Argument Mining is a field of corpus-based discourse analysis that involves the automatic identification of argumentative structures in text.
Argument mining is often addressed by a pipeline method where segmentation of text into argumentative units is conducted first and proceeded by an argument component identification task.
Argument mining systems often consider contextual information, i. e. information outside of an argumentative discourse unit, when trained to accomplish tasks such as argument component identification, classification, and relation extraction.
Measuring the similarity between two different sentential arguments is an important task in argument mining.
We use a topic model to extract topic- and sentence-specific evidence from the structured knowledge base Wikidata, building a graph based on the cosine similarity between the entity word vectors of Wikidata and the vector of the given sentence.
Argument Mining is defined as the task of automatically identifying and extracting argumentative components (e. g., premises, claims, etc.)
The growing interest in argument mining and computational argumentation brings with it a plethora of Natural Language Understanding (NLU) tasks and corresponding datasets.
Our proposed model incorporates (i) task-specific parameterization (TSP) that effectively encodes a sequence of propositions and (ii) a proposition-level biaffine attention (PLBA) that can predict a non-tree argument consisting of edges.
This paper targets the automated extraction of components of argumentative information and their relations from natural language text.
Although Natural Language Processing (NLP) research on argument mining has advanced considerably in recent years, most studies draw on corpora of asynchronous and written texts, often produced by individuals.
To analyze persuasive strategies, it is important to understand how individuals construct posts and comments based on the semantics of the argumentative components.