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Argument mining studies in natural language text often use lexical (e.g. n-grams) and syn- tactic (e.g. grammatical production rules) fea- tures with all possible values. In prior work on a …...

AbstractGovernments are increasingly utilising online platforms in order to engage with, and ascertain the opinions of, their citizens. Whilst policy makers could potentially benefit from such enormous feedback from society, …...

AbstractGiven a controversial issue, argument mining from natural language texts (news papers, and any form of text on the Internet) is extremely challenging: domain knowledge is often required together with …...

AbstractThe problem of understanding the stream of messages exchanged on social media such as Facebook and Twitter is becoming a major challenge for automated systems. The tremendous amount of data …...

AbstractArgument mining aims to determine the argumentative structure of texts. Although it is said to be crucial for future applications such as writing support systems, the benefit of its output …...

AbstractFuture search engines are expected to deliver pro and con arguments in response to queries on controversial topics. While argument mining is now in the focus of research, the question …...

AbstractSocial media collect and spread on the Web personal opinions, facts, fake news and all kind of information users may be interested in. Applying argument mining methods to such heterogeneous …...

AbstractWe propose a novel factor graph model for argument mining, designed for settings in which the argumentative relations in a document do not necessarily form a tree structure. (This is …...

AbstractIn order to determine argument structure in text, one must understand how individual components of the overall argument are linked. This work presents the first neural network-based approach to link …...

AbstractWe propose a deep learning architecture to capture argumentative relations of attack and support from one piece of text to another, of the kind that naturally occur in a debate. …...

AbstractArgument mining has become a popular research area in NLP. It typically includes the identification of argumentative components, e.g. claims, as the central component of an argument. We perform a …...

We propose ESCRITO, a toolkit for scoring student writings using NLP techniques that addresses two main user groups: teachers and NLP researchers. Teachers can use a high-level API in the …...

In this abstract we present a methodology to improve Argument annotation guidelines by exploiting inter-annotator agreement measures. After a first stage of the annotation effort, we have detected problematic issues …...

Computational argumentation aims to model arguments as a set of premises that either support each other or collectively support a conclusion. We prepare three datasets of text-hypothesis pairs with support-based …...

AbstractSearching for sentences containing claims in a large text corpus is a key component in developing an argumentative content search engine. Previous works focused on detecting claims in a small …...

AbstractPeer-review plays a critical role in the scientific writing and publication ecosystem. To assess the efficiency and efficacy of the reviewing process, one essential element is to understand and evaluate …...

AbstractPolitical debates offer a rare opportunity for citizens to compare the candidates’ positions on the most controversial topics of the campaign. Thus they represent a natural application scenario for Argument …...

Identifying argument components has become an important area of research in argument mining. When argument components are identified, they can not only be used for stance classification but also can …...

AbstractArgument mining systems often consider contextual information, i.e. information outside of an argumentative discourse unit, when trained to accomplish tasks such as argument component identification, classification, and relation extraction. However, …...

AbstractArgumentation accommodates various rhetorical devices, such as questions, reported speech, and imperatives. These rhetorical tools usually assert argumentatively relevant propositions rather implicitly, so understanding their true meaning is key to …...

AbstractThe growing interest in argument mining and computational argumentation brings with it a plethora of Natural Language Understanding (NLU) tasks and corresponding datasets. However, as with many other NLU tasks, …...

AbstractAlthough NLP research on argument mining has advanced considerably in recent years, most studies draw on corpora of asynchronous and written texts, often produced by individuals. Few published corpora of …...

AbstractAtomic clauses are fundamental text units for understanding complex sentences. Identifying the atomic sentences within complex sentences is important for applications such as summarization, argument mining, discourse analysis, discourse parsing, …...

AbstractWhile online conversations can cover a vast amount of information in many different formats, abstractive text summarization has primarily focused on modeling solely news articles. This research gap is due, …...

AbstractNon-neural approaches to argument mining (AM) are often pipelined and require heavy feature-engineering. In this paper, we propose a neural end-to-end approach to AM which is based on dependency parsing, …...

AbstractThis survey builds an interdisciplinary picture of Argument Mining (AM), with a strong focus on its potential to address issues related to Social and Political Science. More specifically, we focus …...

AbstractArgument mining (AM) is a challenging task as it requires recognizing the complex argumentation structures involving multiple subtasks. To handle all subtasks of AM in an end-to-end fashion, previous works …...

AbstractInterest in argument mining has resulted in an increasing number of argument annotated corpora. However, most focus on English texts with explicit argumentative discourse markers, such as persuasive essays or …...

AbstractIdentifying argument components from unstructured texts and predicting the relationships expressed among them are two primary steps of argument mining. The intrinsic complexity of these tasks demands powerful learning models. …...

AbstractWhile neural approaches to argument mining (AM) have advanced considerably, most of the recent work has been limited to parsing monologues. With an urgent interest in the use of conversational …...

AbstractMining an argument structure from text is an important step for tasks such as argument search and summarization. While studies on argument(ation) mining have proposed promising neural network models, they …...

AbstractData exploration is an important step of every data science and machine learning project, including those involving textual data. We provide a novel language tool, in the form of a …...

AbstractGRhOOT, the German RhetOrical OnTology, is a domain ontology of 110 rhetorical figures in the German language. The overall goal of building an ontology of rhetorical figures in German is …...

AbstractThe goal of text zoning is to segment a text into zones (i.e., Background, Conclusion) that serve distinct functions. Argumentative zoning, a specific text zoning scheme for the scientific domain, …...

While argument mining has garnered attention over the years, its application in the financial sector remains nascent. This study presents a BERT-based ensemble learning approach tailored for sentiment analysis grounded …...

AbstractWhen we interact with other humans, humanvalues guide us to consider the human element. As we shall see, value analysis in NLP hasbeen applied to personality profiling but not toargument …...

AbstractArgument mining seeks to extract arguments and their structure from unstructured texts. Identifying relations between arguments (such as attack, support, and neutral) is a challenging task because two arguments may …...