Dai Quoc Nguyen


2020

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A Relational Memory-based Embedding Model for Triple Classification and Search Personalization
Dai Quoc Nguyen | Tu Nguyen | Dinh Phung
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Knowledge graph embedding methods often suffer from a limitation of memorizing valid triples to predict new ones for triple classification and search personalization problems. To this end, we introduce a novel embedding model, named R-MeN, that explores a relational memory network to encode potential dependencies in relationship triples. R-MeN considers each triple as a sequence of 3 input vectors that recurrently interact with a memory using a transformer self-attention mechanism. Thus R-MeN encodes new information from interactions between the memory and each input vector to return a corresponding vector. Consequently, R-MeN feeds these 3 returned vectors to a convolutional neural network-based decoder to produce a scalar score for the triple. Experimental results show that our proposed R-MeN obtains state-of-the-art results on SEARCH17 for the search personalization task, and on WN11 and FB13 for the triple classification task.

2019

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A Capsule Network-based Embedding Model for Knowledge Graph Completion and Search Personalization
Dai Quoc Nguyen | Thanh Vu | Tu Dinh Nguyen | Dat Quoc Nguyen | Dinh Phung
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

In this paper, we introduce an embedding model, named CapsE, exploring a capsule network to model relationship triples (subject, relation, object). Our CapsE represents each triple as a 3-column matrix where each column vector represents the embedding of an element in the triple. This 3-column matrix is then fed to a convolution layer where multiple filters are operated to generate different feature maps. These feature maps are reconstructed into corresponding capsules which are then routed to another capsule to produce a continuous vector. The length of this vector is used to measure the plausibility score of the triple. Our proposed CapsE obtains better performance than previous state-of-the-art embedding models for knowledge graph completion on two benchmark datasets WN18RR and FB15k-237, and outperforms strong search personalization baselines on SEARCH17.

2018

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NIHRIO at SemEval-2018 Task 3: A Simple and Accurate Neural Network Model for Irony Detection in Twitter
Thanh Vu | Dat Quoc Nguyen | Xuan-Son Vu | Dai Quoc Nguyen | Michael Catt | Michael Trenell
Proceedings of The 12th International Workshop on Semantic Evaluation

This paper describes our NIHRIO system for SemEval-2018 Task 3 “Irony detection in English tweets.” We propose to use a simple neural network architecture of Multilayer Perceptron with various types of input features including: lexical, syntactic, semantic and polarity features. Our system achieves very high performance in both subtasks of binary and multi-class irony detection in tweets. In particular, we rank at least fourth using the accuracy metric and sixth using the F1 metric. Our code is available at: https://github.com/NIHRIO/IronyDetectionInTwitter

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A Fast and Accurate Vietnamese Word Segmenter
Dat Quoc Nguyen | Dai Quoc Nguyen | Thanh Vu | Mark Dras | Mark Johnson
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network
Dai Quoc Nguyen | Tu Dinh Nguyen | Dat Quoc Nguyen | Dinh Phung
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)

In this paper, we propose a novel embedding model, named ConvKB, for knowledge base completion. Our model ConvKB advances state-of-the-art models by employing a convolutional neural network, so that it can capture global relationships and transitional characteristics between entities and relations in knowledge bases. In ConvKB, each triple (head entity, relation, tail entity) is represented as a 3-column matrix where each column vector represents a triple element. This 3-column matrix is then fed to a convolution layer where multiple filters are operated on the matrix to generate different feature maps. These feature maps are then concatenated into a single feature vector representing the input triple. The feature vector is multiplied with a weight vector via a dot product to return a score. This score is then used to predict whether the triple is valid or not. Experiments show that ConvKB achieves better link prediction performance than previous state-of-the-art embedding models on two benchmark datasets WN18RR and FB15k-237.

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VnCoreNLP: A Vietnamese Natural Language Processing Toolkit
Thanh Vu | Dat Quoc Nguyen | Dai Quoc Nguyen | Mark Dras | Mark Johnson
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations

We present an easy-to-use and fast toolkit, namely VnCoreNLP—a Java NLP annotation pipeline for Vietnamese. Our VnCoreNLP supports key natural language processing (NLP) tasks including word segmentation, part-of-speech (POS) tagging, named entity recognition (NER) and dependency parsing, and obtains state-of-the-art (SOTA) results for these tasks. We release VnCoreNLP to provide rich linguistic annotations to facilitate research work on Vietnamese NLP. Our VnCoreNLP is open-source and available at: https://github.com/vncorenlp/VnCoreNLP

2017

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Sequence to Sequence Learning for Event Prediction
Dai Quoc Nguyen | Dat Quoc Nguyen | Cuong Xuan Chu | Stefan Thater | Manfred Pinkal
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

This paper presents an approach to the task of predicting an event description from a preceding sentence in a text. Our approach explores sequence-to-sequence learning using a bidirectional multi-layer recurrent neural network. Our approach substantially outperforms previous work in terms of the BLEU score on two datasets derived from WikiHow and DeScript respectively. Since the BLEU score is not easy to interpret as a measure of event prediction, we complement our study with a second evaluation that exploits the rich linguistic annotation of gold paraphrase sets of events.

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A Mixture Model for Learning Multi-Sense Word Embeddings
Dai Quoc Nguyen | Dat Quoc Nguyen | Ashutosh Modi | Stefan Thater | Manfred Pinkal
Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)

Word embeddings are now a standard technique for inducing meaning representations for words. For getting good representations, it is important to take into account different senses of a word. In this paper, we propose a mixture model for learning multi-sense word embeddings. Our model generalizes the previous works in that it allows to induce different weights of different senses of a word. The experimental results show that our model outperforms previous models on standard evaluation tasks.

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From Word Segmentation to POS Tagging for Vietnamese
Dat Quoc Nguyen | Thanh Vu | Dai Quoc Nguyen | Mark Dras | Mark Johnson
Proceedings of the Australasian Language Technology Association Workshop 2017

2014

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RDRPOSTagger: A Ripple Down Rules-based Part-Of-Speech Tagger
Dat Quoc Nguyen | Dai Quoc Nguyen | Dang Duc Pham | Son Bao Pham
Proceedings of the Demonstrations at the 14th Conference of the European Chapter of the Association for Computational Linguistics

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Sentiment Classification on Polarity Reviews: An Empirical Study Using Rating-based Features
Dai Quoc Nguyen | Dat Quoc Nguyen | Thanh Vu | Son Bao Pham
Proceedings of the 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

2013

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A Two-Stage Classifier for Sentiment Analysis
Dai Quoc Nguyen | Dat Quoc Nguyen | Son Bao Pham
Proceedings of the Sixth International Joint Conference on Natural Language Processing

2011

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Systematic Knowledge Acquisition for Question Analysis
Dat Quoc Nguyen | Dai Quoc Nguyen | Son Bao Pham
Proceedings of the International Conference Recent Advances in Natural Language Processing 2011