GRASP: Rich Patterns for Argumentation Mining

Shnarch, Eyal; Levy, Ran; Raykar, Vikas C.; Slonim, Noam

doi:10.18653/v1/d17-1140

Cited by 9 publications

(13 citation statements)

References 15 publications

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“…We identify textual patterns associating posts with their actual or predicted labels. We do this using GrASP [20], an algorithm for extracting rich patterns from textual data. Specifically, we use the GrASP library from [13].…”

Section: Classifying the Postsmentioning

confidence: 99%

Explainable Patterns for Distinction and Prediction of Moral Judgement on Reddit

Efstathiadis¹,

Paulino-Passos²,

Toni³

2022

Preprint

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The forum r/AmITheAsshole in Reddit hosts discussion on moral issues based on concrete narratives presented by users. Existing analysis of the forum focuses on its comments, and does not make the underlying data publicly available. In this paper we build a new dataset of comments and also investigate the classification of the posts in the forum. Further, we identify textual patterns associated with the provocation of moral judgement by posts, with the expression of moral stance in comments, and with the decisions of trained classifiers of posts and comments.

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Section: Classifying the Postsmentioning

confidence: 99%

Explainable Patterns for Distinction and Prediction of Moral Judgement on Reddit

Efstathiadis¹,

Paulino-Passos²,

Toni³

2022

Preprint

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“…Hybrid approaches to text classification include the incorporation of lexical features into DL architectures (Koufakou et al, 2020;Pamungkas and Patti, 2019) and voting between rule-based and ML systems (Razavi et al, 2010;Gémes et al, 2021). The systems most similar to POTATO are the HEIDL (Sen et al, 2019) and GrASP (Lertvittayakumjorn et al, 2021;Shnarch et al, 2017) libraries, both of which support pattern-based text classification with automatic suggestions. Our tool differs from these in its use of syntactic and semantic graphs to represent text input, its ability to suggest refinements of rules specified by the user, and functionalities for working with data that has little or no ground truth annotation available.…”

Section: Related Workmentioning

confidence: 99%

POTATO: exPlainable infOrmation exTrAcTion framewOrk

Kovács¹,

Gémes²,

Iklódi³

et al. 2022

Preprint

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We present POTATO, a task-and languageindependent framework for human-in-the-loop (HITL) learning of rule-based text classifiers using graph-based features. POTATO handles any type of directed graph and supports parsing text into Abstract Meaning Representations (AMR), Universal Dependencies (UD), and 4lang semantic graphs. A streamlit-based user interface allows users to build rule systems from graph patterns, provides real-time evaluation based on ground truth data, and suggests rules by ranking graph features using interpretable machine learning models. Users can also provide patterns over graphs using regular expressions, and POTATO can recommend refinements of such rules. POTATO is applied in projects across domains and languages, including classification tasks on German legal text and English social media data. All components of our system are written in Python, can be installed via pip, and are released under an MIT License on GitHub.

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“…In Shnarch et al (2017 ), they presented an algorithm, named GrASP (Greedy Augmented Sequential Patterns), which was weak labeling of argumentative components using multilayer patterns. The algorithm produced highly indicative and expressive patterns by augmenting input n-grams with various layers of attributes, such as name entity recognition, domain knowledge, hypernyms.…”

Section: Background Knowledge and Related Workmentioning

confidence: 99%

Developing a Conversational Agent’s Capability to Identify Structural Wrongness in Arguments Based on Toulmin’s Model of Arguments

Mirzababaei

Pammer-Schindler

2021

Front. Artif. Intell.

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This article discusses the usefulness of Toulmin’s model of arguments as structuring an assessment of different types of wrongness in an argument. We discuss the usability of the model within a conversational agent that aims to support users to develop a good argument. Within the article, we present a study and the development of classifiers that identify the existence of structural components in a good argument, namely a claim, a warrant (underlying understanding), and evidence. Based on a dataset (three sub-datasets with 100, 1,026, 211 responses in each) in which users argue about the intelligence or non-intelligence of entities, we have developed classifiers for these components: The existence and direction (positive/negative) of claims can be detected a weighted average F1 score over all classes (positive/negative/unknown) of 0.91. The existence of a warrant (with warrant/without warrant) can be detected with a weighted F1 score over all classes of 0.88. The existence of evidence (with evidence/without evidence) can be detected with a weighted average F1 score of 0.80. We argue that these scores are high enough to be of use within a conditional dialogue structure based on Bloom’s taxonomy of learning; and show by argument an example conditional dialogue structure that allows us to conduct coherent learning conversations. While in our described experiments, we show how Toulmin’s model of arguments can be used to identify structural problems with argumentation, we also discuss how Toulmin’s model of arguments could be used in conjunction with content-wise assessment of the correctness especially of the evidence component to identify more complex types of wrongness in arguments, where argument components are not well aligned. Owing to having progress in argument mining and conversational agents, the next challenges could be the developing agents that support learning argumentation. These agents could identify more complex type of wrongness in arguments that result from wrong connections between argumentation components.

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GRASP: Rich Patterns for Argumentation Mining

Cited by 9 publications

References 15 publications

Explainable Patterns for Distinction and Prediction of Moral Judgement on Reddit

Explainable Patterns for Distinction and Prediction of Moral Judgement on Reddit

POTATO: exPlainable infOrmation exTrAcTion framewOrk

Developing a Conversational Agent’s Capability to Identify Structural Wrongness in Arguments Based on Toulmin’s Model of Arguments

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