1,720,985 research outputs found
Argumentative Evidences Classification and Argument Scheme Detection Using Tree Kernels
Get PDFpeer reviewedThe purpose of this study is to deploy a novel methodology for classifying different argumentative support (supporting evidences) in arguments, without considering the context. The proposed methodology is based on the idea that the use of Tree Kernel algorithms can be a good way to discriminate between different types of argumentative stances without the need of highly engineered features. This can be useful in different Argumentation Mining sub-tasks. This work provides an example of classifier built using a Tree Kernel method, which can discriminate between different kinds of argumentative support with a high accuracy. The ability to distinguish different kinds of support is, in fact, a key step toward Argument Scheme classification
Comparing Tree Kernels performances in argumentative evidence classification
Get PDFThe purpose of this study is to deploy a novel methodology for classifying argumentative support (or evidence) in arguments. The methodology shows that Tree Kernel can discriminate between different types of argumentative evidence with high scores, while keeping a good generalization. Moreover, the results of two different Tree Kernels are evaluated
Argumentation Schemes as Templates? Combining Bottom-up and Top-down Knowledge Representation
Get PDFThis paper describes a long-term research goal which aims at creating a middleware interface between Argumentation Schemes and natural language. This idea comes from the need to face some challenges related to the automatic extraction of Argumentation Schemes from Natural Language: for example the ability to extract Argumentation Schemes at different level of granularity. In the paper we describe how this process can be designed and how the structures of Argumentation Schemes can be modeled to this aim
Transfer Learning with Sentence Embeddings for Argumentative Evidence Classification
Get PDFThis work describes a simple Transfer Learning methodology aiming at discriminating evidences related to Argumentation Schemes using three different pre-trained neural architectures. Although Transfer Learning techniques are increasingly gaining momentum, the number of Transfer Learning works in the field of Argumentation Mining is relatively little and, to the best of our knowledge, no attempt has been performed towards the specific direction of discriminating evidences related to Argumentation Schemes. The research question of this paper is whether Transfer Learning can discriminate Argumentation Schemes’ components, a crucial yet rarely explored task in Argumentation Mining.
Results show that, even with small amount of data, classifiers trained on sentence embeddings extracted from pre-trained transformers can achieve encouraging scores, outperforming previous results on evidence classification
Detecting “Slippery Slope” and Other Argumentative Stances of Opposition Using Tree Kernels in Monologic Discourse
No full textThe aim of this study is to propose an innovative methodology to classify argumentative stances in a monologic argumentative context. Particularly, the proposed approach shows that Tree Kernels can be used in combination with traditional textual vectorization to discriminate between different stances of opposition without the need of extracting highly engineered features. This can be useful in many Argument Mining sub-tasks. In particular, this work explores the possibility of classifying opposition stances by training multiple classifiers to reach different degrees of granularity. Noticeably, discriminating support and opposition stances can be particularly useful when trying to detect Argument Schemes, one of the most challenging sub-task in the Argument Mining pipeline. In this sense, the approach can be also considered as an attempt to classify stances of opposition that are related to specific Argument Schemes
Uncertainty in Argumentation Schemes: Negative Consequences and Basic Slippery Slope
Get PDFThis study is an approach to encompass uncertainty in the well-known Argumentation Scheme from Negative Consequences and in the more recent “Basic Slippery Slope Argument” proposed by Douglas Walton. This work envisages two new kinds of uncertainty that should be taken into account, one related to time and one related to the material relation between premises and conclusion. Furthermore, it is argued that some modifications to the structure of these Argumentation Schemes or to their Critical Questions could facilitate the process of Knowledge Extraction and modeling from these two argumentative patterns. For example, the study suggests to change the premises of the Basic Slippery Slope related to the Control and the Loss of Control
Hybrid AI Framework for Legal Analysis of the EU Legislation Corrigenda
Get PDFThis paper presents an AI use-case developed in the project “Study on legislation in the era of artificial intelligence and digitization” promoted by the EU Commission Directorate-General for Informatics. We propose a hybrid technical framework where AI techniques, Data Analytics, Semantic Web approaches and LegalXML modelisation produce benefits in legal drafting activity. This paper aims to classify the corrigenda of the EU legislation with the goal to detect some criteria that could prevent errors during the drafting or during the publication process. We use a pipeline of different techniques combining AI, NLP, Data Analytics, Semantic annotation and LegalXML instruments for enriching the non-symbolic AI tools with legal knowledge interpretation to offer to the legal expert
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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