# Focusing Knowledge-based Graph Argument Mining via Topic Modeling

arXiv: https://arxiv.org/abs/2102.02086 · HTML: https://mlsanity.com/paper/2102.02086/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 32 references that are in the map.
- First posted on arXiv 3 Feb 2021.

## Builds on (most cited first, 16 of 16)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge](https://mlsanity.com/paper/1811.00937/) (2018)
- [Teaching Machines to Read and Comprehend](https://mlsanity.com/paper/1506.03340/) (2015)
- [ERNIE: Enhanced Language Representation with Informative Entities](https://mlsanity.com/paper/1905.07129/) (2019)
- [K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters](https://mlsanity.com/paper/2002.01808/) (2020)
- [Knowledge Enhanced Contextual Word Representations](https://mlsanity.com/paper/1909.04164/) (2019)
- [A Knowledge-Enhanced Pretraining Model for Commonsense Story Generation](https://mlsanity.com/paper/2001.05139/) (2020)
- [Pretrained Encyclopedia: Weakly Supervised Knowledge-Pretrained Language Model](https://mlsanity.com/paper/1912.09637/) (2019)
- [SenseBERT: Driving Some Sense into BERT](https://mlsanity.com/paper/1908.05646/) (2019)
- [Graph-Based Reasoning over Heterogeneous External Knowledge for Commonsense Question Answering](https://mlsanity.com/paper/1909.05311/) (2019)
- [Cross-topic Argument Mining from Heterogeneous Sources Using Attention-based Neural Networks](https://mlsanity.com/paper/1802.05758/) (2018)
- [From 'F' to 'A' on the N.Y. Regents Science Exams: An Overview of the Aristo Project](https://mlsanity.com/paper/1909.01958/) (2019)
- [Integrating Graph Contextualized Knowledge into Pre-trained Language Models](https://mlsanity.com/paper/1912.00147/) (2019)
- [Enriching BERT with Knowledge Graph Embeddings for Document Classification](https://mlsanity.com/paper/1909.08402/) (2019)
- [TACAM: Topic And Context Aware Argument Mining](https://mlsanity.com/paper/1906.00923/) (2019)

## Built on by (most cited first, 3 of 3)

- [Modeling Associative Reasoning Processes](https://mlsanity.com/paper/2201.00716/) (2022)
- [Synergizing Machine Learning & Symbolic Methods: A Survey on Hybrid Approaches to Natural Language Processing](https://mlsanity.com/paper/2401.11972/) (2024)
- [Tethering Broken Themes: Aligning Neural Topic Models with Labels and Authors](https://mlsanity.com/paper/2410.18140/) (2024)

## Abstract (arXiv)

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.