Dinh Dien

Also published as: Dien Dinh


2020

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Identifying Authors Based on Stylometric measures of Vietnamese texts
Ho Ngoc Lam | Vo Diep Nhu | Dinh Dien | Nguyen Tuyet Nhung
Proceedings of the 34th Pacific Asia Conference on Language, Information and Computation

2014

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Building English-Vietnamese Named Entity Corpus with Aligned Bilingual News Articles
Quoc Hung Ngo | Dinh Dien | Werner Winiwarter
Proceedings of the Fifth Workshop on South and Southeast Asian Natural Language Processing

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A Novel Approach for Handling Unknown Word Problem in Chinese-Vietnamese Machine Translation
Phuoc Tran | Dien Dinh
International Journal of Computational Linguistics & Chinese Language Processing, Volume 19, Number 1, March 2014

2010

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An ontology-driven system for detecting global health events
Nigel Collier | Reiko Matsuda Goodwin | John McCrae | Son Doan | Ai Kawazoe | Mike Conway | Asanee Kawtrakul | Koichi Takeuchi | Dinh Dien
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

2003

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POS-Tagger for English-Vietnamese Bilingual Corpus
Dinh Dien | Hoang Kiem
Proceedings of the HLT-NAACL 2003 Workshop on Building and Using Parallel Texts: Data Driven Machine Translation and Beyond

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A hybrid approach to word order transfer in the English-to-Vietnamese machine translation
Dinh Dien | Nguyen Luu Thuy Ngan | Do Xuan Quang | Van Chi Nam
Proceedings of Machine Translation Summit IX: Papers

Word Order transfer is a compulsory stage and has a great effect on the translation result of a transfer-based machine translation system. To solve this problem, we can use fixed rules (rule-based) or stochastic methods (corpus-based) which extract word order transfer rules between two languages. However, each approach has its own advantages and disadvantages. In this paper, we present a hybrid approach based on fixed rules and Transformation-Based Learning (or TBL) method. Our purpose is to transfer automatically the English word orders into the Vietnamese ones. The learning process will be trained on the annotated bilingual corpus (named EVC: English-Vietnamese Corpus) that has been automatically word-aligned, phrase-aligned and POS-tagged. This transfer result is being used for the transfer module in the English-Vietnamese transfer-based machine translation system.

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BTL: a hybrid model for English-Vietnamese machine translation
Dinh Dien | Kiem Hoang | Eduard Hovy
Proceedings of Machine Translation Summit IX: Papers

Machine Translation (MT) is the most interesting and difficult task which has been posed since the beginning of computer history. The highest difficulty which computers had to face with, is the built-in ambiguity of Natural Languages. Formerly, a lot of human-devised rules have been used to disambiguate those ambiguities. Building such a complete rule-set is time-consuming and labor-intensive task whilst it doesn’t cover all the cases. Besides, when the scale of system increases, it is very difficult to control that rule-set. In this paper, we present a new model of learning-based MT (entitled BTL: Bitext-Transfer Learning) that learns from bilingual corpus to extract disambiguating rules. This model has been experimented in English-to-Vietnamese MT system (EVT) and it gave encouraging results.

2002

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Building a Training Corpus for Word Sense Disambiguation in English-to-Vietnamese Machine Translation
Dien Dinh
COLING-02: Machine Translation in Asia

2001

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An Approach to Parsing Vietnamese Noun Compounds
Dinh Dien | Hoang Kiem
Proceedings of the Seventh International Workshop on Parsing Technologies