منابع مشابه
Stacking Heterogeneous Joint Models of Chinese POS Tagging and Dependency Parsing
Previous joint models of Chinese part-of-speech (POS) tagging and dependency parsing are extended from either graphor transition-based dependency models. Our analysis shows that the two models have different error distributions. In addition, integration of graphand transition-based dependency parsers by stacked learning (stacking) has achieved significant improvements. These motivate us to stud...
متن کاملAnalyzing the Effect of Global Learning and Beam-Search on Transition-Based Dependency Parsing
Beam-search and global models have been applied to transition-based dependency parsing, leading to state-of-the-art accuracies that are comparable to the best graph-based parsers. In this paper, we analyze the effects of global learning and beam-search on the overall accuracy and error distribution of a transition-based dependency parser. First, we show that global learning and beam-search must...
متن کامل藍芽無線環境下中文語音辨識效能之評估與分析 (Performance Evaluation and Analysis of Mandarin Speech Recognition over Bluetooth Communication Environments) [In Chinese]
摘要 本論文探討語音辨識技術於藍芽(Bluetooth)無線環境下之效能。我們分別在藍芽實際與模擬使用 環境下,應用 TCC-300語料庫及 HTK軟體,進行一系列語者無關(Speaker Independent)的語音辨識實 驗。此外,為彌補通道效應之影響,我們亦引用若干強健性技術以提升辨識率。 為評估藍芽實際使用環境下之語音辨識效能,我們將 TCC-300語料庫轉錄成室內使用環境 0公尺、 4 公尺以及走廊使用環境 50 公尺三個藍芽操作環境語料庫,此語料庫可提供語音辨識或其他相關語音 處理研究之用。實驗結果顯示,在訓練環境與測試環境完全匹配情況下,測試距離為 0、4與 50公尺所 獲得之音節辨識率分別為 55.82%、53.54%、以及 42.74%,辨識率隨著距離增加而下降,而且遠低於在 原來的 TCC-300語料庫進行相同測試所得之 69.25%的辨識率。另外,在環境不匹配...
متن کاملExploiting Lexical Dependencies from Large-Scale Data for Better Shift-Reduce Constituency Parsing
This paper proposes a method to improve shift-reduce constituency parsing by using lexical dependencies. The lexical dependency information is obtained from a large amount of auto-parsed data that is generated by a baseline shift-reduce parser on unlabeled data. We then incorporate a set of novel features defined on this information into the shift-reduce parsing model. The features can help to ...
متن کاملSemantic Associative Topic Models for Information Retrieval
主題模型(topic model)被廣泛地應用在各種文件建 模以及語音識別、資訊檢索和本文探勘系統中,有 效地擷取文件或字詞的語意和統計資料。大多數主 題模式,例如機率潛在語意分析(probabilistic latent semantic analysis) 和 潛 在 狄 利 克 里 分 配 (latent Dirichlet allocation),主要都透過一組潛藏的主題機 率分布來描述文件與字詞之間的關係,並用以擷取 文件的潛在語意資訊。然而,傳統的主題模型受限 於詞袋(bag-of-words)的假設,其潛藏主題僅能用來 擷取個體詞(individual word)之間的語意資訊。雖然 個體詞可傳達主題信息,但有時會缺乏本文準確的 語意知識,容易造成文件的誤判,降低檢索的品 質。為了改善主題模型的缺點,本論文提出一種新 穎的語意關聯主題模型(semantic associ...
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ژورنال
عنوان ژورنال: 财经与管理
سال: 2019
ISSN: 2529-7848,2529-783X
DOI: 10.26549/cjygl.v3i6.2464