Identifying Argument Components through TextRank

Petasis, Georgios; Karkaletsis, Vangelis

doi:10.18653/v1/w16-2811

Cited by 21 publications

(15 citation statements)

References 19 publications

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“…TextRank, on the other hand, takes into account the lexical meaning of the text unit, as well [26,27]. In [28], TextRank is extended in an unsupervised extractive summarization scheme that can examine whether there is any potential overlap between the extractive summarization and argument mining, while in [29], a system that applies a series of syntactic filters to identify part-of-speech tags is described, that is used to evaluate selected words as possible keywords.…”

Section: Keyword Extractionmentioning

confidence: 99%

A Geolocation Analytics-Driven Ontology for Short-Term Leases: Inferring Current Sharing Economy Trends

2020

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Short-term property rentals are perhaps one of the most common traits of present day shared economy. Moreover, they are acknowledged as a major driving force behind changes in urban landscapes, ranging from established metropolises to developing townships, as well as a facilitator of geographical mobility. A geolocation ontology is a high level inference tool, typically represented as a labeled graph, for discovering latent patterns from a plethora of unstructured and multimodal data. In this work, a two-step methodological framework is proposed, where the results of various geolocation analyses, important in their own respect, such as ghost hotel discovery, form intermediate building blocks towards an enriched knowledge graph. The outlined methodology is validated upon data crawled from the Airbnb website and more specifically, on keywords extracted from comments made by users of the said platform. A rather solid case-study, based on the aforementioned type of data regarding Athens, Greece, is addressed in detail, studying the different degrees of expansion & prevalence of the phenomenon among the city’s various neighborhoods.

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Section: Keyword Extractionmentioning

confidence: 99%

A Geolocation Analytics-Driven Ontology for Short-Term Leases: Inferring Current Sharing Economy Trends

2020

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“…In (Petasis and Karkaletsis, 2016), the score was determined for each sentence, and the method was evaluated correct if the top one or two sentences according to the score includes the target major claim or claim.…”

Section: Component Classification Using Textrankmentioning

confidence: 99%

Argument Component Classification by Relation Identification by Neural Network and TextRank

Deguchi¹,

Yamaguchi²

2019

Proceedings of the 6th Workshop on Argument Mining

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In recent years, argumentation mining, which automatically extracts the structure of argumentation from unstructured documents such as essays and debates, is gaining attention. For argumentation mining applications, argumentcomponent classification is an important subtask. The existing methods can be classified into supervised methods and unsupervised methods. Many existing supervised methods use a classifier to identify the roles of argument components, such as claim or premise , but many of them use information of a single sentence without relying on the whole document. On the other hand, existing unsupervised document classification has the advantage of being able to use the whole document, but accuracy of these methods is not so high. In this paper, we propose a method for argument-component classification that combines relation identification by neural networks and TextRank to integrate relation informations (i.e. the strength of the relation). This method can use argumentation-specific knowledge by employing supervised learning on a corpus while maintaining the advantage of using the whole document. Experiments on two corpora, one consisting of student essays and the other of Wikipedia articles, show the effectiveness of this method.

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“…For Divisiveness, we then look at the sentiment polarity of each text span compared to the rest of the corpus to measure how many others are in conflict with it and the amount of support which the two sides have. TextRank has been successfully applied to many natural language processing applications, including identifying those parts of a text which are argumentative (as opposed to those which are not) (Petasis and Karkaletsis, 2016).…”

Section: Related Workmentioning

confidence: 99%

Using Complex Argumentative Interactions to Reconstruct the Argumentative Structure of Large-Scale Debates

Lawrence

Reed

2017

Proceedings of the 4th Workshop on Argument Mining

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In this paper we consider the insights that can be gained by considering large scale argument networks and the complex interactions between their constituent propositions. We investigate metrics for analysing properties of these networks, illustrating these using a corpus of arguments taken from the 2016 US Presidential Debates. We present techniques for determining these features directly from natural language text and show that there is a strong correlation between these automatically identified features and the argumentative structure contained within the text. Finally, we combine these metrics with argument mining techniques and show how the identification of argumentative relations can be improved by considering the larger context in which they occur.

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Identifying Argument Components through TextRank

Cited by 21 publications

References 19 publications

A Geolocation Analytics-Driven Ontology for Short-Term Leases: Inferring Current Sharing Economy Trends

A Geolocation Analytics-Driven Ontology for Short-Term Leases: Inferring Current Sharing Economy Trends

Argument Component Classification by Relation Identification by Neural Network and TextRank

Using Complex Argumentative Interactions to Reconstruct the Argumentative Structure of Large-Scale Debates

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