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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/65780

    Title: 以商家名稱萃取與地址配對協助地理資訊檢索之研究;Store Name Extraction and Name-Address Matching for Geographic Information Retrieval
    Authors: 林育暘;Lin,Yu-Yang
    Contributors: 資訊工程學系
    Keywords: 條件隨機域;自然語言處理;地理資訊檢索;CRF;NLP;GIR
    Date: 2014-08-21
    Issue Date: 2014-10-15 17:10:07 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 行動化是2014的趨勢之一,根據IDC調查報告顯示,平板電腦的出貨量在2013年Q4首次超過個人電腦,而智慧型手機不論在出貨量或市佔率早就遠遠超過其他裝置的總和。適地性服務(Location-based Service)在這波趨勢中具有至關重要的地位,因為裝置行動化的因素,大量查詢需求因此誕生,例如:路線導航、查詢附近餐廳、加油站等。適地性服務要能廣泛的提供服務,通常需要有一個完整的POI(Point of Interest)資料庫,而整個網路就是最大的資訊來源。這些資料源自於網站管理者、群眾外包(crowdsourcing)或個人使用者所分享的資訊,包括了地址、名稱、電話、評論等資訊。現在雖然有各種擷取地址相關資訊的方法,但經常面臨無法取得明確POI的名稱,在資訊檢索上受到很大的限制。
    在本篇論文中,我們提出一個商家名稱辨識的方法,藉由收集網路上包含地址的網頁,來辨認命名實體,建立一個具有商家名稱與地址關聯性的資料庫,以提高地址相關資訊檢索的效果,讓使用者在使用行動裝置查詢時,能直接輸入店家名稱或關鍵字查詢地址之服務,有效提供使用者便利性。其中,在商家命名實體辨認上,本篇論文提出了商家與組織名稱在命名上的共同特性,利用此共同特性當作特徵加入CRF模型,以提供N-Gram與詞性之外的特徵。;Mobile devices are the trend of 2014. According to the report of IDC, the first time unit shipments of tablet has exceed PCs in 2013 Q4. The smart phone has already exceed other devices in unit shipments and market ratio. LBS (Location-based Service) plays an important role in this trend. Because of the device mobility, many demand have been proposed, for example, navigation, searching restaurant or gas station. It’s usually needs a POI (Point-of Interest) database to support a LBS. The web is the largest data source, these data come from website manager, crowdsourcing and people sharing information, including address, name, phone and comment. There are many method to extract address associated information nowadays, but they are usually faced with the challenge of extracting name of POI. It’s a limitation of information retrieval.
    Our system could be separated into three parts: the Taiwan address normalization, the Store Name Entity Recognition and Address-StoreNE matching. Finally, users can search the store names on the mobile device and get the informations like address, telephone and comment immediately. In the part of Store NER, our research propose a common characteristic of store and organization names. We use these characteristic as features to join the CRF model, enhanced the recognition result.
    Appears in Collections:[資訊工程研究所] 博碩士論文

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