2016
40.
Ittoo, Ashwin; Minh, Nguyen Le; Tojo, Satoshi
Preface KSE 2016 Conference
2016, (cited By 0).
@conference{Nguyen2016viii,
title = {Preface KSE 2016},
author = {Ashwin Ittoo and Nguyen Le Minh and Satoshi Tojo},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85006998383&doi=10.1109%2fKSE.2016.7758016&partnerID=40&md5=dbe8055ae432cb86c3e7c35d8204cb13},
doi = {10.1109/KSE.2016.7758016},
year = {2016},
date = {2016-01-01},
journal = {Proceedings - 2016 8th International Conference on Knowledge and Systems Engineering, KSE 2016},
pages = {viii},
note = {cited By 0},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
2015
39.
Nguyen, Hai-Long Trieu1 Phuong-Thai; Nguyen, Le-Minh
A New Feature to Improve Moore’s Sentence Alignment Method Journal Article
In: 2015.
BibTeX | Tags:
@article{nguyennew,
title = {A New Feature to Improve Moore’s Sentence Alignment Method},
author = {Hai-Long Trieu1 Phuong-Thai Nguyen and Le-Minh Nguyen},
year = {2015},
date = {2015-01-01},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
38.
Nguyen-Van, Hao; Nguyen, Dat; Nguyen, Thang; Nguyen, Minh; Pham-Nguyen, Loan
A Li-Ion battery charger with stable charging mode controller in noise environments Inproceedings
In: 2015 International Conference on Advanced Technologies for Communications (ATC), pp. 270–274, IEEE 2015.
BibTeX | Tags:
@inproceedings{nguyen2015li,
title = {A Li-Ion battery charger with stable charging mode controller in noise environments},
author = {Hao Nguyen-Van and Dat Nguyen and Thang Nguyen and Minh Nguyen and Loan Pham-Nguyen},
year = {2015},
date = {2015-01-01},
booktitle = {2015 International Conference on Advanced Technologies for Communications (ATC)},
pages = {270--274},
organization = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
37.
Duong, Duc Anh; Tojo, Satoshi; Nguyen, Minh Le; Le, Duy-Dinh; Ngo, Thanh Duc; Nguyen, Tuan Anh; Nguyen, Nam; Duong, Duc Minh; Lam, Vu; Nguyen, Khuong Dinh; others,
KSE 2015 Organizing Committee Journal Article
In: 2015.
BibTeX | Tags:
@article{duongkse,
title = {KSE 2015 Organizing Committee},
author = {Duc Anh Duong and Satoshi Tojo and Minh Le Nguyen and Duy-Dinh Le and Thanh Duc Ngo and Tuan Anh Nguyen and Nam Nguyen and Duc Minh Duong and Vu Lam and Khuong Dinh Nguyen and others},
year = {2015},
date = {2015-01-01},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
36.
Trieu, Hai Long; Dang, Thanh-Quyen; Nguyen, Phuong-Thai; Nuyen, Le-Minh
The JAIST-UET-MITI machine translation systems for IWSLT 2015 Inproceedings
In: Proceedings of the 12th International Workshop on Spoken Language Translation: Evaluation Campaign, 2015.
BibTeX | Tags:
@inproceedings{trieu2015jaist,
title = {The JAIST-UET-MITI machine translation systems for IWSLT 2015},
author = {Hai Long Trieu and Thanh-Quyen Dang and Phuong-Thai Nguyen and Le-Minh Nuyen},
year = {2015},
date = {2015-01-01},
booktitle = {Proceedings of the 12th International Workshop on Spoken Language Translation: Evaluation Campaign},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
35.
Tran, Quan Hung; Tran, Duc-Vu; Vu, Tu; Nguyen, Minh Le; Pham, Son Bao
JAIST: Combining multiple features for answer selection in community question answering Inproceedings
In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 215–219, 2015.
BibTeX | Tags:
@inproceedings{tran2015jaist,
title = {JAIST: Combining multiple features for answer selection in community question answering},
author = {Quan Hung Tran and Duc-Vu Tran and Tu Vu and Minh Le Nguyen and Son Bao Pham},
year = {2015},
date = {2015-01-01},
booktitle = {Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)},
pages = {215--219},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
34.
Do, Khac Phong; Nguyen, Ba Tung; Nguyen, Xuan Thanh; Bui, Quang Hung; Tran, Nguyen Le; Nguyen, Thi Nhat Thanh; Vuong, Van Quynh; Nguyen, Huy Lai; Le, Thanh Ha
Spatial interpolation and assimilation methods for satellite and ground meteorological data in Vietnam Journal Article
In: Journal of Information Processing Systems, vol. 11, no. 4, pp. 556–572, 2015.
BibTeX | Tags:
@article{do2015spatial,
title = {Spatial interpolation and assimilation methods for satellite and ground meteorological data in Vietnam},
author = {Khac Phong Do and Ba Tung Nguyen and Xuan Thanh Nguyen and Quang Hung Bui and Nguyen Le Tran and Thi Nhat Thanh Nguyen and Van Quynh Vuong and Huy Lai Nguyen and Thanh Ha Le},
year = {2015},
date = {2015-01-01},
journal = {Journal of Information Processing Systems},
volume = {11},
number = {4},
pages = {556--572},
publisher = {Korea Information Processing Society},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
33.
Trieu, Hai Long; Dang, Thanh Quyen; Nguyen, Phuong Thai; Nguyen, Le Minh
Phrase-based Machine Translation System Journal Article
In: 2015.
BibTeX | Tags:
@article{trieu2015phrase,
title = {Phrase-based Machine Translation System},
author = {Hai Long Trieu and Thanh Quyen Dang and Phuong Thai Nguyen and Le Minh Nguyen},
year = {2015},
date = {2015-01-01},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
32.
Trieu, Long Hai; Nguyen, Thai Phuong
A New Feature to Improve Moore's Sentence Alignment Method Journal Article
In: VNU Journal of Science: Computer Science and Communication Engineering, vol. 31, no. 1, 2015.
BibTeX | Tags:
@article{trieu2015new,
title = {A New Feature to Improve Moore's Sentence Alignment Method},
author = {Long Hai Trieu and Thai Phuong Nguyen},
year = {2015},
date = {2015-01-01},
journal = {VNU Journal of Science: Computer Science and Communication Engineering},
volume = {31},
number = {1},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
31.
Nguyen, Truong Son; Ho, Bao Quoc; Nguyen, Minh Le
Jaist: A two-phase machine learning approach for identifying discourse relations in newswire texts Inproceedings
In: Proceedings of the Nineteenth Conference on Computational Natural Language Learning-Shared Task, pp. 66–70, 2015.
BibTeX | Tags:
@inproceedings{nguyen2015jaist,
title = {Jaist: A two-phase machine learning approach for identifying discourse relations in newswire texts},
author = {Truong Son Nguyen and Bao Quoc Ho and Minh Le Nguyen},
year = {2015},
date = {2015-01-01},
booktitle = {Proceedings of the Nineteenth Conference on Computational Natural Language Learning-Shared Task},
pages = {66--70},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
30.
Pham, Quang Hong; Nguyen, Minh Le; Nguyen, Binh Thanh; Nguyen, Viet Cuong
Semi-supervised learning for Vietnamese named entity recognition using online conditional random fields Inproceedings
In: Proceedings of the Fifth Named Entity Workshop, pp. 50–55, 2015.
BibTeX | Tags:
@inproceedings{pham2015semi,
title = {Semi-supervised learning for Vietnamese named entity recognition using online conditional random fields},
author = {Quang Hong Pham and Minh Le Nguyen and Binh Thanh Nguyen and Viet Cuong Nguyen},
year = {2015},
date = {2015-01-01},
booktitle = {Proceedings of the Fifth Named Entity Workshop},
pages = {50--55},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
29.
Son, Nguyen Truong; Duyen, Nguyen Thi Phuong; Quoc, Ho Bao; Minh, Nguyen Le
Recognizing logical parts in Vietnamese legal texts using conditional random fields Conference
2015, (cited By 4).
Abstract | Links | BibTeX | Tags:
@conference{Son20151,
title = {Recognizing logical parts in Vietnamese legal texts using conditional random fields},
author = {Nguyen Truong Son and Nguyen Thi Phuong Duyen and Ho Bao Quoc and Nguyen Le Minh},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84925841423&doi=10.1109%2fRIVF.2015.7049865&partnerID=40&md5=02653a7e1875591d0ff0e1f57835bc5d},
doi = {10.1109/RIVF.2015.7049865},
year = {2015},
date = {2015-01-01},
journal = {Proceedings - 2015 IEEE RIVF International Conference on Computing and Communication Technologies: Research, Innovation, and Vision for Future, IEEE RIVF 2015},
pages = {1-6},
abstract = {Änalyzing the structure of legal sentences in legal document is an important phase to build a knowledge management system in Legal Engineering. This paper proposes a new approach to recognize logical parts in Vietnamese legal documents based on a statistic machine learning method - Conditional Random Fields. Beside linguistic features such as word features, part of speech features, we use semantic features of logical parts such as trigger features and ontology features to improve the result of the annotation system. Experiments were conducted in a Vietnamese Business Law data set and obtained 78.12% at precision and 68.72% at recall measure. Compare to state-of-the-art systems, it improves the result for recognizing some logical parts. © 2015 IEEE."},
note = {cited By 4},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Änalyzing the structure of legal sentences in legal document is an important phase to build a knowledge management system in Legal Engineering. This paper proposes a new approach to recognize logical parts in Vietnamese legal documents based on a statistic machine learning method - Conditional Random Fields. Beside linguistic features such as word features, part of speech features, we use semantic features of logical parts such as trigger features and ontology features to improve the result of the annotation system. Experiments were conducted in a Vietnamese Business Law data set and obtained 78.12% at precision and 68.72% at recall measure. Compare to state-of-the-art systems, it improves the result for recognizing some logical parts. © 2015 IEEE."
28.
Bach, Ngo Xuan; Hiraishi, Kunihiko; Minh, Nguyen Le; Shimazu, Akira
A joint model for vietnamese part-of-speech tagging using dual decomposition Journal Article
In: Smart Innovation, Systems and Technologies, vol. 30, pp. 353-367, 2015, (cited By 1).
Abstract | Links | BibTeX | Tags:
@article{Bach2015353,
title = {A joint model for vietnamese part-of-speech tagging using dual decomposition},
author = {Ngo Xuan Bach and Kunihiko Hiraishi and Nguyen Le Minh and Akira Shimazu},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84922061527&doi=10.1007%2f978-3-319-13545-8_20&partnerID=40&md5=80c44783c6c7e273fabcf159be34fdf6},
doi = {10.1007/978-3-319-13545-8_20},
year = {2015},
date = {2015-01-01},
journal = {Smart Innovation, Systems and Technologies},
volume = {30},
pages = {353-367},
abstract = {Part-of-speech (POS) tagging is a fundamental task of Natural Language Processing (NLP). It provides useful information for many other NLP tasks, including word sense disambiguation, text chunking, named entity recognition, syntactic parsing, semantic role labeling, and semantic parsing. Several methods have been proposed to deal with the POS tagging task in Vietnamese. They can be divided into two types of models: word-based models and syllable-based models. While a word-based model assigns a POS tag to each word, a syllable-based model assigns a POS tag to each syllable. This chapter presents a new model for Vietnamese POS tagging using dual decomposition. The chapter shows how dual decomposition can be exploited to integrate a word-based model and a syllable-based model to yield a more powerfulmodel for tagging Vietnamese sentences. Then the chapter describes experiments on the Viet Treebank corpus, a large annotated corpus for Vietnamese POS tagging. This chapter also presents an error analysis to investigate which types of words in Vietnamese are more difficult to tag than other words. Experimental results show that the word-based model and the syllable-based model are complementary. Moreover, the proposed model using dual decomposition outperforms both the word-based and the syllable-based models. © Springer International Publishing Switzerland 2015.},
note = {cited By 1},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Part-of-speech (POS) tagging is a fundamental task of Natural Language Processing (NLP). It provides useful information for many other NLP tasks, including word sense disambiguation, text chunking, named entity recognition, syntactic parsing, semantic role labeling, and semantic parsing. Several methods have been proposed to deal with the POS tagging task in Vietnamese. They can be divided into two types of models: word-based models and syllable-based models. While a word-based model assigns a POS tag to each word, a syllable-based model assigns a POS tag to each syllable. This chapter presents a new model for Vietnamese POS tagging using dual decomposition. The chapter shows how dual decomposition can be exploited to integrate a word-based model and a syllable-based model to yield a more powerfulmodel for tagging Vietnamese sentences. Then the chapter describes experiments on the Viet Treebank corpus, a large annotated corpus for Vietnamese POS tagging. This chapter also presents an error analysis to investigate which types of words in Vietnamese are more difficult to tag than other words. Experimental results show that the word-based model and the syllable-based model are complementary. Moreover, the proposed model using dual decomposition outperforms both the word-based and the syllable-based models. © Springer International Publishing Switzerland 2015.
27.
Bach, Ngo Xuan; Hiraishi, Kunihiko; Minh, Nguyen Le; Shimazu, Akira
A joint model for vietnamese part-of-speech tagging using dual decomposition Journal Article
In: Smart Innovation, Systems and Technologies, vol. 30, pp. 353-367, 2015, (cited By 1).
Abstract | Links | BibTeX | Tags:
@article{Bach2015353b,
title = {A joint model for vietnamese part-of-speech tagging using dual decomposition},
author = {Ngo Xuan Bach and Kunihiko Hiraishi and Nguyen Le Minh and Akira Shimazu},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84922061527&doi=10.1007%2f978-3-319-13545-8_20&partnerID=40&md5=80c44783c6c7e273fabcf159be34fdf6},
doi = {10.1007/978-3-319-13545-8_20},
year = {2015},
date = {2015-01-01},
journal = {Smart Innovation, Systems and Technologies},
volume = {30},
pages = {353-367},
abstract = {Part-of-speech (POS) tagging is a fundamental task of Natural Language Processing (NLP). It provides useful information for many other NLP tasks, including word sense disambiguation, text chunking, named entity recognition, syntactic parsing, semantic role labeling, and semantic parsing. Several methods have been proposed to deal with the POS tagging task in Vietnamese. They can be divided into two types of models: word-based models and syllable-based models. While a word-based model assigns a POS tag to each word, a syllable-based model assigns a POS tag to each syllable. This chapter presents a new model for Vietnamese POS tagging using dual decomposition. The chapter shows how dual decomposition can be exploited to integrate a word-based model and a syllable-based model to yield a more powerfulmodel for tagging Vietnamese sentences. Then the chapter describes experiments on the Viet Treebank corpus, a large annotated corpus for Vietnamese POS tagging. This chapter also presents an error analysis to investigate which types of words in Vietnamese are more difficult to tag than other words. Experimental results show that the word-based model and the syllable-based model are complementary. Moreover, the proposed model using dual decomposition outperforms both the word-based and the syllable-based models. © Springer International Publishing Switzerland 2015.},
note = {cited By 1},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Part-of-speech (POS) tagging is a fundamental task of Natural Language Processing (NLP). It provides useful information for many other NLP tasks, including word sense disambiguation, text chunking, named entity recognition, syntactic parsing, semantic role labeling, and semantic parsing. Several methods have been proposed to deal with the POS tagging task in Vietnamese. They can be divided into two types of models: word-based models and syllable-based models. While a word-based model assigns a POS tag to each word, a syllable-based model assigns a POS tag to each syllable. This chapter presents a new model for Vietnamese POS tagging using dual decomposition. The chapter shows how dual decomposition can be exploited to integrate a word-based model and a syllable-based model to yield a more powerfulmodel for tagging Vietnamese sentences. Then the chapter describes experiments on the Viet Treebank corpus, a large annotated corpus for Vietnamese POS tagging. This chapter also presents an error analysis to investigate which types of words in Vietnamese are more difficult to tag than other words. Experimental results show that the word-based model and the syllable-based model are complementary. Moreover, the proposed model using dual decomposition outperforms both the word-based and the syllable-based models. © Springer International Publishing Switzerland 2015.
26.
Tran, Quan Hung; Nguyen, Minh Le; Pham, Son Bao
Question analysis for a community-based vietnamese question answering system Journal Article
In: Ädvances in Intelligent Systems and Computing", vol. 326, pp. 641-651, 2015, (cited By 0).
Abstract | Links | BibTeX | Tags:
@article{Tran2015641,
title = {Question analysis for a community-based vietnamese question answering system},
author = {Quan Hung Tran and Minh Le Nguyen and Son Bao Pham},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84910672791&doi=10.1007%2f978-3-319-11680-8_51&partnerID=40&md5=f9d50edefec6d048be675bf756ff4e84},
doi = {10.1007/978-3-319-11680-8_51},
year = {2015},
date = {2015-01-01},
journal = {Ädvances in Intelligent Systems and Computing"},
volume = {326},
pages = {641-651},
abstract = {This paper describes the approach for analyzing questions in our community-based Vietnamese question answering system (VnCQAs), in which we focus on two subtasks: question classification and keyword identification. The question classification employs the machine learning approaches with a feature which represents a measure of similarity between two questions, while the keyword identification uses the dependency-tree-based features. Experimental results are promising, in which the question classification obtains the accuracy of 95.7% and the keyword identification gains the accuracy of 85.8%. Furthermore, these two subtasks help to improve the accuracy for finding the similar questions in our VnCQAs by 6.75%. © Springer International Publishing Switzerland 2015.},
note = {cited By 0},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This paper describes the approach for analyzing questions in our community-based Vietnamese question answering system (VnCQAs), in which we focus on two subtasks: question classification and keyword identification. The question classification employs the machine learning approaches with a feature which represents a measure of similarity between two questions, while the keyword identification uses the dependency-tree-based features. Experimental results are promising, in which the question classification obtains the accuracy of 95.7% and the keyword identification gains the accuracy of 85.8%. Furthermore, these two subtasks help to improve the accuracy for finding the similar questions in our VnCQAs by 6.75%. © Springer International Publishing Switzerland 2015.
25.
Tran, Quan Hung; Nguyen, Nien Dinh; Do, Kien Duc; Nguyen, Thinh Khanh; Tran, Dang Hai; Nguyen, Minh Le; Pham, Son Bao
Question analysis for a community-based vietnamese question answering system Journal Article
In: Ädvances in Intelligent Systems and Computing", vol. 326, pp. 641-651, 2015, (cited By 0).
Abstract | Links | BibTeX | Tags:
@article{Tran2015641b,
title = {Question analysis for a community-based vietnamese question answering system},
author = {Quan Hung Tran and Nien Dinh Nguyen and Kien Duc Do and Thinh Khanh Nguyen and Dang Hai Tran and Minh Le Nguyen and Son Bao Pham},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84910672791&doi=10.1007%2f978-3-319-11680-8_51&partnerID=40&md5=f9d50edefec6d048be675bf756ff4e84},
doi = {10.1007/978-3-319-11680-8_51},
year = {2015},
date = {2015-01-01},
journal = {Ädvances in Intelligent Systems and Computing"},
volume = {326},
pages = {641-651},
abstract = {This paper describes the approach for analyzing questions in our community-based Vietnamese question answering system (VnCQAs), in which we focus on two subtasks: question classification and keyword identification. The question classification employs the machine learning approaches with a feature which represents a measure of similarity between two questions, while the keyword identification uses the dependency-tree-based features. Experimental results are promising, in which the question classification obtains the accuracy of 95.7% and the keyword identification gains the accuracy of 85.8%. Furthermore, these two subtasks help to improve the accuracy for finding the similar questions in our VnCQAs by 6.75%. © Springer International Publishing Switzerland 2015.},
note = {cited By 0},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This paper describes the approach for analyzing questions in our community-based Vietnamese question answering system (VnCQAs), in which we focus on two subtasks: question classification and keyword identification. The question classification employs the machine learning approaches with a feature which represents a measure of similarity between two questions, while the keyword identification uses the dependency-tree-based features. Experimental results are promising, in which the question classification obtains the accuracy of 95.7% and the keyword identification gains the accuracy of 85.8%. Furthermore, these two subtasks help to improve the accuracy for finding the similar questions in our VnCQAs by 6.75%. © Springer International Publishing Switzerland 2015.
24.
Le, Tho Thi Ngoc; Shirai, Kiyoaki; Nguyen, Minh Le; Shimazu, Akira
Extracting indices from Japanese legal documents Journal Article
In: Ärtificial Intelligence and Law", vol. 23, no. 4, pp. 315-344, 2015, (cited By 6).
Abstract | Links | BibTeX | Tags:
@article{Le2015315,
title = {Extracting indices from Japanese legal documents},
author = {Tho Thi Ngoc Le and Kiyoaki Shirai and Minh Le Nguyen and Akira Shimazu},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84947040774&doi=10.1007%2fs10506-015-9168-8&partnerID=40&md5=8b65914ced61151cc49102f52144dd7a},
doi = {10.1007/s10506-015-9168-8},
year = {2015},
date = {2015-01-01},
journal = {Ärtificial Intelligence and Law"},
volume = {23},
number = {4},
pages = {315-344},
abstract = {This article addresses the problem of automatically extracting legal indices which express the important contents of legal documents. Legal indices are not limited to single-word keywords and compound-word (or phrase) keywords, they are also clause keywords. We approach index extraction using structural information of Japanese sentences, i.e. chunks and clauses. Based on the assumption that legal indices are composed of important tokens from the documents, extracting legal indices is treated as a problem of collecting chunks and clauses that contain as many important tokens as possible. Each token is assigned a weight which is a statistical score, e.g. TF–IDF and Okapi BM25, to indicate its importance. The importance of a chunk or clause is determined based on the average weight of tokens included in that chunk or clause. Then, highly weighted chunks and clauses are recognized as the indices for legal documents. The experimental results on Japanese National Pension Act data show that our proposed method achieves better performance (8.6 % higher on F1-score) than TextRank, the most popular unsupervised method in extracting single-word and compound-word keywords. In addition, this approach is also applicable to extract clause keywords with high performance. © 2015, Springer Science+Business Media Dordrecht.},
note = {cited By 6},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This article addresses the problem of automatically extracting legal indices which express the important contents of legal documents. Legal indices are not limited to single-word keywords and compound-word (or phrase) keywords, they are also clause keywords. We approach index extraction using structural information of Japanese sentences, i.e. chunks and clauses. Based on the assumption that legal indices are composed of important tokens from the documents, extracting legal indices is treated as a problem of collecting chunks and clauses that contain as many important tokens as possible. Each token is assigned a weight which is a statistical score, e.g. TF–IDF and Okapi BM25, to indicate its importance. The importance of a chunk or clause is determined based on the average weight of tokens included in that chunk or clause. Then, highly weighted chunks and clauses are recognized as the indices for legal documents. The experimental results on Japanese National Pension Act data show that our proposed method achieves better performance (8.6 % higher on F1-score) than TextRank, the most popular unsupervised method in extracting single-word and compound-word keywords. In addition, this approach is also applicable to extract clause keywords with high performance. © 2015, Springer Science+Business Media Dordrecht.
23.
Le, Tho Thi Ngoc; Nguyen, Minh Le; Shimazu, Akira
Generalizing hierarchical structure of indices for Japanese legal documents Conference
vol. 60, no. 1, 2015, (cited By 0).
Abstract | Links | BibTeX | Tags:
@conference{Le2015103,
title = {Generalizing hierarchical structure of indices for Japanese legal documents},
author = {Tho Thi Ngoc Le and Minh Le Nguyen and Akira Shimazu},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84941116519&doi=10.1016%2fj.procs.2015.08.109&partnerID=40&md5=e57f74323ca29dac0e9734cf8c3d122e},
doi = {10.1016/j.procs.2015.08.109},
year = {2015},
date = {2015-01-01},
journal = {Procedia Computer Science},
volume = {60},
number = {1},
pages = {103-112},
abstract = {The idea of hierarchical index is applied to the legal domain to provide the readers a general understanding of legal concepts via their super/sub-ordinate relations. This work serves as effort in automatic legal ontology learning in which super/sub-ordinate relations are considered. Indices are extracted from legal documents as keywords and their relationships are discovered by language processing method. We propose an approach to extract the super/subordinate relation between each pair of concepts individually based on directional similarity. The relations among a set of legal indices are represented in a directed graph and the hierarchical structure of indices is simply exported from this graph. We adopt this proposal to the Japanese National Pension Act document. The resulted hierarchical structure is compared to an annotated legal ontology on the number of correct relations. The proposed method achieves 40.6% for precision, 46.9% for recall and 43.5% for F-measure as the performance. © 2015 The Authors. Published by Elsevier B.V.},
note = {cited By 0},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
The idea of hierarchical index is applied to the legal domain to provide the readers a general understanding of legal concepts via their super/sub-ordinate relations. This work serves as effort in automatic legal ontology learning in which super/sub-ordinate relations are considered. Indices are extracted from legal documents as keywords and their relationships are discovered by language processing method. We propose an approach to extract the super/subordinate relation between each pair of concepts individually based on directional similarity. The relations among a set of legal indices are represented in a directed graph and the hierarchical structure of indices is simply exported from this graph. We adopt this proposal to the Japanese National Pension Act document. The resulted hierarchical structure is compared to an annotated legal ontology on the number of correct relations. The proposed method achieves 40.6% for precision, 46.9% for recall and 43.5% for F-measure as the performance. © 2015 The Authors. Published by Elsevier B.V.
22.
Jian, Liu Zhi; Tri, Nguyen Le Minh; Thai, Nguyen Le; Le, Phan Xuan
Switching-off angle control for switched reluctance motor using adaptive neural fuzzy inference system Journal Article
In: International Journal of Energy and Power Engineering, vol. 4, no. 1, pp. 39, 2015.
BibTeX | Tags:
@article{jian2015switching,
title = {Switching-off angle control for switched reluctance motor using adaptive neural fuzzy inference system},
author = {Liu Zhi Jian and Nguyen Le Minh Tri and Nguyen Le Thai and Phan Xuan Le},
year = {2015},
date = {2015-01-01},
journal = {International Journal of Energy and Power Engineering},
volume = {4},
number = {1},
pages = {39},
publisher = {Science Publishing Group},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2014
21.
Nguy^en, Minh
Niềm tự h`ao về" ng^oi nh`a" trăm tuổi Journal Article
In: 2014.
BibTeX | Tags:
@article{nguyen2014niềm,
title = {Niềm tự h`ao về" ng^oi nh`a" trăm tuổi},
author = {Minh Nguy^en},
year = {2014},
date = {2014-01-01},
publisher = {DJHQGHN},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
20.
Le, Trung; Tran, Dat; Ma, Wanli; Pham, Thien; Duong, Phuong; Nguyen, Minh
Robust support vector machine Inproceedings
In: 2014 International Joint Conference on Neural Networks (IJCNN), pp. 4137–4144, IEEE 2014.
BibTeX | Tags:
@inproceedings{le2014robust,
title = {Robust support vector machine},
author = {Trung Le and Dat Tran and Wanli Ma and Thien Pham and Phuong Duong and Minh Nguyen},
year = {2014},
date = {2014-01-01},
booktitle = {2014 International Joint Conference on Neural Networks (IJCNN)},
pages = {4137--4144},
organization = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
19.
Bach, Ngo Xuan; Minh, Nguyen Le; Shimazu, Akira
Exploiting discourse information to identify paraphrases Journal Article
In: Expert Systems with Applications, vol. 41, no. 6, pp. 2832-2841, 2014, (cited By 15).
Abstract | Links | BibTeX | Tags:
@article{Bach20142832,
title = {Exploiting discourse information to identify paraphrases},
author = {Ngo Xuan Bach and Nguyen Le Minh and Akira Shimazu},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84890557608&doi=10.1016%2fj.eswa.2013.10.018&partnerID=40&md5=a2f22c18cf4142e5bac599d6aff0a72b},
doi = {10.1016/j.eswa.2013.10.018},
year = {2014},
date = {2014-01-01},
journal = {Expert Systems with Applications},
volume = {41},
number = {6},
pages = {2832-2841},
abstract = {Previous work on paraphrase identification using sentence similarities has not exploited discourse structures, which have been shown as important information for paraphrase computation. In this paper, we propose a new method named EDU-based similarity, to compute the similarity between two sentences based on elementary discourse units. Unlike conventional methods, which directly compute similarities based on sentences, our method divides sentences into discourse units and employs them to compute similarities. We also show the relation between paraphrases and discourse units, which plays an important role in paraphrasing. We apply our method to the paraphrase identification task. Experimental results on the PAN corpus, a large corpus for detecting paraphrases, show the effectiveness of using discourse information for identifying paraphrases. We achieve 93.1% and 93.4% accuracy, respectively by using a single SVM classifier and by using a maximal voting model. © 2013 Elsevier Ltd. All rights reserved.},
note = {cited By 15},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Previous work on paraphrase identification using sentence similarities has not exploited discourse structures, which have been shown as important information for paraphrase computation. In this paper, we propose a new method named EDU-based similarity, to compute the similarity between two sentences based on elementary discourse units. Unlike conventional methods, which directly compute similarities based on sentences, our method divides sentences into discourse units and employs them to compute similarities. We also show the relation between paraphrases and discourse units, which plays an important role in paraphrasing. We apply our method to the paraphrase identification task. Experimental results on the PAN corpus, a large corpus for detecting paraphrases, show the effectiveness of using discourse information for identifying paraphrases. We achieve 93.1% and 93.4% accuracy, respectively by using a single SVM classifier and by using a maximal voting model. © 2013 Elsevier Ltd. All rights reserved.
18.
Nguyen, Minh Le; Shimazu, Akira
A semi supervised learning model for mapping sentences to logical forms with ambiguous supervision Journal Article
In: Data and Knowledge Engineering, vol. 90, pp. 1-12, 2014, (cited By 0).
Abstract | Links | BibTeX | Tags:
@article{LeNguyen20141,
title = {A semi supervised learning model for mapping sentences to logical forms with ambiguous supervision},
author = {Minh Le Nguyen and Akira Shimazu},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84897982015&doi=10.1016%2fj.datak.2013.12.001&partnerID=40&md5=696e4f4e83712ac44326edb6eb6e9ccc},
doi = {10.1016/j.datak.2013.12.001},
year = {2014},
date = {2014-01-01},
journal = {Data and Knowledge Engineering},
volume = {90},
pages = {1-12},
abstract = {Semantic parsing is the task of mapping a sentence in natural language to a meaning representation. The limitation of previous work on supervised semantic parsing is that it is very difficult to obtain annotated training data in which a sentence is paired with a semantic representation. To deal with this problem, we introduce a semi supervised learning model for semantic parsing with ambiguous supervision. The main idea of our method is to utilize a large amount of data, to enrich feature space with the maximum entropy model using our semantic learner. We evaluate the proposed models on standard corpora to demonstrate that our methods are suitable for semantic parsing. Experimental results show that the proposed methods work efficiently and well on ambiguous data and it is comparable to the state of the art methods. © 2014 Elsevier B.V.},
note = {cited By 0},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Semantic parsing is the task of mapping a sentence in natural language to a meaning representation. The limitation of previous work on supervised semantic parsing is that it is very difficult to obtain annotated training data in which a sentence is paired with a semantic representation. To deal with this problem, we introduce a semi supervised learning model for semantic parsing with ambiguous supervision. The main idea of our method is to utilize a large amount of data, to enrich feature space with the maximum entropy model using our semantic learner. We evaluate the proposed models on standard corpora to demonstrate that our methods are suitable for semantic parsing. Experimental results show that the proposed methods work efficiently and well on ambiguous data and it is comparable to the state of the art methods. © 2014 Elsevier B.V.
17.
Shimazu, Akira; Nguyen, Minh Le
Legal Engineering and its natural language processing Journal Article
In: Ädvances in Intelligent Systems and Computing", vol. 244 VOLUME 1, pp. 7, 2014, (cited By 1).
Abstract | Links | BibTeX | Tags:
@article{Shimazu20147,
title = {Legal Engineering and its natural language processing},
author = {Akira Shimazu and Minh Le Nguyen},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84894756314&doi=10.1007%2f978-3-319-02741-8_3&partnerID=40&md5=e4a4c3b3d32d3417852533962293f285},
doi = {10.1007/978-3-319-02741-8_3},
year = {2014},
date = {2014-01-01},
journal = {Ädvances in Intelligent Systems and Computing"},
volume = {244 VOLUME 1},
pages = {7},
abstract = {Öur society is regulated by a lot of laws which are related mutually. When we view a society as a system, laws can be viewed as the specifications for the society. Such a system-oriented aspect of laws have not been studied well so far. In the upcoming e-Society, laws have more important roles in order to achieve a trustworthy society and we expect a methodology which treats a system-oriented aspect of laws. Legal Engineering is the new field that studies the methodology and applies information science, software engineering and artificial intelligence to laws in order to support legislation and to implement laws using computers. So far, as studies on Legal Engineering, Shimazu group of JAIST proposed the logical structure model of law paragraphs, the coreference model of law texts, the editing model of law texts and so on, and implemented their models. Tojo group of JAIST verified whether several related ordinances of Toyama prefecture in Japan contains contradictions or not. Ochimizu group of JAIST studied the model for designing a law-implementation system and proposed the accountability model for the lawimplementation system. Futatsugi group of JAIST proposed the formal description and the verification method of legal domains. As laws are written in natural language, natural language processing is essential for Legal Engineering. In this talk, after the aim, the approach and the problems of Legal Engineering are introduced, studies on natural language processing for Legal Engineering are introduced. © Springer International Publishing Switzerland 2014."},
note = {cited By 1},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Öur society is regulated by a lot of laws which are related mutually. When we view a society as a system, laws can be viewed as the specifications for the society. Such a system-oriented aspect of laws have not been studied well so far. In the upcoming e-Society, laws have more important roles in order to achieve a trustworthy society and we expect a methodology which treats a system-oriented aspect of laws. Legal Engineering is the new field that studies the methodology and applies information science, software engineering and artificial intelligence to laws in order to support legislation and to implement laws using computers. So far, as studies on Legal Engineering, Shimazu group of JAIST proposed the logical structure model of law paragraphs, the coreference model of law texts, the editing model of law texts and so on, and implemented their models. Tojo group of JAIST verified whether several related ordinances of Toyama prefecture in Japan contains contradictions or not. Ochimizu group of JAIST studied the model for designing a law-implementation system and proposed the accountability model for the lawimplementation system. Futatsugi group of JAIST proposed the formal description and the verification method of legal domains. As laws are written in natural language, natural language processing is essential for Legal Engineering. In this talk, after the aim, the approach and the problems of Legal Engineering are introduced, studies on natural language processing for Legal Engineering are introduced. © Springer International Publishing Switzerland 2014."
16.
Tran, Oanh Thi; Ngo, Bach Xuan; Nguyen, Minh Le; Shimazu, Akira
Reference resolution in Japanese legal texts at passage levels Journal Article
In: Ädvances in Intelligent Systems and Computing", vol. 245, pp. 237-249, 2014, (cited By 2).
Abstract | Links | BibTeX | Tags:
@article{Tran2014237,
title = {Reference resolution in Japanese legal texts at passage levels},
author = {Oanh Thi Tran and Bach Xuan Ngo and Minh Le Nguyen and Akira Shimazu},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84927510483&doi=10.1007%2f978-3-319-02821-7_22&partnerID=40&md5=61e12118e686ae9c7c060e258a5d37ea},
doi = {10.1007/978-3-319-02821-7_22},
year = {2014},
date = {2014-01-01},
journal = {Ädvances in Intelligent Systems and Computing"},
volume = {245},
pages = {237-249},
abstract = {Sentences in the domain of legal texts are usually long and complicated. At the discourse level, they contains lots of reference phenomena which make the understanding of laws become more difficult. This paper investigates the task of reference resolution in the legal domain. The aim is to create a system which can automatically extracts referents for references in a real time. This is a new interesting task in the research of Legal Engineering. It does not only help readers in comprehending the law, support law makers in developing and amending laws, but also support in building an information system which works based on laws, etc. The main issues are to detect references and then resolve them to their referents. To detect references, we use a powerfulmachine learning technique rather than rule-based approaches as used in previous works. In resolving them, we design regular expressions to catch up the position of referents. We also build a corpus using Japanese National Pension Law to train and test our model. Our final system achieved 91.6% in the F1 score in detecting references, 96.18% accuracy in resolving them, and 88.5% in the F1 score in the end-to-end system. © Springer International Publishing Switzerland 2014.},
note = {cited By 2},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Sentences in the domain of legal texts are usually long and complicated. At the discourse level, they contains lots of reference phenomena which make the understanding of laws become more difficult. This paper investigates the task of reference resolution in the legal domain. The aim is to create a system which can automatically extracts referents for references in a real time. This is a new interesting task in the research of Legal Engineering. It does not only help readers in comprehending the law, support law makers in developing and amending laws, but also support in building an information system which works based on laws, etc. The main issues are to detect references and then resolve them to their referents. To detect references, we use a powerfulmachine learning technique rather than rule-based approaches as used in previous works. In resolving them, we design regular expressions to catch up the position of referents. We also build a corpus using Japanese National Pension Law to train and test our model. Our final system achieved 91.6% in the F1 score in detecting references, 96.18% accuracy in resolving them, and 88.5% in the F1 score in the end-to-end system. © Springer International Publishing Switzerland 2014.
15.
Tran, Oanh Thi; Ngo, Bach Xuan; Nguyen, Minh Le; Shimazu, Akira
Answering legal questions by mining reference information Journal Article
In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8417, pp. 214-229, 2014, (cited By 5).
Abstract | Links | BibTeX | Tags:
@article{Tran2014214,
title = {Answering legal questions by mining reference information},
author = {Oanh Thi Tran and Bach Xuan Ngo and Minh Le Nguyen and Akira Shimazu},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84921647194&doi=10.1007%2f978-3-319-10061-6_15&partnerID=40&md5=e6cd696b44e3e8253dee2399da3b3198},
doi = {10.1007/978-3-319-10061-6_15},
year = {2014},
date = {2014-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {8417},
pages = {214-229},
abstract = {This paper presents a study on exploiting reference information to build a question answering system restricted to the legal domain. Most previous research focuses on answering legal questions whose answers can be found in one document (The term ‘documents’ corresponds to articles, paragraphs, items, or sub-items according to the naming rules used in the legal domain.) without using reference information. However, there are many legal questions whose answers could not be found without linking information from multiple documents. This connection is represented by explicit or implicit references. To the best of our knowledge, this type of questions is not adequately considered in previous work. To cope with them, we propose a novel approach which allow us to exploit the reference information among legal documents to find answers. This approach also uses requisite-effectuation structures of legal sentences and some effective similarity measures to support finding correct answers without training data. The experimental results showed that the proposed method is quite effective and outperform a traditional QA method, which does not use reference information. © Springer International Publishing Switzerland 2014.},
note = {cited By 5},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This paper presents a study on exploiting reference information to build a question answering system restricted to the legal domain. Most previous research focuses on answering legal questions whose answers can be found in one document (The term ‘documents’ corresponds to articles, paragraphs, items, or sub-items according to the naming rules used in the legal domain.) without using reference information. However, there are many legal questions whose answers could not be found without linking information from multiple documents. This connection is represented by explicit or implicit references. To the best of our knowledge, this type of questions is not adequately considered in previous work. To cope with them, we propose a novel approach which allow us to exploit the reference information among legal documents to find answers. This approach also uses requisite-effectuation structures of legal sentences and some effective similarity measures to support finding correct answers without training data. The experimental results showed that the proposed method is quite effective and outperform a traditional QA method, which does not use reference information. © Springer International Publishing Switzerland 2014.
14.
Tran, Oanh Thi; Ngo, Bach Xuan; Nguyen, Minh Le; Shimazu, Akira
Automated reference resolution in legal texts Journal Article
In: Ärtificial Intelligence and Law", vol. 22, no. 1, pp. 29-60, 2014, (cited By 17).
Abstract | Links | BibTeX | Tags:
@article{Tran201429,
title = {Automated reference resolution in legal texts},
author = {Oanh Thi Tran and Bach Xuan Ngo and Minh Le Nguyen and Akira Shimazu},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84894653551&doi=10.1007%2fs10506-013-9149-8&partnerID=40&md5=e5c0de7788d86478e6cadfec1faa67b8},
doi = {10.1007/s10506-013-9149-8},
year = {2014},
date = {2014-01-01},
journal = {Ärtificial Intelligence and Law"},
volume = {22},
number = {1},
pages = {29-60},
abstract = {This paper investigates the task of reference resolution in the legal domain. This is a new interesting task in Legal Engineering research. The goal is to create a system which can automatically detect references and then extracts their referents. Previous work limits itself to detect and resolve references at the document targets. In this paper, we go a step further in trying to resolve references to sub-document targets. Referents extracted are the smallest fragments of texts in documents, rather than the entire documents that contain the referenced texts. Based on analyzing the characteristics of reference phenomena in legal texts, we propose a four-step framework to deal with the task: mention detection, contextual information extraction, antecedent candidate extraction, and antecedent determination. We also show how machine learning methods can be exploited in each step. The final system achieves 80.06 % in the F1 score for detecting references, 85.61 % accuracy for resolving them, and 67.02 % in the F1 score for the end-to-end setting task on the Japanese National Pension Law corpus. © 2013 Springer Science+Business Media Dordrecht.},
note = {cited By 17},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This paper investigates the task of reference resolution in the legal domain. This is a new interesting task in Legal Engineering research. The goal is to create a system which can automatically detect references and then extracts their referents. Previous work limits itself to detect and resolve references at the document targets. In this paper, we go a step further in trying to resolve references to sub-document targets. Referents extracted are the smallest fragments of texts in documents, rather than the entire documents that contain the referenced texts. Based on analyzing the characteristics of reference phenomena in legal texts, we propose a four-step framework to deal with the task: mention detection, contextual information extraction, antecedent candidate extraction, and antecedent determination. We also show how machine learning methods can be exploited in each step. The final system achieves 80.06 % in the F1 score for detecting references, 85.61 % accuracy for resolving them, and 67.02 % in the F1 score for the end-to-end setting task on the Japanese National Pension Law corpus. © 2013 Springer Science+Business Media Dordrecht.
13.
Nguyen, Dat Quoc; Nguyen, Dai Quoc; Pham, Son Bao; Nguyen, Phuong-Thai; Nguyen, Minh Le
From treebank conversion to automatic dependency parsing for Vietnamese Journal Article
In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8455 LNCS, pp. 196-207, 2014, (cited By 25).
Abstract | Links | BibTeX | Tags:
@article{Nguyen2014196,
title = {From treebank conversion to automatic dependency parsing for Vietnamese},
author = {Dat Quoc Nguyen and Dai Quoc Nguyen and Son Bao Pham and Phuong-Thai Nguyen and Minh Le Nguyen},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84958547080&doi=10.1007%2f978-3-319-07983-7_26&partnerID=40&md5=ddecf30b9dc41844e0f6a71b12357fcc},
doi = {10.1007/978-3-319-07983-7_26},
year = {2014},
date = {2014-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {8455 LNCS},
pages = {196-207},
abstract = {This paper presents a new conversion method to automatically transform a constituent-based Vietnamese Treebank into dependency trees. On a dependency Treebank created according to our new approach, we examine two state-of-the-art dependency parsers: the MSTParser and the MaltParser. Experiments show that the MSTParser outperforms the MaltParser. To the best of our knowledge, we report the highest performances published to date in the task of dependency parsing for Vietnamese. Particularly, on gold standard POS tags, we get an unlabeled attachment score of 79.08% and a labeled attachment score of 71.66%. © Springer International Publishing Switzerland 2014.},
note = {cited By 25},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This paper presents a new conversion method to automatically transform a constituent-based Vietnamese Treebank into dependency trees. On a dependency Treebank created according to our new approach, we examine two state-of-the-art dependency parsers: the MSTParser and the MaltParser. Experiments show that the MSTParser outperforms the MaltParser. To the best of our knowledge, we report the highest performances published to date in the task of dependency parsing for Vietnamese. Particularly, on gold standard POS tags, we get an unlabeled attachment score of 79.08% and a labeled attachment score of 71.66%. © Springer International Publishing Switzerland 2014.
12.
Tran, Oanh Thi; Nguyen, Minh Le; Shimazu, Akira
Automated reference resolution in legal texts Journal Article
In: Ärtificial Intelligence and Law", vol. 22, no. 1, pp. 29-60, 2014, (cited By 17).
Abstract | Links | BibTeX | Tags:
@article{Tran201429b,
title = {Automated reference resolution in legal texts},
author = {Oanh Thi Tran and Minh Le Nguyen and Akira Shimazu},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84894653551&doi=10.1007%2fs10506-013-9149-8&partnerID=40&md5=e5c0de7788d86478e6cadfec1faa67b8},
doi = {10.1007/s10506-013-9149-8},
year = {2014},
date = {2014-01-01},
journal = {Ärtificial Intelligence and Law"},
volume = {22},
number = {1},
pages = {29-60},
abstract = {This paper investigates the task of reference resolution in the legal domain. This is a new interesting task in Legal Engineering research. The goal is to create a system which can automatically detect references and then extracts their referents. Previous work limits itself to detect and resolve references at the document targets. In this paper, we go a step further in trying to resolve references to sub-document targets. Referents extracted are the smallest fragments of texts in documents, rather than the entire documents that contain the referenced texts. Based on analyzing the characteristics of reference phenomena in legal texts, we propose a four-step framework to deal with the task: mention detection, contextual information extraction, antecedent candidate extraction, and antecedent determination. We also show how machine learning methods can be exploited in each step. The final system achieves 80.06 % in the F1 score for detecting references, 85.61 % accuracy for resolving them, and 67.02 % in the F1 score for the end-to-end setting task on the Japanese National Pension Law corpus. © 2013 Springer Science+Business Media Dordrecht.},
note = {cited By 17},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This paper investigates the task of reference resolution in the legal domain. This is a new interesting task in Legal Engineering research. The goal is to create a system which can automatically detect references and then extracts their referents. Previous work limits itself to detect and resolve references at the document targets. In this paper, we go a step further in trying to resolve references to sub-document targets. Referents extracted are the smallest fragments of texts in documents, rather than the entire documents that contain the referenced texts. Based on analyzing the characteristics of reference phenomena in legal texts, we propose a four-step framework to deal with the task: mention detection, contextual information extraction, antecedent candidate extraction, and antecedent determination. We also show how machine learning methods can be exploited in each step. The final system achieves 80.06 % in the F1 score for detecting references, 85.61 % accuracy for resolving them, and 67.02 % in the F1 score for the end-to-end setting task on the Japanese National Pension Law corpus. © 2013 Springer Science+Business Media Dordrecht.
11.
Thanh, Tran Thi My; Minh, Nguyen Le; Vung, Vi Van; Irikura, Kojiro
Values for peak ground acceleration and peak ground velocity using in seismic hazard assessment for Song Tranh 2 hydropower region Journal Article
In: Vietnam Journal of Earth Sciences, vol. 36, no. 4, pp. 462–469, 2014.
BibTeX | Tags:
@article{thanh2014values,
title = {Values for peak ground acceleration and peak ground velocity using in seismic hazard assessment for Song Tranh 2 hydropower region},
author = {Tran Thi My Thanh and Nguyen Le Minh and Vi Van Vung and Kojiro Irikura},
year = {2014},
date = {2014-01-01},
journal = {Vietnam Journal of Earth Sciences},
volume = {36},
number = {4},
pages = {462--469},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2013
10.
NGUYEN, MINH LE; PHAM, MINH; SHIMAZU, AKIRA
Learning approaches for recognizing textual entailment and finding contradiction in texts Journal Article
In: 2013.
BibTeX | Tags:
@article{nguyen2013learning,
title = {Learning approaches for recognizing textual entailment and finding contradiction in texts},
author = {MINH LE NGUYEN and MINH PHAM and AKIRA SHIMAZU},
year = {2013},
date = {2013-01-01},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
9.
Pham, Minh Quang Nhat; Nguyen, Minh Le; Shimazu, Akira
JAIST Participation at NTCIR-10 RITE-2. Journal Article
In: 2013.
BibTeX | Tags:
@article{pham2013jaist,
title = {JAIST Participation at NTCIR-10 RITE-2.},
author = {Minh Quang Nhat Pham and Minh Le Nguyen and Akira Shimazu},
year = {2013},
date = {2013-01-01},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
8.
Hung, Bui Thanh; Minh, Nguyen Le; Shimazu, Akira
Translating legal sentence by segmentation and rule selection Journal Article
In: International Journal on Natural Language Computing, pp. 35–54, 2013.
BibTeX | Tags:
@article{hung2013translating,
title = {Translating legal sentence by segmentation and rule selection},
author = {Bui Thanh Hung and Nguyen Le Minh and Akira Shimazu},
year = {2013},
date = {2013-01-01},
journal = {International Journal on Natural Language Computing},
pages = {35--54},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
7.
Huy, Hien Vu; Nguyen, Phuong-Thai; Nguyen, Tung-Lam; Nguyen, Minh Le
Bootstrapping phrase-based statistical machine translation via wsd integration Inproceedings
In: Proceedings of the Sixth International Joint Conference on Natural Language Processing, pp. 1042–1046, 2013.
BibTeX | Tags:
@inproceedings{huy2013bootstrapping,
title = {Bootstrapping phrase-based statistical machine translation via wsd integration},
author = {Hien Vu Huy and Phuong-Thai Nguyen and Tung-Lam Nguyen and Minh Le Nguyen},
year = {2013},
date = {2013-01-01},
booktitle = {Proceedings of the Sixth International Joint Conference on Natural Language Processing},
pages = {1042--1046},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
6.
Bach, Ngo Xuan; Minh, Nguyen Le; Shimazu, Akira
EDU-Based similarity for paraphrase identification Journal Article
In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7934 LNCS, pp. 65-76, 2013, (cited By 2).
Abstract | Links | BibTeX | Tags:
@article{Bach201365,
title = {EDU-Based similarity for paraphrase identification},
author = {Ngo Xuan Bach and Nguyen Le Minh and Akira Shimazu},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84884941737&doi=10.1007%2f978-3-642-38824-8_6&partnerID=40&md5=385a73bfcfb7e4a4e6e9d8d86021e959},
doi = {10.1007/978-3-642-38824-8_6},
year = {2013},
date = {2013-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {7934 LNCS},
pages = {65-76},
abstract = {We propose a new method to compute the similarity between two sentences based on elementary discourse units, EDU-based similarity. Unlike conventional methods, which directly compute similarities based on sentences, our method divides sentences into discourse units and uses them to compute similarities. We also show the relation between paraphrases and discourse units, which plays an important role in paraphrasing. We apply our method to the paraphrase identification task. By using only a single SVM classifier, we achieve 93.1% accuracy on the PAN corpus, a large corpus for detecting paraphrases. © 2013 Springer-Verlag Berlin Heidelberg.},
note = {cited By 2},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
We propose a new method to compute the similarity between two sentences based on elementary discourse units, EDU-based similarity. Unlike conventional methods, which directly compute similarities based on sentences, our method divides sentences into discourse units and uses them to compute similarities. We also show the relation between paraphrases and discourse units, which plays an important role in paraphrasing. We apply our method to the paraphrase identification task. By using only a single SVM classifier, we achieve 93.1% accuracy on the PAN corpus, a large corpus for detecting paraphrases. © 2013 Springer-Verlag Berlin Heidelberg.
5.
Bach, Ngo Xuan; Minh, Nguyen Le; Oanh, Tran Thi; Shimazu, Akira
A two-phase framework for learning logical structures of paragraphs in legal articles Journal Article
In: ÄCM Transactions on Asian Language Information Processing", vol. 12, no. 1, 2013, (cited By 16).
Abstract | Links | BibTeX | Tags:
@article{Bach2013,
title = {A two-phase framework for learning logical structures of paragraphs in legal articles},
author = {Ngo Xuan Bach and Nguyen Le Minh and Tran Thi Oanh and Akira Shimazu},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84874822251&doi=10.1145%2f2425327.2425330&partnerID=40&md5=b908add0cc750243c6b24be89ac64571},
doi = {10.1145/2425327.2425330},
year = {2013},
date = {2013-01-01},
journal = {ÄCM Transactions on Asian Language Information Processing"},
volume = {12},
number = {1},
abstract = {Änalyzing logical structures of texts is important to understanding natural language, especially in the legal domain, where legal texts have their own specific characteristics. Recognizing logical structures in legal texts does not only help people in understanding legal documents, but also in supporting other tasks in legal text processing. In this article, we present a new task, learning logical structures of paragraphs in legal articles, which is studied in research on Legal Engineering. The goals of this task are recognizing logical parts of law sentences in a paragraph, and then grouping related logical parts into some logical structures of formulas, which describe logical relations between logical parts. We present a two-phase framework to learn logical structures of paragraphs in legal articles. In the first phase, we model the problem of recognizing logical parts in law sentences as a multi-layer sequence learning problem, and present a CRF-based model to recognize them. In the second phase, we propose a graph-based method to group logical parts into logical structures. We consider the problem of finding a subset of complete subgraphs in a weighted-edge complete graph, where each node corresponds to a logical part, and a complete subgraph corresponds to a logical structure. We also present an integer linear programming formulation for this optimization problem. Our models achieve 74.37% in recognizing logical parts, 80.08% in recognizing logical structures, and 58.36% in the whole task on the Japanese National Pension Law corpus. Our work provides promising results for further research on this interesting task. Copyright © 2013 ACM."},
note = {cited By 16},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Änalyzing logical structures of texts is important to understanding natural language, especially in the legal domain, where legal texts have their own specific characteristics. Recognizing logical structures in legal texts does not only help people in understanding legal documents, but also in supporting other tasks in legal text processing. In this article, we present a new task, learning logical structures of paragraphs in legal articles, which is studied in research on Legal Engineering. The goals of this task are recognizing logical parts of law sentences in a paragraph, and then grouping related logical parts into some logical structures of formulas, which describe logical relations between logical parts. We present a two-phase framework to learn logical structures of paragraphs in legal articles. In the first phase, we model the problem of recognizing logical parts in law sentences as a multi-layer sequence learning problem, and present a CRF-based model to recognize them. In the second phase, we propose a graph-based method to group logical parts into logical structures. We consider the problem of finding a subset of complete subgraphs in a weighted-edge complete graph, where each node corresponds to a logical part, and a complete subgraph corresponds to a logical structure. We also present an integer linear programming formulation for this optimization problem. Our models achieve 74.37% in recognizing logical parts, 80.08% in recognizing logical structures, and 58.36% in the whole task on the Japanese National Pension Law corpus. Our work provides promising results for further research on this interesting task. Copyright © 2013 ACM."
4.
Pham, Minh Quang Nhat; Nguyen, Minh Le; Shimazu, Akira
Using shallow semantic parsing and relation extraction for finding contradiction in text Journal Article
In: 2013.
BibTeX | Tags:
@article{pham2013using,
title = {Using shallow semantic parsing and relation extraction for finding contradiction in text},
author = {Minh Quang Nhat Pham and Minh Le Nguyen and Akira Shimazu},
year = {2013},
date = {2013-01-01},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
3.
Le, Tho Thi Ngoc; Nguyen, Minh Le; Shimazu, Akira
Unsupervised keyword extraction for japanese legal documents Journal Article
In: Frontiers in Artificial Intelligence and Applications, vol. 259, pp. 97-106, 2013, (cited By 6).
Abstract | Links | BibTeX | Tags:
@article{Le201397,
title = {Unsupervised keyword extraction for japanese legal documents},
author = {Tho Thi Ngoc Le and Minh Le Nguyen and Akira Shimazu},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84894577961&doi=10.3233%2f978-1-61499-359-9-97&partnerID=40&md5=ed3332b3003f147efb7b99e041d1656c},
doi = {10.3233/978-1-61499-359-9-97},
year = {2013},
date = {2013-01-01},
journal = {Frontiers in Artificial Intelligence and Applications},
volume = {259},
pages = {97-106},
abstract = {This study proposes a novel unsupervised approach for extracting keywords from Japanese legal documents by applying knowledge of Japanese syntax. Japanese keywords usually occur in chunks; the task of extracting Japanese keywords is treated as a matter of finding chunks that yield documents' important content. To find these chunks, all chunks in a given document are assigned weights to indicate their importance. Highly weighted chunks are recognized as candidate keywords, which are post-processed to obtain keywords. Although the proposed method employs simple techniques, the experimental results on Japanese legal documents show that the proposed chunk-based approach achieves better performance (10.5% higher on F1-score) than the graph-based ranking approach, the most popular unsupervised method. © 2013 The authors and IOS Press.},
note = {cited By 6},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This study proposes a novel unsupervised approach for extracting keywords from Japanese legal documents by applying knowledge of Japanese syntax. Japanese keywords usually occur in chunks; the task of extracting Japanese keywords is treated as a matter of finding chunks that yield documents' important content. To find these chunks, all chunks in a given document are assigned weights to indicate their importance. Highly weighted chunks are recognized as candidate keywords, which are post-processed to obtain keywords. Although the proposed method employs simple techniques, the experimental results on Japanese legal documents show that the proposed chunk-based approach achieves better performance (10.5% higher on F1-score) than the graph-based ranking approach, the most popular unsupervised method. © 2013 The authors and IOS Press.
2.
Tran, Dang Hai; Chu, Cuong Xuan; Pham, Son Bao; Nguyen, Minh Le
Learning based approaches for vietnamese question classification using keywords extraction from the web Inproceedings
In: Proceedings of the Sixth International Joint Conference on Natural Language Processing, pp. 740–746, 2013.
BibTeX | Tags:
@inproceedings{tran2013learning,
title = {Learning based approaches for vietnamese question classification using keywords extraction from the web},
author = {Dang Hai Tran and Cuong Xuan Chu and Son Bao Pham and Minh Le Nguyen},
year = {2013},
date = {2013-01-01},
booktitle = {Proceedings of the Sixth International Joint Conference on Natural Language Processing},
pages = {740--746},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
1.
Go, Vivian F; Minh, Nguyen Le; Frangakis, Constantine; Ha, Tran Viet; Latkin, Carl A; Sripaipan, Teerada; Davis, Wendy; Zelaya, Carla; Ngoc, Nguyen Phuong; Quan, Vu Minh
Decreased injecting is associated with increased alcohol consumption among injecting drug users in northern Vietnam Journal Article
In: International Journal of Drug Policy, vol. 24, no. 4, pp. 304–311, 2013.
BibTeX | Tags:
@article{go2013decreased,
title = {Decreased injecting is associated with increased alcohol consumption among injecting drug users in northern Vietnam},
author = {Vivian F Go and Nguyen Le Minh and Constantine Frangakis and Tran Viet Ha and Carl A Latkin and Teerada Sripaipan and Wendy Davis and Carla Zelaya and Nguyen Phuong Ngoc and Vu Minh Quan},
year = {2013},
date = {2013-01-01},
journal = {International Journal of Drug Policy},
volume = {24},
number = {4},
pages = {304--311},
publisher = {Elsevier},
keywords = {},
pubstate = {published},
tppubtype = {article}
}