近年來，對話系統之研究已經有長足之進步。然而口語多樣性往往導致語音辨認之效能急遽地降低，缺乏適當的語意解譯著實也阻礙著口語對話系統的發展。本論文之研究主要為口語對話系統中之語意擷取與比對。在本論文中，許多主題如知識概念模型之整合與擷取、不流暢語流之偵測與修正、語音動作辨認及問句正規化皆包含在其中。

本論文利用島嶼演算法整合並擷取知識概念模型，利用詞彙在平行語料共現的特性來整合兩個既存之知識庫HowNet 及WordNet。整合後之知識概念模型包含了原有知識庫之資訊，可提供更完整之語意解釋。進一步，領域相關之知識概念模型可以利用統計方法，針對兩份對照語料從先前整合之知識概念模型中抽取獲得。最後，將一些與領域相關之專有術語及推論規則整合進去，進而建立一個領域相關之知識概念模型。

不流暢語流往往導致語音辨識與語意理解產生嚴重的錯誤，所以不流暢語流之偵測與修正遂成為對話系統中一個不得不面對的問題，本論文先以語音特徵假設檢定來獲得可能潛藏之中斷點，並利用結構化資訊之語言模型與對正模型來偵測並修正不流暢語流。

關於語音動作之判定，本論文提出兩個方法﹕以知識概念模型之部分樣本樹及語意相依圖。第一個方法先以LSA擷取語意詞或語意概念，並依此建立部分樣本樹。基於包容性之考量，概念的延伸及領域相關之知識概念模型被包含在部分樣本樹中以提升其效率。而語意相依圖乃是基於對話歷程之分析，根據詞與詞之間之相關性判斷其語音動作。

關於問句正規化或FAQ檢索，本論文提出兩個方法﹕語意段落與獨立構面之分析。語意段落乃抽取並比對詢問句之結構藉以解析語意,而獨立構面乃是分析問句與答句中之特徵並以EM演算法達成混合模型之最佳化。

最後本論文亦開發整合醫療領域三項服務之對話系統來評估所提方法之可行性，由實驗結果顯示這些方法皆可以在對話系統中關於語意擷取與比對上提供改善。

In the latest decades, the research of dialog systems has significantly improved for practice. However, the spontaneous speech variety acutely reduces performance of acoustic speech recognition. The lack of applicable semantic interpretation hedges of dialog system development. This dissertation research focuses on semantic extraction and matching in conversational dialog systems. Several helpful events are illustrated in this dissertation: ontology alignment and domain ontology extraction, edit disfluency detection and correction, speech act identification and query formulation.
We propose island-driven algorithm to ontology alignment and domain ontology extraction from two existing knowledge bases, WordNet and HowNet, based on the co-occurrence of words in a bilingual parallel corpus. The bilingual ontology achieves more semantic information coverage by integrating two complementary knowledge bases. A domain-specific ontology is further extracted from the bilingual ontology for domain application. Finally, domain-dependent terminology and axioms between domain terminologies defined in a medical encyclopedia are integrated into the domain-specific ontology.
Mis-understanding is usually resulted from the edit disfluency in conversational speech processed by computer. Detecting and correcting the edit disfluency in spontaneous speech become an important issue in dialog systems. Hypothesis testing using acoustic features is first adopted to detect potential interruption points (IPs) in the input speech. The word order of the cleanup utterance is then cleaned up based on the potential IPs using a class-based cleanup language model. The deletable region and the correction are aligned using an alignment model. Finally, log linear weighting is applied to optimize the result of edit disfluency detection and correction.
Two approaches for speech act identification: partial pattern tree to model the sentence pattern and semantic dependence graph are proposed in this dissertation. The first one extracts the semantic words/concepts using latent semantic analysis (LSA). Based on the extracted semantic words and the domain ontology, a partial pattern tree is constructed to model the speech act of a spoken utterance. The partial pattern tree is used to deal with the ill-formed sentence problem in a spoken dialog system. Concept expansion based on domain ontology is also adopted to improve system performance. Another investigation proposes an approach to modeling the discourse of spoken dialog using semantic dependency graphs. By characterizing the discourse as a sequence of speech acts, discourse modeling becomes the identification of the speech act sequence. A statistical approach is adopted to model the relations between words in the user’s utterance using the semantic dependency graphs. Dependency relation between the headword and other words in a sentence is detected using the semantic dependency grammar.
There are two approaches for FAQ mining: one is semantic segment based investigation and another is word based investigation using the independent aspects. The first one presents a novel approach to semantic segment extraction and matching for retrieving information using natural language queries. A semantic matching method is presented to estimate the similarity between the semantic segments of the query and the questions in the FAQ collection. This investigation presents an approach to domain-specific FAQ (frequently-asked question) retrieval using independent aspects. For semantic representation of the aspects, a domain-specific ontology used to be the domain knowledge representation. A probabilistic mixture model is then used to interpret the query and QA pairs based on independent aspects. The expectation-maximization (EM) algorithm is employed to estimate the optimal mixing weights in the probabilistic mixture model.
Finally, this dissertation describes a dialog system with multiple services in medical domain for evaluating the methods proposed in this dissertation. The experimental results show the proposed methods achieve improvement in semantic extraction and matching for dialog systems.

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