自學考試網(wǎng)上學習社區(qū)中交互式答疑系統(tǒng)的設計和實現(xiàn)
發(fā)布時間:2019-01-04 09:07
【摘要】:交互式答疑系統(tǒng)的構建是一個復雜系統(tǒng)的工程,,包含的功能眾多,研究的側重點主要在于如何提高機器答疑的智能性及機器答疑與人工答疑的無縫鏈接。提高機器答疑智能性的一個重要方面是使系統(tǒng)能夠準確理解學習者提出的問題,為此,一方面通過改進中文分詞器提高系統(tǒng)對中文分詞的能力,另一方面,通過構建學科概念本體庫,提高系統(tǒng)理解中文語義的能力。機器答疑與人工答疑的無縫鏈接通過后臺統(tǒng)計記錄及銜接功能,將機器答疑中解決不了的問題自動轉發(fā)給人工答疑模塊,由人工答疑模塊根據(jù)問題類型選擇相應的教師實施人工答疑。系統(tǒng)會將人工答疑的結果會反饋給機器答疑模塊,以不斷提高機器答疑模塊答案庫的容量。 根據(jù)答疑系統(tǒng)的功能要求及自考生的實際檢索需求,對全文搜索引擎Lucene自帶的中文分詞器進行了改寫。在為構建資源索引時,系統(tǒng)同時使用學科本體庫與Lucene自帶的詞庫對詞語進行分詞,進而為分割后的詞語建立索引。在檢索資源時,答疑系統(tǒng)會首先使用本體庫中的概念對用戶輸入的內(nèi)容進行分詞,進而根據(jù)這些分割后的關鍵詞到系統(tǒng)的各個知識庫索引中查找相關內(nèi)容,然后,再使用Lucene自帶的分詞器對用戶的輸入進行分割,使用分割后的關鍵詞再去索引庫中查找相關內(nèi)容,最后,檢索系統(tǒng)將兩次檢索的結果合并,使用學科本體庫概念查找的結果排在前面,使用由Luence自帶分詞器分割的關鍵詞查找的資源排在后面呈現(xiàn)給檢索者。
[Abstract]:The construction of interactive question answering system is a complex system with many functions. The focus of the research is how to improve the intelligence of machine answering and the seamless link between machine answering and manual answering. An important aspect of improving the intelligence of machine answering questions is to enable the system to accurately understand the problems raised by learners. For this reason, on the one hand, the ability of the system to Chinese word segmentation can be improved by improving the Chinese word segmentation device, on the other hand, By constructing the subject concept ontology database, we can improve the ability of understanding Chinese semantics. The seamless link between machine answering and manual answering can automatically forward the problems that can not be solved in machine answering to the manual answering module through the backstage statistical record and the function of connecting with each other. According to the type of question, the manual answering module selects the corresponding teachers to answer questions manually. The result of manual answer will be fed back to machine answering module to improve the capacity of machine answering module. According to the functional requirements of the answering system and the actual retrieval requirements of the self-examinees, the Chinese word partitioning device of Lucene, the full-text search engine, is rewritten. In order to construct the resource index, the system uses the subject ontology database and the word base of Lucene to segment the words simultaneously, and then establishes the index for the segmented words. When retrieving resources, the answering system first uses the concepts in the ontology library to segment the contents entered by the user, and then, according to these segmented keywords, looks up the relevant contents in the indexes of each knowledge base of the system, and then, Then the user's input is segmented by using Lucene's own word partitioning device, and then the key words after segmentation are used to search the relevant contents in the index database. Finally, the retrieval system combines the results of the two searches. The results of the concept lookup using the subject ontology library are ranked first, and the resources found by the keywords partitioned by the Luence particifier are presented to the searcher at the end.
【學位授予單位】:電子科技大學
【學位級別】:碩士
【學位授予年份】:2012
【分類號】:TP311.52
本文編號:2400082
[Abstract]:The construction of interactive question answering system is a complex system with many functions. The focus of the research is how to improve the intelligence of machine answering and the seamless link between machine answering and manual answering. An important aspect of improving the intelligence of machine answering questions is to enable the system to accurately understand the problems raised by learners. For this reason, on the one hand, the ability of the system to Chinese word segmentation can be improved by improving the Chinese word segmentation device, on the other hand, By constructing the subject concept ontology database, we can improve the ability of understanding Chinese semantics. The seamless link between machine answering and manual answering can automatically forward the problems that can not be solved in machine answering to the manual answering module through the backstage statistical record and the function of connecting with each other. According to the type of question, the manual answering module selects the corresponding teachers to answer questions manually. The result of manual answer will be fed back to machine answering module to improve the capacity of machine answering module. According to the functional requirements of the answering system and the actual retrieval requirements of the self-examinees, the Chinese word partitioning device of Lucene, the full-text search engine, is rewritten. In order to construct the resource index, the system uses the subject ontology database and the word base of Lucene to segment the words simultaneously, and then establishes the index for the segmented words. When retrieving resources, the answering system first uses the concepts in the ontology library to segment the contents entered by the user, and then, according to these segmented keywords, looks up the relevant contents in the indexes of each knowledge base of the system, and then, Then the user's input is segmented by using Lucene's own word partitioning device, and then the key words after segmentation are used to search the relevant contents in the index database. Finally, the retrieval system combines the results of the two searches. The results of the concept lookup using the subject ontology library are ranked first, and the resources found by the keywords partitioned by the Luence particifier are presented to the searcher at the end.
【學位授予單位】:電子科技大學
【學位級別】:碩士
【學位授予年份】:2012
【分類號】:TP311.52
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