Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 8 of 8 for “"sequence labeling"”.
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A medication extraction framework for electronic health records
… decisions. We approach relation extraction as a sequence labeling task, where we label the context between the medications and the medical concepts that are involved in an administered-for relation. We use a Hidden Markov Model with conditional constraints for labeling the relation context. We …
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Domain-agnostic named entity recognition on unstructured text
… task from three different perspectives, namely, sequence labeling, question answering (QA), and span-based classification. We propose a simple span detection and classification pipeline that first detects all mention spans irrespective of entity type and then feeds each mention span as input to a …
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COVID-19 misinformation on Twitter: the role of deceptive support
… to be trustworthy, we extract its claim via a sequence labeling approach. In doing so, we seek to reduce the noise and highlight the informative parts of a Tweet. Instead of detecting erroneous and invalid information by analyzing the propagation patterns or ensuing examination of Tweets …
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A unified framework to identify and extract uncertainty cues, holders, and scopes in one fell-swoop
… swoop by casting each task as a supervised token sequence labeling problem. Third, I choose to work on the Arabic language, in contrast to English, the most commonly studied language in the literature of automatic uncertainty analysis. Finally, I work on the understudied linguistic genre of …
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Multilingual multitask joint neural information extraction
… Next, we explore the portability of models for sequence labeling, the underlying problem of many Natural Language Processing (NLP) tasks such as name tagging. Current models cannot be applied to very dissimilar settings (e.g., other languages), whereas annotating new data for all possible …
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Semi-supervised learning for acoustic and prosodic modeling in speech applications
… research on the use of unlabeled data in the sequence labeling problems. We develop lattice-based approaches for the model optimization that involves both transcribed and untranscribed speech utterances. Experiments for phone recognition show that a maximum mutual information criterion …
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Data quality in the deep learning era: Active semi-supervised learning and text normalization for natural language understanding
… of several semi-supervised methods for three sequence labeling tasks and two classification tasks. Additionally, most methods have assumptions that are less suitable to realistic scenarios. For example, proposed methods in the recent literature treat all unlabeled examples equally. Yet, in …