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Research Proposal in Adaptive Deep Learning with Topic Extraction for Argument Mining

Research Proposal in Adaptive Deep Learning with Topic Extraction for Argument Mining

  Argument mining aims to automatically identify and extract the structures of interpretation and reasoning behind the controversial conservation. Topic extraction plays important role in argument mining to determine the concept or subject of the conversation in an unsupervised manner. Argument mining with topic extraction is not sufficient to analyze the disputable interaction.

  Deep learning assists argument mining to capture the argumentative relation between the conversation due to its ability to process sequential information over a longer range. Adaptive deep learning supports the argument mining to identify the alteration and dynamic changes that occur in the controversial conservation. Adaptive deep learning with topic extraction for argument mining automatically generates effective solutions by understanding the interpretation and reasoning in arguable conversation.

  • In the developing field of argument mining, the collection of data with an argumentative context is a topic of interest in order to support decision making.

  • Topic extraction for argumentation mining significantly enhances the selection of pertinent properties among the arguments.

  • The sentence-level and token-level topic-focused argumentative information are extracted with the help of relational and fine-grained approaches.

  • Contexts from word embeddings, knowledge graph embeddings, and pre-trained models are integrated into argument mining with topic modeling for better decision-making.

  • The knowledge graph model for argument mining from structured and unstructured data through word embedding and topic modeling to discover more relevant paths.

  • Instigating deep learning for topic modeling-focused argument mining provides more suitable outcomes via the automatic acquisition of textual and perceptual knowledge.