4.4.1. The difference of 在 zai4 ‘ZAI’ and 正在 zheng4zai4 ‘ZAI’
4.4.2. The Difference of Static State with and without ZHE and their relation with Negators
4.4.3. The difference of ZHE and LE in existential sentences
4.5. 过 guo4 ‘GUO’
4.5.1. Grammaticality and World Knowledge
4.5.2. Time Frame of GUO
4.5.3. The Truth Condition of GUO
4.6. Negations
4.6.1. 不 bu4
4.6.2. 没有 mei2you3
4.7. Summary
Chapter 5. Formal Representation of Aspect
5.1. Introduction
5.1.1. Second Order Logic
5.1.2. Predicates and Parameters
5.1.3. Class, Instance and Subclass
5.1.4. Attributes, Functionalities and Habits
5.1.5. Instances of Situations
5.1.6. Multi-modal Predicates in Natural Language
5.2. The Basic Predicates Related to Time
5.3. Representations for Ontological Situations
5.3.1. Representations for state and change of state
5.3.2. Representations for Complex Situations
5.4. Linguistic Event Types
5.4.1. Static State: ---
5.4.2. Delimitative State: |---|
5.4.3. Instant Dynamic State: ~~~
5.4.4. Activity: |~~~|
5.4.5. Semelfactive: |~|
5.4.6. Change of State: --|--, --|~~, ~~|--, ~~|~~
5.4.7. Accomplishment: |~~~|--, |~~~|~~
5.4.8. Instantaneous Accomplishment: |~|--, |~|~~
5.5. Chinese Aspectual Markers
5.5.1. 了 le0 ‘LE’
5.5.2. 着 zhe0 ‘ZHE’ and 在 zai4 ‘ZAI’
5.5.3. 过 guo4 ‘GUO’
5.5.4. Negators
5.6. Summary
Chapter 6. Annotating a Chinese Corpus for Aspectual Study
6.1. Introduction
6.2. Annotation Framework
6.2.1. Event Annotation
6.2.2. Illocutionary Acts
6.2.3. Modalities
6.2.4. Sentence Type Hierarchy
6.3. Some Constructions in Chinese
6.3.1. Serial Verb Constructions (SVCs)
6.3.2. Resultative Verbal Constructions (RVCs)
6.4. Annotating a Chinese Corpus
6.4.1. Data Selection
6.4.2. Data Annotation
6.4.3. Annotation Result
6.4.4. Agreement Test
6.5. Summary
Chapter 7. Automatic Aspectual Classification Chinese Sentences
7.1. Introduction
7.2. Linguistic indicators for sentence type classification
7.2.1. Indicators for different event types
7.2.2. Indicators for modalities
7.2.3. Indicators for speech acts
7.3. Aspectual Classification
7.3.1. Grounding the Features
7.3.2. Feature Extraction
7.3.3. Classifiers
7.4. Experiments
7.4.1. Sentence Type Classification
7.4.2. Classification on Different Modalities
7.4.3. Classification on Different Speech Acts
7.4.4. Classification on Mid-Level Event Types
7.4.5. Classification on Different Accomplishments
7.4.6. Classification on Different Achievements
7.4.7. Experiments with predicated features
7.4.8. Discussions
7.5. Summary
Chapter 8. Conclusion
8.1. Summarization of the Thesis
8.2. Consequences of the Study
8.2.1. Ontology and Lexicon
8.2.2. Extended Generative Lexicon
8.2.3. Computational Semantics
References
Hongzhi Xu received his Ph.D. in Linguistics from The Hong Kong Polytechnic University and subsequently worked as a postdoctoral researcher at the Computer Science Department, University of Pennsylvania. He is now an assistant researcher at the Institute of Corpus Study and Application, Shanghai International Studies University. He has published important papers on ACL, COLING, and various aspects of linguistics and computational linguistics.
This book presents a theoretical study on aspect in Chinese, including both situation and viewpoint aspects. Unlike previous studies, which have largely classified linguistic units into different situation types, this study defines a set of ontological event types that are conceptually universal and on the basis of which different languages employ various linguistic devices to describe such events. To do so, it focuses on a particular component of events, namely the viewpoint aspect. It includes and discusses a wealth of examples to show how such ontological events are realized in Chinese. In addition, the study discusses how Chinese modal verbs and adverbs affect the distribution of viewpoint aspects associated with certain situation types.
In turn, the book demonstrates how the proposed linguistic theory can be used in a computational context. Simply identifying events in terms of the verbs and their arguments is insufficient for real situations such as understanding the factivity and the logical/temporal relations between events. The proposed framework offers the possibility of analyzing events in Chinese text, yielding deep semantic information.