Minh Trung Nguyen


The Dots Have Their Values: Exploiting the Node-Edge Connections in Graph-based Neural Models for Document-level Relation Extraction
Hieu Minh Tran | Minh Trung Nguyen | Thien Huu Nguyen
Findings of the Association for Computational Linguistics: EMNLP 2020

The goal of Document-level Relation Extraction (DRE) is to recognize the relations between entity mentions that can span beyond sentence boundary. The current state-of-the-art method for this problem has involved the graph-based edge-oriented model where the entity mentions, entities, and sentences in the documents are used as the nodes of the document graphs for representation learning. However, this model does not capture the representations for the nodes in the graphs, thus preventing it from effectively encoding the specific and relevant information of the nodes for DRE. To address this issue, we propose to explicitly compute the representations for the nodes in the graph-based edge-oriented model for DRE. These node representations allow us to introduce two novel representation regularization mechanisms to improve the representation vectors for DRE. The experiments show that our model achieves state-of-the-art performance on two benchmark datasets.


An Empirical Study on Fine-Grained Named Entity Recognition
Khai Mai | Thai-Hoang Pham | Minh Trung Nguyen | Tuan Duc Nguyen | Danushka Bollegala | Ryohei Sasano | Satoshi Sekine
Proceedings of the 27th International Conference on Computational Linguistics

Named entity recognition (NER) has attracted a substantial amount of research. Recently, several neural network-based models have been proposed and achieved high performance. However, there is little research on fine-grained NER (FG-NER), in which hundreds of named entity categories must be recognized, especially for non-English languages. It is still an open question whether there is a model that is robust across various settings or the proper model varies depending on the language, the number of named entity categories, and the size of training datasets. This paper first presents an empirical comparison of FG-NER models for English and Japanese and demonstrates that LSTM+CNN+CRF (Ma and Hovy, 2016), one of the state-of-the-art methods for English NER, also works well for English FG-NER but does not work well for Japanese, a language that has a large number of character types. To tackle this problem, we propose a method to improve the neural network-based Japanese FG-NER performance by removing the CNN layer and utilizing dictionary and category embeddings. Experiment results show that the proposed method improves Japanese FG-NER F-score from 66.76% to 75.18%.


Extended Named Entity Recognition API and Its Applications in Language Education
Tuan Duc Nguyen | Khai Mai | Thai-Hoang Pham | Minh Trung Nguyen | Truc-Vien T. Nguyen | Takashi Eguchi | Ryohei Sasano | Satoshi Sekine
Proceedings of ACL 2017, System Demonstrations