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中文Word2Set的開發與在字詞與短文的相似度應用

為了解決Advantage synonym的問題，作者鄭耀騰 這樣論述：

在互聯網興起的環境下，機器的訊息和知識組成形式傾向於由網際網路中各式各樣的短文本或短篇章的文件上獲取內容。然而，由於短文本在內容的同義字或近義詞的用字不同的特性，使得短文本的內容在預測或分類的落差過大，導致於機器在預測文本時會受學習時的既有文字資料之用字的差異，產生短文本的預測與分類之誤判。因此，在短文本處理時需將短文本中的單詞內容進行語意分析是非常重要的，因此本論文提出的以中文WordNet為基礎的中文Word2Set的開發與整合Word2Vec到中文Word2Set的改進，透過這兩種方法的整合來做為單詞比較的語意分析。基於單詞比較的語意分析合併至文本內容進行分析，本論文提出的整合Word

2Set與Word2Vec對潛在狄利克雷分配的改進的方法做為文本比較的語意分析，降低機器在預測時受到用詞的差異而產生的誤判。在單詞比較的語意分析的任務需求，以人工智慧中自然語言處理的領域是最為合適。在單詞的辨識上，單詞的語意訊息和單詞的關聯詞對於識別單詞非常重要。單詞的語意訊息由具有語意訊息的語料庫中文WordNet提供，以單詞的同義詞和反義詞所組成的訊息。根據語言體系上的漢語與英文的表意差異，開發出適合於漢語體系的中文Word2Set。單詞的關聯詞訊息由詞嵌入中的Word2Vec模型透過外部的語料庫訓練得到的關聯詞所組成。因此提出整合Word2Vec到中文Word2Set的改進，透過這兩種方

法的整合來做為單詞比較的語意分析。最後，透過同義詞、反義詞及近義詞的測試來評估所提出方法的有效性。在短文本的語意比較分析應用上，使用潛在狄利克雷分配找出文本的主題組成來比對未知文本的相似度。在主題的單詞組成上是基於訓練集的詞彙表和句子中的詞頻矩陣所組成。透過以中文WordNet為基礎的中文Word2Set的開發、整合Word2Vec到中文Word2Set的改進，透過這兩種方法的整合來改善詞頻矩陣中的詞頻統計，並且可以 擴展LDA的詞彙內容。 實驗結果表明，該方法可以有效地區分短文中具有相似含義的不同單詞，從而可以區分同義詞和近義詞，從而比較未知短文的相似性。

探索斑腿樹蛙腸道菌以及其網絡關係

為了解決Advantage synonym的問題，作者翁正軒 這樣論述：

The concerted activity of intestinal microbes is crucial to the health and development of their host organisms. Studies have suggested that microbial assemblages in the intestine of animals are engines of globally important host physiological processes between hibernating and non-hibernating states

. The advances in Next Generation Sequencing (NGS) technologies facilitate our understanding of gut microbiota with high resolutions in diversity and metabolic functioning between hibernating and non-hibernating seasons. Polypedates megacephalus is an invasive species in Taiwan since 2006. The appro

ach of habitat usage and population dispersal of this invasive treefrogs across seasons have suggested a rapid expansion across counties in Taiwan. However, it still lacks an effective solution to control the expansion of invasive P. megacephalus. Due to the reciprocal interactions between gut micro

biota and host physiology, I attempt to explore gut microbiota of P. megacephalus, decipher microbial interactions to understand the potential mechanisms of microbial ecosystem, and further manipulate host response by modulating gut microbiota according to the guidance by computational network analy

sis. This study not only delineated seasonal changes of gut microbiota in composition and metabolic functioning but demonstrated the potentials of computational network inference toward practical applications on animal systems.The compositional and predicted functional changes of gut microbiota acro

ss non-hibernating and artificial hibernating seasons were identified based on 16S rRNA amplicon analysis. The abundance profile and predicted functions of microbial community significantly change between artificial hibernating (AH) and non-hibernating (NH) treefrogs. Artificial hibernation signific

antly reduces microbial diversity and the level of Firmicutes and increases the level of Proteobacteria in the treefrog gut microbiota. In addition, AH treefrogs harbor core taxonomic units that are rarely abundant in NH treefrogs. Moreover, artificial hibernation significantly increased relative ab

undance of red-leg syndrome-related genera such as Citrobacter and Aeromonas. Functional predictions via PICRUSt and Tax4Fun suggested that artificial hibernation has effects on most pathways including metabolism and signal transduction. These results suggest that artificial hibernation restructure

gut microbiota in treefrogs and significantly reduce microbial complexity of gut microbiome.The use of computational methods to decipher microbial interactions have been applied on microbiome data in a time-series fashion. A time-series microbiome data could monitor the population changes of each ba

cterium in the community over time. Due to the adjustable gut microbiome complexity of hibernating animals, the growth microbiome time-series (GMT) dataset is proposed to apply on the computational network inference methods. Among varieties of network inference tools, regression-based network model

is selected and utilized due to its better performance tested by using in silico dataset. Lotka-Volterra models, also known as predator–prey equations, are the most currently used regression-based method, and predict both dynamics of microbial communities and how communities are structured and susta

ined. The interaction network of gut microbiota at the genus level in the treefrog was constructed using Metagenomic Microbial Interaction Simulator (MetaMIS) package. The interaction network contained 1,568 commensal, 1,737 amensal, 3,777 mutual, and 3,232 competitive relationships, e.g., Lactococc

us garvieae has a commensal relationship with Corynebacterium variabile. To validate the interacting relationships, I took advantage of probiotic system to evaluate the responses of gut microbiota to the probiotic trials. The trials involved different groups including single strain (L. garvieae, C.

variabile, and Bacillus coagulans, respectively) and a combination of L. garvieae, C. variabile, and B. coagulans, because of the cooperative relationship among their respective genera identified in the interaction network. After a two-week trial, the combination of cooperative microbes yielded sign

ificantly higher probiotic concentrations than single strains, and the immune response (interleukin-10 expression) also significantly changed in a manner consistent with improved probiotic effects.