自然語言檢索是使用人類語言問句搜尋答案。其目的是要擷取適合使用者的答案。相較於一般的短語查詢，自然語言問句查詢則是更能直覺的呈現出使用者的問題。例如，問句「台南三天兩夜情侶浪漫旅遊?」。但現今的搜尋引擎在自然語言檢索方面的表現往往不佳，導致使用者必須花大部分的時間在瀏覽出現部分詞彙網頁答案。在本論文，自然語言問句搜尋被定義為使用者想獲得一系列同性質的地點，並與ROSE等人提出條列式資訊目的定義相符。
在仔細的分析自然語言問句後，問句結構可以被分為問句時間、問句稱謂、問句活動、問句情緒以及問句地點類別。並且我們發現到了問句結構之間有一些關係存在，提出了一個自動化的方式找出問句結構之間的關聯來擴充問句。我們基於使用者需求並分析問句中的時間及稱謂以及隱含的因素，進而推薦適合的地點。我們使用適合中文的問句分析演算法辨識以上問句結構特徵。至於答案結構可以被分成適合的地點與適合的地點類別。我們利用問句結構和答案結構的關係，結合實際上的網路的評價意見，建構出以時間及稱謂為基礎的地點排序模型(LSRM)來改善地點搜尋。
實驗結果顯示我們提出的模型LSRM可以幫助使用者找到他們想要的地點。也顯示出我們的系統著實能增進地點搜尋的效能

Natural language search is to use human language questions as query to search answers. The task of natural language search is to extract suitable answers for users. Compare with short query, Natural language query users can directly submit their query intents. For example, the question, “The three days and two nights romantic travel which suit for couple?” But conventional search engines can’t efficiently process natural language queries and users can’t get good search results. So users need to spend lots of time on browsing and filter the result pages, which may involve some noise information. In those natural language question search, user wants to obtain a list of homogeneous locations. According to Rose et al. proposed list-informational goal definition, natural language question search is properly matched list-informational goal.
After advanced analysis on natural language question structure, the question structure can be divided into six parts, they are question Time, question people, question activity, question sentiment, question location category and question area. We noted that there are some relations between our question structures. Then, we proposed an automated method to identify related question structures and use them to expand questions. We try to recommend suitable locations based on the intent of users by analyzing the Time, people and other hidden factor. We used the algorithm of question analysis to identify question features. As to the answer structure, it can be divided into answer location and answer location category to match the question structure. We combine the relationship between the question structure and the answer structure to construct Location-Search Ranking Model (LSRM) to improve location search.
Experiment result shows that our proposed method LSRM can help user to get location list which matched their intent. And it shows LSRM really can enhance performance in location search.

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