以文字探勘技術擷取常識性知識應用於情境背景圖推薦系統

Web-based Text Mining to Retrieve Commonsense Knowledge for Contextual Background Image Suggestion Systems

洪聖豪(Sheng-Hao Hung)

指導教授 ： 洪政欣

國立暨南國際大學/科技學院/資訊工程學系/博士(2009年)

摘要

我們長遠的目標的研究是開發“智能風格”系統。這論文制定了一個文字背景圖像檢索系統，可用於自動選擇背景圖片。
本研究是透過文字摘要系統找出文章中最有可能的”關鍵字句” ，然後再透過自然語言處理技術如語義角色標註和知識庫如ConceptNet等加以歸納分析，進而找出合理的背景圖片。

關鍵字

文字探勘技術 ； 語意角色標註技術

並列摘要

The long-term goal of our research is to develop “intelligent styling” systems that automate the styling processes for Web documents. This thesis developed a framework for a contextual background image retrieval system that can be applied to automatically select background images, which are appropriate to decorate a textual passage in a coherent manner, from the contextual information implied in the sentences. We applied textual summarization techniques to determine possible salient concepts of a given textual passage. Natural language techniques such as name entity extraction, semantic role labeling, together with knowledgebase such as ConceptNet, are integrated for such a purpose. The initial results indicate the potential of the proposed techniques for different categories of textual passages. However, the results also showed that existing commonsense knowledge are rather restricted for the addressed application. This motivated the work of the second part of this thesis which aims to develop web-based text mining techniques to retrieve event-based commonsense knowledge out of web pages. We proposed a framework based on lexico-syntactic pattern matching and semantic role labeling techniques. The evaluation results showed that the proposed approach could automatically accumulate commonsense knowledge efficiently with very high accuracy rates that are close to 98%.

並列關鍵字

web mining ； semantic role labeling

參考文獻

[1] ACE, Automatic Content Extraction, National Institute of Standards and Technology, 2009, available at: http://www.nist.gov/speech/tests/ace, last visited at: July. 16th 2009.

[2] Ahlswede T. and Evens M., 1988, “Parsing vs. Text Processing in the Analysis of Dictionary Definitions.” Proceedings of the 26th Annual Meeting of the Association for Computational Linguistics, pp. 217-224.

[3] Alshawi H., 1987, “Processing Dictionary Definitions with Phrasal Pattern Hierarchies.” American Journal of Computational Linguistics, Vol. 13, No. 3, pp. 195-202.

[4] Aone C., Ramos-Santacruz M., 2000, “REES: A Large-Scale Relation and Event Extraction System.” Proceedings of the 6th Conference on Applied Natural Language Processing, Seattle, Washington, pp. 76-83.

[5] Berland M. and Charniak E., 2002, “Finding Parts in Very Large Corpora.” Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics, pp.57-64.