精神科諮詢文件檢索(Psychiatric Consultation Record Retrieval)之目的在協助廣大的憂鬱症潛在族群快速且有效地找到符合自己憂鬱問題的諮詢文件。一份諮詢文件中包含了使用者的憂鬱問題以及相對應的專家建議。藉由參考相關的諮詢文件，使用者能從前人經驗中獲得專家的建議事項，藉此排解自身的憂鬱情緒。為達成此一目標，本論文提出以文件中的主題(Topic)及主題之間的關係(Relation)為基礎，計算使用者查詢(Query)與諮詢文件的相似度。諮詢文件中的主題包括負面生活事件(Negative Life Event)與憂鬱症狀(Depressive Symptom)，症狀之間的關係則包括因果關係(Cause-Effect Relation)與時間關係(Temporal Relation)。經由分析文件中的事件、症狀與關係，可對使用者的憂鬱問題更加瞭解，並使檢索結果更能符合使用者之需求。
實驗分成兩大部分。第一部分主要評估主題識別(Topic Identification)的方法；第二部份比較使用主題或文字(Word)的檢索模型，比較對象包括向量空間模型(Vector Space Model, VSM)以及Okapi BM25。實驗結果顯示，考慮諮詢文件中的主題資訊比起單純使用文字資訊更能達到精確的檢索結果。

The aim of psychiatric consultation record retrieval is to assist people efficiently and effectively locating the consultation records relevant to their depressive problems. A consultation record consists of depressive problems and their corresponding responses. By referring to the relevant records, people can be aware that they are not alone because many people have suffered from the same or similar problems. Also, they can understand how to alleviate their depressive symptoms according to the suggestion from the health professionals. To achieve the goal, this thesis proposes the use of topics and inter-topic relations to compute the similarities between users’ queries and consultation records. The topics in the consultation records include negative life events and depressive symptoms. The inter-topic relations, which refer to the relations that hold between symptoms, include cause-effect relations and temporal relations. Taking into account events, symptoms and relations is beneficial for better understanding of users’ information needs so as to obtain more precise retrieval results.
The experiments are divided into two parts. First, the identification of events, symptoms and relations are evaluated. Then, we compare our topic-based retrieval method to word-based methods such as vector space model (VSM) and Okapi BM25 model. The results show that using topic information can achieve higher precision than using word-level information alone.

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