Using Activity and Hidden Sentiment Expansion to Improve Location Entity Identification

  • 鄭 文森

Student thesis: Master's Thesis


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 “Which places in Tainan are appropriate for dating?” 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 entities 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 four parts they are question context question entity question activity and question sentiment We noted that there are some relations between activity and sentiment Then we proposed an automated method to identify related sentiments and use them to expand questions We try to recommend suitable entities based on the intent of users by analyzing the activity which user want to do and the sentiments which behind the activity We used the algorithm of question analysis to identify question features As to the answer structure it can be divided into context evidence page entity type entity activity and entity sentiment summation to match the question structure We combine the relationship between the question structure and the answer structure to construct Activity-Sentiment-based Entity Ranking Model (ASERM) to improve entity search Experiment result shows that our proposed method ASERM can help user to get entity list which matched their intent And it shows ASERM really can enhance performance in entity search
Date of Award2014 Aug 27
Original languageEnglish
SupervisorWen-Hsiang Lu (Supervisor)

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