On the Retrieval of Wikipedia Articles Containing Claims on Controversial Topics

Roitman, Haggai; Hummel, Shay; Rabinovich, Ella; Sznajder, Benjamin; Slonim, Noam; Aharoni, Ehud

doi:10.1145/2872518.2891115

Cited by 19 publications

(20 citation statements)

References 16 publications

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“…Besides the attack/support relation between a tweet and a topic, the authors also examined the relatedness of a topic towards the pre-defined topic and the existence or not of an arguable reason, where the evaluation of the argument toward its completeness take place. Considering the complexity of the proposed methodology and the comparison with state-of-the-art baselines [68,84,85], the presented results that can be characterized as promising. The entire process is characterized as information retrieval task, thus the learning-to-rank approach was adopted and the metric precision at k was used, which indicates the precision among the k top results of the retrieval.…”

Section: Relations Identificationmentioning

confidence: 86%

The evolution of argumentation mining: From models to social media and emerging tools

Lytos

Λάγκας²,

Sarigiannidis

et al. 2019

Information Processing & Management

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Argumentation mining is a rising subject in the computational linguistics domain focusing on extracting structured arguments from natural text, often from unstructured or noisy text. The initial approaches on modeling arguments was aiming to identify a flawless argument on specific fields (Law, Scientific Papers) serving specific needs (completeness, effectiveness). With the emerge of Web 2.0 and the explosion in the use of social media both the diffusion of the data and the argument structure have changed. In this survey article, we bridge the gap between theoretical approaches of argumentation mining and pragmatic schemes that satisfy the needs of social media generated data, recognizing the need for adapting more flexible and expandable schemes, capable to adjust to the argumentation conditions that exist in social media. We review, compare, and classify existing approaches, techniques and tools, identifying the positive outcome of combining tasks and features, and eventually propose a conceptual architecture framework. The proposed theoretical framework is an argumentation mining scheme able to identify the distinct sub-tasks and capture the needs of social media text, revealing the need for adopting more flexible and extensible frameworks.

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Section: Relations Identificationmentioning

confidence: 86%

The evolution of argumentation mining: From models to social media and emerging tools

Lytos

Λάγκας²,

Sarigiannidis

et al. 2019

Information Processing & Management

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“…There also have been a few attempts to detect controversial content with lexicons. Roitman et al retrieve Wikipedia articles with claims about controversial query topics [18], and Mejova et al use crowdsourcing to label controversial words [14].…”

Section: Related Work and Backgroundmentioning

confidence: 99%

“…Controversy-indicative terms: Documents that are retrieved by a query believed to indicate controversy. We explore Cramer's terms [5] as well as manual lexicons from past work [14,18].…”

Section: Controversy Language Modelsmentioning

confidence: 99%

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Probabilistic Approaches to Controversy Detection

Jang

Foley

Dori-Hacohen

et al. 2016

Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

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Recently, the problem of automated controversy detection has attracted a lot of interest in the information retrieval community. Existing approaches to this problem have set forth a number of detection algorithms, but there has been little effort to model the probability of controversy in a document directly. In this paper, we propose a probabilistic framework to detect controversy on the web, and investigate two models. We first recast a state-of-the-art controversy detection algorithm into a model in our framework. Based on insights from social science research, we also introduce a language modeling approach to this problem. We evaluate different methods of creating controversy language models based on a diverse set of public datasets including Wikipedia, Web and News corpora. Our automatically derived language models show a significant relative improvement of 18% in AUC over prior work, and 23% over two manually curated lexicons.

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“…This paradigm has two limitations. First, it relies on a manual, or automatic (Roitman et al, 2016), process to retrieve the relevant set of articles, which is non-trivial and prone to errors. In addition, when considering large corpora, relevant claims may spread across a much wider and diverse set of articles compared to those considered by earlier works.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised corpus–wide claim detection

Levy

Gretz

Sznajder

et al. 2017

Proceedings of the 4th Workshop on Argument Mining

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Automatic claim detection is a fundamental argument mining task that aims to automatically mine claims regarding a topic of consideration. Previous works on mining argumentative content have assumed that a set of relevant documents is given in advance. Here, we present a first corpuswide claim detection framework, that can be directly applied to massive corpora. Using simple and intuitive empirical observations, we derive a claim sentence query by which we are able to directly retrieve sentences in which the prior probability to include topic-relevant claims is greatly enhanced. Next, we employ simple heuristics to rank the sentences, leading to an unsupervised corpus-wide claim detection system, with precision that outperforms previously reported results on the task of claim detection given relevant documents and labeled data.

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On the Retrieval of Wikipedia Articles Containing Claims on Controversial Topics

Cited by 19 publications

References 16 publications

The evolution of argumentation mining: From models to social media and emerging tools

The evolution of argumentation mining: From models to social media and emerging tools

Probabilistic Approaches to Controversy Detection

Unsupervised corpus–wide claim detection

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