Argument Extraction from News

Sardianos, Christos; Katakis, Ioannis Manousos; Petasis, Georgios; Karkaletsis, Vangelis

doi:10.3115/v1/w15-0508

Cited by 53 publications

(35 citation statements)

References 18 publications

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“…The big potential of argument extraction in political textual data has attracted many researchers in text mining. For instance, Sardianos et al [43] proposed a supervised technique, based on conditional random fields, to extract arguments and their underlying facts. They applied the method to web pages containing news and tagged speeches.…”

Section: Arguments Extractionmentioning

confidence: 99%

“…As the number of digital documents has grown exponentially, almost on any topic, automated knowledge extraction methods have become popular in many sections, from science to marketing and business. Many text mining techniques deal with extracting knowledge authors represented in their texts, e.g., extracting arguments [43][44][45][46] and extracting opinions [13][14][15][16]. Knowledge extraction is usually concerned with finding and extracting the key elements from textual data (e.g., articles, short notes, tweets, blogs, etc.)…”

Section: Knowledge Extractionmentioning

confidence: 99%

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Text Mining in Big Data Analytics

Hassani

Beneki

Unger

et al. 2020

BDCC

136

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Text mining in big data analytics is emerging as a powerful tool for harnessing the power of unstructured textual data by analyzing it to extract new knowledge and to identify significant patterns and correlations hidden in the data. This study seeks to determine the state of text mining research by examining the developments within published literature over past years and provide valuable insights for practitioners and researchers on the predominant trends, methods, and applications of text mining research. In accordance with this, more than 200 academic journal articles on the subject are included and discussed in this review; the state-of-the-art text mining approaches and techniques used for analyzing transcripts and speeches, meeting transcripts, and academic journal articles, as well as websites, emails, blogs, and social media platforms, across a broad range of application areas are also investigated. Additionally, the benefits and challenges related to text mining are also briefly outlined.

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Section: Arguments Extractionmentioning

confidence: 99%

Section: Knowledge Extractionmentioning

confidence: 99%

Text Mining in Big Data Analytics

Hassani

Beneki

Unger

et al. 2020

BDCC

136

View full text Add to dashboard Cite

show abstract

“…Winkels et al 2013 [20] were among the first who experimented with unsupervised techniques in Argumentation Mining. Their results showed that pure unsupervised clustering does not yield satisfactory results [21], and Habernal and Gurevych 2015 [22] employed the new idea of word embeddings in a semi-supervised fashion for the argument identification subtask. In some cases, the results yielded a 100% improvement over previous attempts on complex online corpora.…”

Section: Related Work On Argument Identificationmentioning

confidence: 99%

Unlocking Transfer Learning in Argumentation Mining: A Domain-Independent Modelling Approach

Wambsganss¹,

Molyndris²,

Söllner³

2020

WI2020 Zentrale Tracks

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Argument identification is the fundamental block of every Argumentation Mining pipeline, which in turn is a young upcoming field with multiple applications ranging from strategy support to opinion mining and news fact-checking. We developed a model, which is tackling the two biggest practical and academic challenges of the research field today. First, it addresses the lack of corpus-agnostic models and, second, it tackles the problem of human-laborintensive NLP models being costly to develop. We do that by suggesting and implementing an easy-to-use solution that utilizes the latest advancements in natural language Transfer Learning. The result is a twofold contribution: A system that delivers state-of-the-art results in multiple corpora and opens up a new way of academic advancement of the field through Transfer Learning. Additionally, it provides the architecture for an easy-to-use tool that can be used for practical applications without the need for domain-specific knowledge.

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“…[66,82,83,94], persuasive essays, e.g. [152,153,201,203,205], and social media [81,155,191]. Despite these, and other, developments, only few attempts, such as [204], appear to have been made at fully automating the process of building AFs from text.…”

Section: News Articles Corpusmentioning

confidence: 99%

Using Argumentation to Improve Classification in Natural Language Problems

Carstens

Toni

2017

ACM Trans. Internet Technol.

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Argumentation has proven successful in a number of domains, including multi-agent systems and decision support in medicine and engineering. We propose its application to a domain yet largely unexplored by argumentation research: computational linguistics. Over the past decade or so advances in this field have commonly relied on data-driven solutions, i.e. machine learning. Recently, however, there appears to be a growing consent that, in order to achieve significant advances in certain areas of artificial intelligence, in general, and computational linguistics, in particular, we may need to consider data-driven approaches in unison with reasoning-, logic-and rule-based solutions. To this end we have developed a novel classification methodology that incorporates reasoning through argumentation with supervised classifiers. We train classifiers and then argue about the validity of their output.To do so we identify arguments that formalise prototypical knowledge of a problem and use them to correct misclassifications. We thus have at our disposal multiple ways of incorporating knowledge in classification and are able to integrate knowledge as it becomes available, without the need to retrain classifiers.We illustrate our methodology on two tasks. On the one hand we address binary cross-domain sentiment polarity classification, where we train classifiers on one corpus, e.g. Tweets, to identify positive/negative polarity, and classify instances from another corpus, e.g. sentences from movie reviews. On the other hand we address a form of argumentation mining that we call Relation-based Argumentation Mining, where we classify pairs of sentences based on whether the first sentence attacks or supports the second, or whether it does neither. Whenever we find that one sentence attacks/supports the other we consider both to be argumentative, irrespective of their stand-alone argumentativeness. For both tasks we improve classification performance when using our methodology, compared to using standard classifiers, only.3

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Argument Extraction from News

Cited by 53 publications

References 18 publications

Text Mining in Big Data Analytics

Text Mining in Big Data Analytics

Unlocking Transfer Learning in Argumentation Mining: A Domain-Independent Modelling Approach

Using Argumentation to Improve Classification in Natural Language Problems

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