Focusing Knowledge-based Graph Argument Mining via Topic Modeling

TL;DR

This paper introduces a hybrid graph-based model that leverages topic modeling, structured knowledge from Wikidata, and unstructured data from Google articles to improve sentence-level argument mining related to specific topics.

Contribution

It presents a novel combination of latent Dirichlet allocation and word embeddings to integrate structured and unstructured knowledge for argument classification.

Findings

The model effectively uses both Wikidata and Google articles for evidence extraction.

Combining structured and unstructured data improves argument classification accuracy.

The approach demonstrates the benefit of hybrid knowledge sources in argument mining.

Abstract

Decision-making usually takes five steps: identifying the problem, collecting data, extracting evidence, identifying pro and con arguments, and making decisions. Focusing on extracting evidence, this paper presents a hybrid model that combines latent Dirichlet allocation and word embeddings to obtain external knowledge from structured and unstructured data. We study the task of sentence-level argument mining, as arguments mostly require some degree of world knowledge to be identified and understood. Given a topic and a sentence, the goal is to classify whether a sentence represents an argument in regard to the topic. 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. Also, we build a second graph based on topic-specific articles found via Google to tackle the general incompleteness of structured knowledge bases. Combining these graphs, we obtain a graph-based model which, as our evaluation shows, successfully capitalizes on both structured and unstructured data.