TAILIEUCHUNG - Auto-Generation of NVEF Knowledge in Chinese

Every event, regardless of size, needs careful planning in advance. The planning process should include, in an appropriate way, everyone who will be assisting in the event delivery as well as any other relevant agencies such as the local authority and police. Keeping track of all the steps in the process is made much easier if you keep all the information together in a single Event Management Plan (see suggested outline below). If you keep this electronically make sure you back it up frequently. Small events do not need the same mass of documentation as large ones, and there. | Computational Linguistics and Chinese Language Processing Vol. 9 No. 1 February 2004 pp. 41-64 41 The Association for Computational Linguistics and Chinese Language Processing Auto-Generation of NVEF Knowledge in Chinese Jia-Lin Tsai Gladys Hsieh and Wen-Lian Hsu Abstract Noun-verb event frame NVEF knowledge in conjunction with an NVEF word-pair identifier Tsai et al. 2002 comprises a system that can be used to support natural language processing NLP and natural language understanding NLU . In Tsai et al. 2002a we demonstrated that NVEF knowledge can be used effectively to solve the Chinese word-sense disambiguation WSD problem with accuracy for nouns and verbs. In Tsai et al. 2002b we showed that NVEF knowledge can be applied to the Chinese syllable-to-word STW conversion problem to achieve accuracy for the NVEF related portions of Chinese sentences. In Tsai et al. 2002a we defined a collection of NVEF knowledge as an NVEF word-pair a meaningful NV word-pair and its corresponding NVEF sense-pairs. No methods exist that can fully and automatically find collections of NVEF knowledge from Chinese sentences. We propose a method here for automatically acquiring large-scale NVEF knowledge without human intervention in order to identify a large varied range of NVEF-sentences sentences containing at least one NVEF word-pair . The auto-generation of NVEF knowledge AUTO-NVEF includes four major processes 1 segmentation checking 2 Initial Part-of-Speech IPOS sequence generation 3 NV knowledge generation and 4 NVEF knowledge auto-confirmation. Our experimental results show that AUTO-NVEF achieved accuracy for news and for specific text types which included research reports classical literature and modern literature. AUTO-NVEF automatically discovered over 400 000 NVEF word-pairs from the 2001 United Daily News 2001 UDN corpus. According to our estimation the acquired NVEF knowledge from 2001 UDN helped to identify 54 of the NVEF-sentences in the .

• Tổ chức sự kiện
• liberal policy
• previously referred
• Steinman discovered
• memory against
• organisational involvement
• altruist focused
• unanimously chosen
• Performance
• Strategic Choices
• Subcontracting SMEs
• Charles Eliot
• donation or providing