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Full-Text Articles in Physical Sciences and Mathematics
Use Of Machine Learning And Natural Language Processing To Enhance Traffic Safety Analysis, Md Abu Sayed
Use Of Machine Learning And Natural Language Processing To Enhance Traffic Safety Analysis, Md Abu Sayed
Theses and Dissertations
Despite significant advances in vehicle technologies, safety data collection and analysis, and engineering advancements, tens of thousands of Americans die every year in motor vehicle crashes. Alarmingly, the trend of fatal and serious injury crashes appears to be heading in the wrong direction. In 2021, the actual rate of fatalities exceeded the predicted rate. This worrisome trend prompts and necessitates the development of advanced and holistic approaches to determining the causes of a crash (particularly fatal and major injuries). These approaches range from analyzing problems from multiple perspectives, utilizing available data sources, and employing the most suitable tools and technologies …
Innovative Heuristics To Improve The Latent Dirichlet Allocation Methodology For Textual Analysis And A New Modernized Topic Modeling Approach, Jamie T. Zimmerman
Innovative Heuristics To Improve The Latent Dirichlet Allocation Methodology For Textual Analysis And A New Modernized Topic Modeling Approach, Jamie T. Zimmerman
Theses and Dissertations
Natural Language Processing is a complex method of data mining the vast trove of documents created and made available every day. Topic modeling seeks to identify the topics within textual corpora with limited human input into the process to speed analysis. Current topic modeling techniques used in Natural Language Processing have limitations in the pre-processing steps. This dissertation studies topic modeling techniques, those limitations in the pre-processing, and introduces new algorithms to gain improvements from existing topic modeling techniques while being competitive with computational complexity. This research introduces four contributions to the field of Natural Language Processing and topic modeling. …
Language Learning Using Models Of Intentionality In Repeated Games With Cheap Talk, Jonathan Berry Skaggs
Language Learning Using Models Of Intentionality In Repeated Games With Cheap Talk, Jonathan Berry Skaggs
Theses and Dissertations
Language is critical to establishing long-term cooperative relationships among intelligent agents (including people), particularly when the agents' preferences are in conflict. In such scenarios, an agent uses speech to coordinate and negotiate behavior with its partner(s). While recent work has shown that neural language modeling can produce effective speech agents, such algorithms typically only accept previous text as input. However, in relationships among intelligent agents, not all relevant context is expressed in conversation. Thus, in this paper, we propose and analyze an algorithm, called Llumi, that incorporates other forms of context to learn to speak in long-term relationships modeled as …
Incorporating Spatial Relationship Information In Signal-To-Text Processing, Jeremy Elon Davis
Incorporating Spatial Relationship Information In Signal-To-Text Processing, Jeremy Elon Davis
Theses and Dissertations
This dissertation outlines the development of a signal-to-text system that incorporates spatial relationship information to generate scene descriptions. Existing signal-to-text systems generate accurate descriptions in regards to information contained in an image. However, to date, no signalto- text system incorporates spatial relationship information. A survey of related work in the fields of object detection, signal-to-text, and spatial relationships in images is presented first. Three methodologies followed by evaluations were conducted in order to create the signal-to-text system: 1) generation of object localization results from a set of input images, 2) derivation of Level One Summaries from an input image, and …
Symbolic Semantic Memory In Transformer Language Models, Robert Kenneth Morain
Symbolic Semantic Memory In Transformer Language Models, Robert Kenneth Morain
Theses and Dissertations
This paper demonstrates how transformer language models can be improved by giving them access to relevant structured data extracted from a knowledge base. The knowledge base preparation process and modifications to transformer models are explained. We evaluate these methods on language modeling and question answering tasks. These results show that even simple additional knowledge augmentation leads to a reduction in validation loss by 73%. These methods also significantly outperform common ways of improving language models such as increasing the model size or adding more data.
Improving Anonymized Search Relevance With Natural Language Processing And Machine Learning, Niko A. Petrocelli
Improving Anonymized Search Relevance With Natural Language Processing And Machine Learning, Niko A. Petrocelli
Theses and Dissertations
Users often sacrifice personal data for more relevant search results, presenting a problem to communities that desire both search anonymity and relevant results. To balance these priorities, this research examines the impact of using Siamese networks to extend word embeddings into document embeddings and detect similarities between documents. The predicted similarity can locally re-rank search results provided from various sources. This technique is leveraged to limit the amount of information collected from a user by a search engine. A prototype is produced by applying the methodology in a real-world search environment. The prototype yielded an additional function of finding new …
Incorporating Ontological Information In Biomedical Entity Linking Of Phrases In Clinical Text, Evan French
Incorporating Ontological Information In Biomedical Entity Linking Of Phrases In Clinical Text, Evan French
Theses and Dissertations
Biomedical Entity Linking (BEL) is the task of mapping spans of text within biomedical documents to normalized, unique identifiers within an ontology. Translational application of BEL on clinical notes has enormous potential for augmenting discretely captured data in electronic health records, but the existing paradigm for evaluating BEL systems developed in academia is not well aligned with real-world use cases. In this work, we demonstrate a proof of concept for incorporating ontological similarity into the training and evaluation of BEL systems to begin to rectify this misalignment. This thesis has two primary components: 1) a comprehensive literature review and 2) …
Temporal Disambiguation Of Relative Temporal Expressions In Clinical Texts Using Temporally Fine-Tuned Contextual Word Embeddings., Amy L. Olex
Theses and Dissertations
Temporal reasoning is the ability to extract and assimilate temporal information to reconstruct a series of events such that they can be reasoned over to answer questions involving time. Temporal reasoning in the clinical domain is challenging due to specialized medical terms and nomenclature, shorthand notation, fragmented text, a variety of writing styles used by different medical units, redundancy of information that has to be reconciled, and an increased number of temporal references as compared to general domain texts. Work in the area of clinical temporal reasoning has progressed, but the current state-of-the-art still has a ways to go before …