Abstract
Temporal relation resolution involves extraction of temporal information explicitly or implicitly embedded in a language. This information is often inferred from a variety of interactive grammatical and lexical cues, especially in Chinese. For this purpose, inter-clause relations (temporal or otherwise) in a multiple-clause sentence play an important role. In this paper, a computational model based on machine learning and heterogeneous collaborative bootstrapping is proposed for analyzing temporal relations in a Chinese multiple-clause sentence. The model makes use of the fact that events are represented in different temporal structures. It takes into account the effects of linguistic features such as tense/aspect, temporal connectives, and discourse structures. A set of experiments has been conducted to investigate how linguistic features could affect temporal relation resolution.
| Original language | English |
|---|---|
| Pages (from-to) | 582-588 |
| Number of pages | 7 |
| Journal | Proceedings of the Annual Meeting of the Association for Computational Linguistics |
| Publication status | Published - 2004 |
| Event | 42nd Annual Meeting of the Association for Computational Linguistics, ACL 2004 - Barcelona, Spain Duration: 21 Jul 2004 → 26 Jul 2004 |
ASJC Scopus subject areas
- Computer Science Applications
- Linguistics and Language
- Language and Linguistics