Phân cụm từ tiếng việt và nhận diện từ trái nghĩa

Nguyễn Phương Thái Năm bảo vệ: 2013 Abstract: Automatically constructing and clustering of words similarity have many important applications in Natural Language Processing NLP tasks,

Phân cụm từ Tiếng Việt và nhận diện từ trái nghĩa

Nguyễn Kim Anh

Trường Đại học Công nghệ Ngành: Khoa học máy tính; Mã số: 60 48 01 Người hướng dẫn: TS Nguyễn Phương Thái

Năm bảo vệ: 2013

Abstract: Automatically constructing and clustering of words similarity have

many important applications in Natural Language Processing (NLP) tasks, such as dictionary construction, statistical machine translation, named-entity recognition, functional labeling, word segmentation… In recent years, it is a common trend that word clustering is researched in some languages as English, Germany, Chinese… However, the task of word clustering in Vietnamese is a more recent one In this thesis, I use a large unlabeled data of Vietnamese of about 15 millions words which is equivalent to approximately 700 thousands of sentences This unlabeled data is extracted from newspapers: Lao dong, PC World, Tuoi tre and then part-of-speech tagged I investigated some approaches for constructing word clusters in Vietnamese, in which I mainly focus on two main methods by Brown and Dekang Lin I use the same Vietnamese corpus and the same evaluating tool for these two methods so that I can compare and evaluate the effects of those methods in certain NLP tasks Besides, I use the statistics method to suggest 20 frames of antonym which can be used to identify antonym classes in clusters

Keywords: Khoa học máy tính; Xử lý ngôn ngữ tự nhiên; Cụm từ; Từ trái nghĩa

Table of Contents

Acknowledgements 4

Abstract 5

Chapter I - Introduction 10

1.1 Word Similarity 11

1.2 Hierarchical Clustering of Word 11

1.3 Function tags 12

1.4 Objectives of the Thesis 13

1.5 Our Contributions 13

1.6 Thesis structure 14

Chapter II - Related Works 15

2.1 Word Clustering 15

2.1.1 The Brown algorithm 15

2.1.2 Sticky Pairs and Sematic Classes 17

2.2 Word Similarity 18

2.2.1 Approach 18

2.2.2 Grammar Relationships 19

2.2.3 Results 20

2.3 Clustering By Committee 20

2.3.1 Motivation 21

2.3.2 Algorithm 21

2.3.3 Results 23

Chapter III - Our approach 25

3.1 Word clustering in Vietnamese 25

3.1.1 Brown's algorithm 25

3.1.2 Word similarity 26

3.2 Evaluating Methodology 28

3.3 Antonym classes 31

3.3.1 Ancillary antonym 31

3.3.2 Coordinated antonym 32

3.3.3 Minor classes 33

Chapter IV - Experiment 37

4.1 Results and Comparison 37

4.2 Antonym frames 40

4.3 Effectiveness of Word Cluster feature in Vietnamese Functional labeling 42

4.4 Error analyses 43

4.5 Summarization 44

Chapter V - Conclusion and Future works 45

5.1 Conclusion 45

5.2 Future works 45

Bibliography 46

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