query语义依存分析技术分析.docx

(4)将语义依存分析问题转换为分类问题,提出了基于统计的 query 语义
依存分析技术,包括语义资源的挖掘、分类特征的设计和选择。最终通过对比和实验说明了规则和统计两种方法的有效性。
关键词: 语义依存分析;语义搜索;搜索引擎;用户查询
Abstract
With the rapid development of the , information is generated and spread at unprecedented speed. At the same time, garbage information is growing in an exponen- tial way. How to find out really useful information from it es great challenge of search engine. In the traditional search engines, user enters query, search engine returns a very long URL list. It doesn’t know what the user asks, what the user looks for. It only finds out webpages containing the keywords based on keyword matching retrieval methods, and then rank them through page rank algorithm. Users need to identify which page satisfies their needs from a long list of pages. Query semantic dependency parsing (QSDP) can improve the page ranking of traditional search engine, it can reach a deep semantic understanding of query, thus lead more accurate understanding of user’s needs.
On the other hand, compared with traditional search engines, semantic search re- cently attracts wide attention of both industry and academic circles. Semantic anizes all information into a huge structured knowledge base, and directly returns the answer when user searches, thus it saves time to identify information. QSDP can help semantic search engine understand user’s needs and return the most accurate answer re- trieved from knowledge base. In addition, QSDP also has a wide range of applications, including automatic question answering, intelligent personal assistant, information re- trieval, information extraction and so on.
This paper presents both rule-based and statistics-based QSDP techniques, research topics include:
Similarities and differences of semantic dependency parsing technology on query and normal sentence. Compared to normal sentence, query has shorter length and loos- er structure, therefor, semantic dependency parsing technologies on query and normal sentence differ greatly.
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