The purpose of this study was to compare the effects of guided musical analysis and guided attention to textual understanding on the speed of solo memorization by selected university vocal students. The guided musical analysis consisted of instruction to a subject regarding the melodic elements, rhythmic elements, phraseology, form, and key relationships of the experimental songs. The guided attention to textual understanding consisted of instruction to a subject regarding the meaning and phraseology of the experimental song texts. Subjects were required to rhythmically scan the texts in a declamatory manner. It was concluded that the three conditions of memorization were equally effective. Memorization rates were not significantly altered by historical period of song. Differences in memorization rates appeared to parallel the subjects' academic performances and their performances on the Drake Musical Aptitude Tests. Findings of this study indicated that future memorization experiments should be conducted with larger samples of subjects of a single sex who are music majors.

More than ever, information delivery online and storage heavily rely on text. Billions of texts are produced every day in the form of documents, news, logs, search queries, ad keywords, tags, tweets, messenger conversations, social network posts, etc. Text understanding is a fundamental and essential task involving broad research topics, and contributes to many applications in the areas text summarization, search engine, recommendation systems, online advertising, conversational bot and so on. However, understanding text for computers is never a trivial task, especially for noisy and ambiguous text such as logs, search queries. This dissertation mainly focuses on textual understanding tasks derived from the two domains, i.e., disaster management and IT service management that mainly utilizing textual data as an information carrier. Improving situation awareness in disaster management and alleviating human efforts involved in IT service management dictates more intelligent and efficient solutions to understand the textual data acting as the main information carrier in the two domains. From the perspective of data mining, four directions are identified: (1) Intelligently generate a storyline summarizing the evolution of a hurricane from relevant online corpus; (2) Automatically recommending resolutions according to the textual symptom description in a ticket; (3) Gradually adapting the resolution recommendation system for time correlated features derived from text; (4) Efficiently learning distributed representation for short and lousy ticket symptom descriptions and resolutions. Provided with different types of textual data, data mining techniques proposed in those four research directions successfully address our tasks to understand and extract valuable knowledge from those textual data. My dissertation will address the research topics outlined above. Concretely, I will focus on designing and developing data mining methodologies to better understand textual information, including (1) a storyline generation method for efficient summarization of natural hurricanes based on crawled online corpus; (2) a recommendation framework for automated ticket resolution in IT service management; (3) an adaptive recommendation system on time-varying temporal correlated features derived from text; (4) a deep neural ranking model not only successfully recommending resolutions but also efficiently outputting distributed representation for ticket descriptions and resolutions.