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The purpose of this dissertation was to use the technology of text mining and topic modeling to explore unobserved themes of medical case reports that involve medical imaging. Case reports have a valuable place in medical research because they provide educational benefits, offer evidence, and encourage discussions. Their form has evolved throughout the years, but they have remained a key staple in providing important information to the medical communities around the world with educational context and illuminating visuals. Examining medical case reports that have been published throughout the years on multiple medical subjects can be challenging, therefore text mining and topic modeling methods were used to analyze a large set of abstracts from medical case reports involving radiology. The total number of abstracts used for the data analysis was 68,845 that were published between the years 1975 to 2022. The findings indicate that text mining and topic modeling can offer a unique and reproducible approach to examine a large quantity of abstracts for theme analysis.

This dissertation focuses on the review understanding problem, which has gained attention from both industry and academia, and has found applications in many downstream tasks, such as recommendation, information retrieval and review summarization. In this dissertation, we aim to develop machine learning and natural language processing tools to understand and learn structured knowledge from unstructured reviews, which can be investigated in three research directions, including understanding review corpora, understanding review documents, and understanding review segments. For the corpus-level review understanding, we have focused on discovering knowledge from corpora that consist of short texts. Since they have limited contextual information, automatically learning topics from them remains a challenging problem. We propose a semantics-assisted non-negative matrix factorization model to deal with this problem. It effectively incorporates the word-context semantic correlations into the model, where the semantic relationships between the words and their contexts are learned from the skip-gram view of a corpus. We conduct extensive sets of experiments on several short text corpora to demonstrate the proposed model can discover meaningful and coherent topics. For document-level review understanding, we have focused on building interpretable and reliable models for the document-level multi-aspect sentiment analysis (DMSA) task, which can help us to not only recover missing aspect-level ratings and analyze sentiment of customers, but also detect aspect and opinion terms from reviews. We conduct three studies in this research direction. In the first study, we collect a new DMSA dataset in the healthcare domain and systematically investigate reviews in this dataset, including a comprehensive statistical analysis and topic modeling to discover aspects. We also propose a multi-task learning framework with self-attention networks to predict sentiment and ratings for given aspects. In the second study, we propose corpus-level and concept-based explanation methods to interpret attention-based deep learning models for text classification, including sentiment classification. The proposed corpus-level explanation approach aims to capture causal relationships between keywords and model predictions via learning importance of keywords for predicted labels across a training corpus based on attention weights. We also propose a concept-based explanation method that can automatically learn higher level concepts and their importance to model predictions. We apply these methods to the classification task and show that they are powerful in extracting semantically meaningful keywords and concepts, and explaining model predictions. In the third study, we propose an interpretable and uncertainty aware multi-task learning framework for DMSA, which can achieve competitive performance while also being able to interpret the predictions made. Based on the corpus-level explanation method, we propose an attention-driven keywords ranking method, which can automatically discover aspect terms and aspect-level opinion terms from a review corpus using the attention weights. In addition, we propose a lecture-audience strategy to estimate model uncertainty in the context of multi-task learning. For the segment-level review understanding, we have focused on the unsupervised aspect detection task, which aims to automatically extract interpretable aspects and identify aspect-specific segments from online reviews. The existing deep learning-based topic models suffer from several problems such as extracting noisy aspects and poorly mapping aspects discovered by models to the aspects of interest. To deal with these problems, we propose a self-supervised contrastive learning framework in order to learn better representations for aspects and review segments. We also introduce a high-resolution selective mapping method to efficiently assign aspects discovered by the model to the aspects of interest. In addition, we propose using a knowledge distillation technique to further improve the aspect detection performance. / Doctor of Philosophy / Nowadays, online reviews are playing an important role in our daily lives. They are also critical to the success of many e-commerce and local businesses because they can help people build trust in brands and businesses, provide insights into products and services, and improve consumers' confidence. As a large number of reviews accumulate every day, a central research problem is to build an artificial intelligence system that can understand and interact with these reviews, and further use them to offer customers better support and services. In order to tackle challenges in these applications, we first have to get an in-depth understanding of online reviews. In this dissertation, we focus on the review understanding problem and develop machine learning and natural language processing tools to understand reviews and learn structured knowledge from unstructured reviews. We have addressed the review understanding problem in three directions, including understanding a collection of reviews, understanding a single review, and understanding a piece of a review segment. In the first direction, we proposed a short-text topic modeling method to extract topics from review corpora that consist of primary complaints of consumers. In the second direction, we focused on building sentiment analysis models to predict the opinions of consumers from their reviews. Our deep learning models can provide good prediction accuracy as well as a human-understandable explanation for the prediction. In the third direction, we develop an aspect detection method to automatically extract sentences that mention certain features consumers are interested in, from reviews, which can help customers efficiently navigate through reviews and help businesses identify the advantages and disadvantages of their products.

Pastaza Quichua (PQ) is a member of the Quechua IIB branch of Quechuan languages and has a rich morphology. However, this richness is often over-simplified for the sake of simpler explanation. Most Quechuan languages have a morpheme that is usually spelt -ga, -ka or -qa, and is described as a topicalizing clitic. In this thesis, I will examine the morpheme -ga in PQ, which, like its cognates, is often said to be a topicalizing clitic, despite the fact that it frequently breaks traditional rules for both topic marking and clitic-hood. I find that -ga is a topic marker according to Büring’s (2016) description of topic, and that it is also a clitic according to Spencer & Luís’s (2012a) criteria of canonical clitics. I also describe some of the most common functions and usages of -ga, such as its frequent occurrence in topic-switching questions and its role in marking the context of a phrase.

While many people enjoy hiking as a weekend activity, to many outdoor enthusiasts there is a hiking culture with which they feel affiliated. However, the way that these cultures interact with each other is still unclear. Exploring these different cultures and understanding how they relate to each other can help in engaging stakeholders of the trail. This is an important step toward finding ways to encourage environmentally friendly outdoor recreation practices and developing hiker-approved (and environmentally conscious) technologies to use on the trail. We explored these cultures by analyzing an extensive collection of tweets (over 1.5 million). We used topic modeling to identify the topics described by the communities of Triple Crown trails. We labeled training data for a classifier that identifies tweets relating to depreciative behaviors on the trail. Then, we compared the distribution of tweets across various depreciative trail behaviors to those of corresponding blog posts in order to see how tweets reflected cultures in comparison with blog posts. To harness metadata beyond the text of the tweets, we experimented with visualization techniques. We combined those efforts with ethnographic studies of hikers and conservancy organizations to produce this exploration of trail cultures. In this thesis, we show that through the use of natural language processing, we can identify cultural differences between trail communities. We identify the most significantly discussed forms of trail depreciation, which is helpful to conservation organizations so that they can more appropriately share which Leave No Trace practices hikers should place extra effort into practicing. / Master of Science / In a memoir of her hike on the Pacific Crest Trail, Wild, Cheryl Strayed said to a reporter in an amused tone, “I’m not a hobo, I’m a long-distance hiker”. While many people enjoy hiking as a weekend activity, to many outdoor enthusiasts there is a hiking culture with which they feel affiliated. There are cultures of trail conservation, and cultures of trail depreciation. There are cultures of long-distance hiking, and there are cultures of day hiking and weekend warrior hiking. There are also cultures across different hiking trails—where the hikers of one trail have different sets of values and behaviors than for another trail. However, the way that these cultures interact with each other is still unclear. Exploring these different cultures and understanding how they relate to each other can help in engaging stakeholders of the trail. This is an important step toward finding ways to encourage environmentally friendly outdoor recreation practices and developing hiker-approved (and environmentally conscious) technologies to use on the trail. We decided to explore these cultures by analyzing an extensive collection of tweets (over 1.5 million). We combined those expoorts with ethnographic style studies of conservancy organizations and avid hikers to produce this exploration of trail cultures.

Electronic Theses and Dissertations (ETDs) contain valuable scholarly information that can be of immense value to the scholarly community. Millions of ETDs are now publicly available online, often through one of many digital libraries. However, since a majority of these digital libraries are institutional repositories with the objective being content archiving, they often lack end-user services needed to make this valuable data useful for the scholarly community. To effectively utilize such data to address the information needs of users, digital libraries should support various end-user services such as document search and browsing, document recommendation, as well as services to make navigation of long PDF documents easier. In recent years, with advances in the field of machine learning for text data, several techniques have been proposed to support such end-user services. However, limited research has been conducted towards integrating such techniques with digital libraries. This research is aimed at building tools and techniques for discovering and accessing the knowledge buried in ETDs, as well as to support end-user services for digital libraries, such as document browsing and long document navigation. First, we review several machine learning models that can be used to support such services. Next, to support a comprehensive evaluation of different models, as well as to train models that are tailored to the ETD data, we introduce several new datasets from the ETD domain. To minimize the resources required to develop high quality training datasets required for supervised training, a novel AI-aided annotation method is also discussed. Finally, we propose techniques and frameworks to support the various digital library services such as search, browsing, and recommendation. The key contributions of this research are as follows: - A system to help with parsing long scholarly documents such as ETDs by means of object-detection methods trained to extract digital objects from long documents. The parsed documents can be used for further downstream tasks such as long document navigation, figure and/or table search, etc. - Datasets to support supervised training of object detection models on scholarly documents of multiple types, such as born-digital and scanned. In addition to manually annotated datasets, a framework (along with the resulting dataset) for AI-aided annotation also is proposed. - A web-based system for information extraction from long PDF theses and dissertations, into a structured format such as XML, aimed at making scholarly literature more accessible to users with disabilities. - A topic-modeling based framework to support exploration tasks such as searching and/or browsing documents (and document portions, e.g., chapters) by topic, document recommendation, topic recommendation, and describing temporal topic trends. / Doctor of Philosophy / Electronic Theses and Dissertations (ETDs) contain valuable scholarly information that can be of immense value to the research community. Millions of ETDs are now publicly available online, often through one of many online digital libraries. However, since a majority of these digital libraries are institutional repositories with the objective being content archiving, they often lack end-user services needed to make this valuable data useful for the scholarly community. To effectively utilize such data to address the information needs of users, digital libraries should support various end-user services such as document search and browsing, document recommendation, as well as services to make navigation of long PDF documents easier and accessible. Several advances in the field of machine learning for text data in recent years have led to the development of techniques that can serve as the backbone of such end-user services. However, limited research has been conducted towards integrating such techniques with digital libraries. This research is aimed at building tools and techniques for discovering and accessing the knowledge buried in ETDs, by parsing the information contained in the long PDF documents that make up ETDs, into a more compute-friendly format. This would enable researchers and developers to build end-user services for digital libraries. We also propose a framework to support document browsing and long document navigation, which are some of the important end-user services required in digital libraries.