Matches in SemOpenAlex for { <https://semopenalex.org/work/W4384698103> ?p ?o ?g. }
Showing items 1 to 59 of
59
with 100 items per page.
- W4384698103 abstract "Natural Language Processing (NLP) aims to utilize computational resources to comprehend and generate human language. Emotion and sentiment are integral parts of human beings, and they are often reflected in human language. Consequently, these two closely related ideas are of paramount importance to NLP. In this thesis, we focus on several NLP tasks related to human emotion and sentiment. Particularly, we focus on the domains of Sentiment Analysis and Emotion-Cause Analysis (ECA). Like most other NLP tasks, machine learning technologies are frequently leveraged to perform various NLP tasks in these two domains. A common challenge in applying machine learning technology to context-dependent tasks like Sentiment Analysis is that they require a large amount of labeled data to develop a performant model. In this thesis, we develop several techniques leveraging Transformer-based large language models (LLMs) to perform various NLP tasks within these two domains in a limited to no labeled data setting. Specifically, we devise two technical architectures to perform multi-class Sentiment Analysis with limited labeled data. We introduce two new NLP tasks within the domain of ECA, which are also the first Natural Language Generation (NLG) tasks in this domain. We devise technical solutions to perform these NLG tasks, one with limited labeled data, and the other with no labeled data. We publish a new dataset for one of these novel NLG tasks. Lastly, we propose leveraging conversational LLMs for the automatic evaluation of open-ended NLG tasks, which also does not require any new training or labeled data." @default.
- W4384698103 created "2023-07-20" @default.
- W4384698103 creator A5021332185 @default.
- W4384698103 date "2023-07-18" @default.
- W4384698103 modified "2023-09-23" @default.
- W4384698103 title "Human Emotion and Sentiment in Natural Language Understanding and Generation using Large Language Models with Limited to No Labeled Data" @default.
- W4384698103 doi "https://doi.org/10.22215/etd/2023-15505" @default.
- W4384698103 hasPublicationYear "2023" @default.
- W4384698103 type Work @default.
- W4384698103 citedByCount "0" @default.
- W4384698103 crossrefType "dissertation" @default.
- W4384698103 hasAuthorship W4384698103A5021332185 @default.
- W4384698103 hasBestOaLocation W43846981031 @default.
- W4384698103 hasConcept C120665830 @default.
- W4384698103 hasConcept C121332964 @default.
- W4384698103 hasConcept C134306372 @default.
- W4384698103 hasConcept C151730666 @default.
- W4384698103 hasConcept C154945302 @default.
- W4384698103 hasConcept C192209626 @default.
- W4384698103 hasConcept C195324797 @default.
- W4384698103 hasConcept C204321447 @default.
- W4384698103 hasConcept C2776187449 @default.
- W4384698103 hasConcept C2779343474 @default.
- W4384698103 hasConcept C33923547 @default.
- W4384698103 hasConcept C36503486 @default.
- W4384698103 hasConcept C41008148 @default.
- W4384698103 hasConcept C66402592 @default.
- W4384698103 hasConcept C86803240 @default.
- W4384698103 hasConceptScore W4384698103C120665830 @default.
- W4384698103 hasConceptScore W4384698103C121332964 @default.
- W4384698103 hasConceptScore W4384698103C134306372 @default.
- W4384698103 hasConceptScore W4384698103C151730666 @default.
- W4384698103 hasConceptScore W4384698103C154945302 @default.
- W4384698103 hasConceptScore W4384698103C192209626 @default.
- W4384698103 hasConceptScore W4384698103C195324797 @default.
- W4384698103 hasConceptScore W4384698103C204321447 @default.
- W4384698103 hasConceptScore W4384698103C2776187449 @default.
- W4384698103 hasConceptScore W4384698103C2779343474 @default.
- W4384698103 hasConceptScore W4384698103C33923547 @default.
- W4384698103 hasConceptScore W4384698103C36503486 @default.
- W4384698103 hasConceptScore W4384698103C41008148 @default.
- W4384698103 hasConceptScore W4384698103C66402592 @default.
- W4384698103 hasConceptScore W4384698103C86803240 @default.
- W4384698103 hasLocation W43846981031 @default.
- W4384698103 hasOpenAccess W4384698103 @default.
- W4384698103 hasPrimaryLocation W43846981031 @default.
- W4384698103 hasRelatedWork W159132833 @default.
- W4384698103 hasRelatedWork W2104232660 @default.
- W4384698103 hasRelatedWork W2293457016 @default.
- W4384698103 hasRelatedWork W2772769880 @default.
- W4384698103 hasRelatedWork W2883560263 @default.
- W4384698103 hasRelatedWork W2977842567 @default.
- W4384698103 hasRelatedWork W4225162041 @default.
- W4384698103 hasRelatedWork W87581401 @default.
- W4384698103 hasRelatedWork W91453536 @default.
- W4384698103 hasRelatedWork W1872130062 @default.
- W4384698103 isParatext "false" @default.
- W4384698103 isRetracted "false" @default.
- W4384698103 workType "dissertation" @default.