臺灣博碩士論文加值系統

虛擬文件檢索(Pseudo-Relevance Feedback)為目前最常見的查詢重建(Query Reformulation)典範。它假設預檢索(Initial-round of Retrieval)排名前端的文件都是相關的，所以可全用於查詢擴展(Query Expansion)。然而，預檢索所獲得的文件中，極可能同時包含重複性資訊(Redundant)和非關聯(Non-relevant)資訊，使得重新建立的查詢不能有良好檢索效能。有鑑於此，本論文探討運用不同資訊以在預檢索獲得的語音文件中挑選適當的關聯文件來建立查詢表示，讓語音文件檢索結果可以更準確。另一方面，關聯模型(Relevance Model )雖然可藉由詞袋(Bag-of-words)假設來簡化模型推導和估測，卻可能因此過度簡化問題，特別是用於語音辨識的語言模型。為了調適關聯模型，本論文有兩個貢獻。其一，本論文提出詞鄰近資訊使用於關聯模型以改善詞袋(Bag-of-words)假設於語音辨識的不適。其二，本論文也進一步探討主題鄰近資訊以強化鄰近關聯模型的架構。實驗結果證明本論文所提出之方法，不論在語音文件檢索還是語音辨識方面皆可有效改善現有方法的效能。

Pseudo-relevance feedback is by far the most commonly-used paradigm for query reformulation in spoken document retrieval, which assumes that a small amount of top-ranked feedback documents obtained from the initial retrieval are relevant and can be utilized for query expansion. Nevertheless, simply taking all of the top-ranked feedback documents acquired from the initial retrieval for query modeling does not necessary work well, especially when the top-ranked documents contain much redundant or non-relevant cues. In view of this, we explore different kinds of information cues for selecting helpful feedback documents to further improve information retrieval. On the other hand, relevance model (RM) based on “bag-of-words” assumption, which can facilitate the derivation and estimation, may be oversimplified for the task of language modeling in speech recognition. Hence, we also enhance RM in two significant aspects. First, “bag-of-words” assumption of RM is relaxed by incorporating word proximity information into RM formulation. Second, topic-based proximity information is additionally explored to further enhance the proximity-based RM framework. Experiments conducted on not only a spoken document retrieval task but also a speech recognition task indicates that our approaches can bring competitive utilities to existing ones.

1 Introduction 1
1.1 Motivation 1
1.1.1 Spoken Document Retrieval 1
1.1.2 Speech Recognition 6
1.2 Contribution 7
1.3 Outline of the Thesis 8
2 Related Work 10
2.1 Language Modeling for Spoken Document Retrieval 10
2.1.1 Retrieval Modeling Approaches 11
2.1.2 Pseudo-Relevance Feedback 15
2.1.3 Query Modeling 25
2.2 Language Modeling for Speech Recognition 31
2.2.1 N-gram Language Model 31
2.2.2 Topic-based Language Models 32
2.2.3 Trigger-based Language Model 33
2.2.4 Recurrent Neural Network Language Model vs. Discriminative Language Model 34
2.2.5 Relevance Modeling 34
3 Effective Pseudo-Relevance Feedback &; Proximity Information 38
3.1 Diversity Measure 39
3.2 Density Measure 40
3.3 Non-Relevance Measure 42
3.4 Proximity Information for RM 44
3.5 Topic-based Proximity Information for RM 46
4 Experiments on Spoken Document Retrieval 47
4.1 Spoken Document Collections &; Evaluation Metrics 47
4.2 Subword-level Index Features 49
4.3 Baseline Experiments 50
4.4 Using Effective Pseudo-Relevance Feedback 52
4.5 IDF-Based Term Weighting 54
4.6 Fusion of Different Levels of Indexing Features 54
5 Experiments on Speech Recognition 56
5.1 Speech Recognition Corpus &; Evaluation Metrics 56
5.2 Baseline Experiments 58
5.3 Using Proximity Information 59
5.4. Using Latent Topic Proximity Information 60
6 Conclusion and Future Work 62
Bibliography 64

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