  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.
