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BKEE: Pioneering Event Extraction in the Vietnamese Language
Thi-Nhung Nguyen | Bang Tien Tran | Trong-Nghia Luu | Thien Huu Nguyen | Kiem-Hieu Nguyen
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

Event Extraction (EE) is a fundamental task in information extraction, aimed at identifying events and their associated arguments within textual data. It holds significant importance in various applications and serves as a catalyst for the development of related tasks. Despite the availability of numerous datasets and methods for event extraction in various languages, there has been a notable absence of a dedicated dataset for the Vietnamese language. To address this limitation, we propose BKEE, a novel event extraction dataset for Vietnamese. BKEE encompasses over 33 distinct event types and 28 different event argument roles, providing a labeled dataset for entity mentions, event mentions, and event arguments on 1066 documents. Additionally, we establish robust baselines for potential downstream tasks on this dataset, facilitating the analysis of challenges and future development prospects in the field of Vietnamese event extraction.

2023

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A Self-enhancement Multitask Framework for Unsupervised Aspect Category Detection
Thi-Nhung Nguyen | Hoang Ngo | Kiem-Hieu Nguyen | Tuan-Dung Cao
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Our work addresses the problem of unsupervised Aspect Category Detection using a small set of seed words. Recent works have focused on learning embedding spaces for seed words and sentences to establish similarities between sentences and aspects. However, aspect representations are limited by the quality of initial seed words, and model performances are compromised by noise. To mitigate this limitation, we propose a simple framework that automatically enhances the quality of initial seed words and selects high-quality sentences for training instead of using the entire dataset. Our main concepts are to add a number of seed words to the initial set and to treat the task of noise resolution as a task of augmenting data for a low-resource task. In addition, we jointly train Aspect Category Detection with Aspect Term Extraction and Aspect Term Polarity to further enhance performance. This approach facilitates shared representation learning, allowing Aspect Category Detection to benefit from the additional guidance offered by other tasks. Extensive experiments demonstrate that our framework surpasses strong baselines on standard datasets.

2021

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An Uncertainty-Aware Encoder for Aspect Detection
Thi-Nhung Nguyen | Kiem-Hieu Nguyen | Young-In Song | Tuan-Dung Cao
Findings of the Association for Computational Linguistics: EMNLP 2021

Aspect detection is a fundamental task in opinion mining. Previous works use seed words either as priors of topic models, as anchors to guide the learning of aspects, or as features of aspect classifiers. This paper presents a novel weakly-supervised method to exploit seed words for aspect detection based on an encoder architecture. The encoder maps segments and aspects into a low-dimensional embedding space. The goal is approximating similarity between segments and aspects in the embedding space and their ground-truth similarity generated from seed words. An objective function is proposed to capture the uncertainty of ground-truth similarity. Our method outperforms previous works on several benchmarks in various domains.

2020

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Utilizing Bert for Question Retrieval on Vietnameses E-commerce Sites
Thi-Thanh Ha | Van-Nha Nguyen | Kiem-Hieu Nguyen | Kim-Anh Nguyen | Tien-Thanh Nguyen
Proceedings of the 34th Pacific Asia Conference on Language, Information and Computation

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A Study on Seq2seq for Sentence Compressionin Vietnamese
Thi-Trang Nguyen | Huu-Hoang Nguyen | Kiem-Hieu Nguyen
Proceedings of the 34th Pacific Asia Conference on Language, Information and Computation

2018

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BKTreebank: Building a Vietnamese Dependency Treebank
Kiem-Hieu Nguyen
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2016

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A Dataset for Open Event Extraction in English
Kiem-Hieu Nguyen | Xavier Tannier | Olivier Ferret | Romaric Besançon
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

This article presents a corpus for development and testing of event schema induction systems in English. Schema induction is the task of learning templates with no supervision from unlabeled texts, and to group together entities corresponding to the same role in a template. Most of the previous work on this subject relies on the MUC-4 corpus. We describe the limits of using this corpus (size, non-representativeness, similarity of roles across templates) and propose a new, partially-annotated corpus in English which remedies some of these shortcomings. We make use of Wikinews to select the data inside the category Laws & Justice, and query Google search engine to retrieve different documents on the same events. Only Wikinews documents are manually annotated and can be used for evaluation, while the others can be used for unsupervised learning. We detail the methodology used for building the corpus and evaluate some existing systems on this new data.

2015

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Désambiguïsation d’entités pour l’induction non supervisée de schémas événementiels
Kiem-Hieu Nguyen | Xavier Tannier | Olivier Ferret | Romaric Besançon
Actes de la 22e conférence sur le Traitement Automatique des Langues Naturelles. Articles longs

Cet article présente un modèle génératif pour l’induction non supervisée d’événements. Les précédentes méthodes de la littérature utilisent uniquement les têtes des syntagmes pour représenter les entités. Pourtant, le groupe complet (par exemple, ”un homme armé”) apporte une information plus discriminante (que ”homme”). Notre modèle tient compte de cette information et la représente dans la distribution des schémas d’événements. Nous montrons que ces relations jouent un rôle important dans l’estimation des paramètres, et qu’elles conduisent à des distributions plus cohérentes et plus discriminantes. Les résultats expérimentaux sur le corpus de MUC-4 confirment ces progrès.

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Generative Event Schema Induction with Entity Disambiguation
Kiem-Hieu Nguyen | Xavier Tannier | Olivier Ferret | Romaric Besançon
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

2014

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Ranking Multidocument Event Descriptions for Building Thematic Timelines
Kiem-Hieu Nguyen | Xavier Tannier | Veronique Moriceau
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

2012

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Semantic Relatedness for Biomedical Word Sense Disambiguation
Kiem-Hieu Nguyen | Cheol-Young Ock
Workshop Proceedings of TextGraphs-7: Graph-based Methods for Natural Language Processing