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Exploiting Background Knowledge for Argumentative Relation Classification

Jonathan Kobbe, Juri Opitz, Maria Becker, Ioana Hulpus, Heiner Stuckenschmidt, Anette Frank, Michael Wagner
2019 International Conference on Language, Data, and Knowledge  
model for argumentative relation classification.  ...  We propose an argumentative relation classification system that employs linguistic as well as knowledge-based features, and investigate the effects of injecting background knowledge into a neural baseline  ...  Acknowledgements We gratefully acknowledge the support of NVIDIA Corporation through a donation of GPUs that were used for this research.  ... 
doi:10.4230/oasics.ldk.2019.8 dblp:conf/ldk/KobbeOBHSF19 fatcat:etbraev7lfavnjc7wflkselgsi

Argumentation Mining: Exploiting Multiple Sources and Background Knowledge [article]

Anastasios Lytos, Thomas Lagkas, Panagiotis Sarigiannidis, Kalina Bontcheva
2018 arXiv   pre-print
Argumentation Mining.  ...  The recent progress in the wider field of Artificial Intelligence in combination with the available data through Social Web has create great potential for every sub-field of Natural Language Process including  ...  Exploiting and Adapting Background Knowledge Apart from the combination of multiple sources the second challenge we try to answer is the constant generation and adaptation of the background knowledge of  ... 
arXiv:1809.06943v1 fatcat:gcgepa6havfpxnbceve7ltpebm

Explaining Arguments with Background Knowledge

Maria Becker, Ioana Hulpuş, Juri Opitz, Debjit Paul, Jonathan Kobbe, Heiner Stuckenschmidt, Anette Frank
2020 Datenbank-Spektrum  
This requires human background knowledge and reasoning capacity, in order to explicate the complete reasoning of an argument.  ...  A particular challenge for such a system is to uncover implicit knowledge which many arguments rely on.  ...  Background Knowledge for Coarsegrained Argumentative Relation Classification Issues with state of the art systems lacking deeper understanding of argumentative relations Consider again Argument 1.  ... 
doi:10.1007/s13222-020-00348-6 fatcat:zd55bxjr7bhs5ab5whi3ih4q4y

Towards Explaining Natural Language Arguments with Background Knowledge

Ioana Hulpus, Jonathan Kobbe, Christian Meilicke, Heiner Stuckenschmidt, Maria Becker, Juri Opitz, Vivi Nastase, Anette Frank
2019 International Semantic Web Conference  
In this paper, we propose the task of argument explicitation, a task that makes the structure of a natural language argument explicit, as well as the background knowledge the argument is built on, in the  ...  We propose a framework for argument explicitation that joins a variety of AI and NLPbased argumentation mining sub-tasks that by now have mostly been treated separately in the literature.  ...  Acknowledgments This work has been funded by the Deutsche Forschungsgemeinschaft (DFG) within the project ExpLAIN, Grant Number STU 266/14-1 and FR 1707/-4-1, as part of the Priority Program "Robust Argumentation  ... 
dblp:conf/semweb/HulpusKMSBONF19 fatcat:nphdivvk4nbddnozrd6zspgaue

Neural-Symbolic Argumentation Mining: An Argument in Favor of Deep Learning and Reasoning

Andrea Galassi, Kristian Kersting, Marco Lippi, Xiaoting Shao, Paolo Torroni
2020 Frontiers in Big Data  
Deep learning is bringing remarkable contributions to the field of argumentation mining, but the existing approaches still need to fill the gap toward performing advanced reasoning tasks.  ...  In this position paper, we posit that neural-symbolic and statistical relational learning could play a crucial role in the integration of symbolic and sub-symbolic methods to achieve this goal.  ...  ACKNOWLEDGMENTS We thank the reviewers for their comments and contributions, which have increased the quality of this work.  ... 
doi:10.3389/fdata.2019.00052 pmid:33693375 pmcid:PMC7931943 fatcat:mtp5xigtlndwvnz7olwcpvd6na

Argumentative Relation Classification with Background Knowledge

Debjit Paul, Juri Opitz, Maria Becker, Jonathan Kobbe, Graeme Hirst, Anette Frank
2020 Computational Models of Argument  
A common conception is that the understanding of relations that hold between argument units requires knowledge beyond the text.  ...  This knowledge is integrated into a neural argumentative relation classifier via an attention-based gating mechanism.  ...  Paul et al. / Argumentative Relation Classification with Background Knowledge http://www.debatepedia.org D. Paul et al. / Argumentative Relation Classification with Background Knowledge  ... 
doi:10.3233/faia200515 dblp:conf/comma/PaulOBKHF20 fatcat:n2wqqkaoynalpn6dcxtlr4mw3m

Relational and Fine-Grained Argument Mining

Dietrich Trautmann, Michael Fromm, Volker Tresp, Thomas Seidl, Hinrich Schütze
2020 Datenbank-Spektrum  
The main part of the article describes our research on argument mining, both coarse-grained and fine-grained methods, and on same-side stance classification, a relational approach to the problem of stance  ...  classification.  ...  With our improved argument mining techniques and based on our relational framework for stance classification, we would like to exploit graphs for argument validation.  ... 
doi:10.1007/s13222-020-00341-z fatcat:lshyjkqhobeyvhjph43eqtj3l4

Leveraging cognitive context knowledge for argumentation based object classification in multi-sensor networks

Zhiyong Hao, Junfeng Wu, Tingting Liu, Xiaohong Chen
2019 IEEE Access  
To address this category of granularity inconsistent problem in multi-sensor collaborative object classification tasks, we propose a cognitive context knowledge-enriched method for classification conflict  ...  The cognitive context is concerned, in this paper, to investigate how rich contextual knowledge-equipped cognitive agents can facilitate semantic consensus in argumentation-based object classification.  ...  This robust object classification for noisy sensor data is due to the shared background knowledge advantages offered by the cognitive context enriched method in argumentation based object classification  ... 
doi:10.1109/access.2019.2919073 fatcat:azmkmxylcneppjga45mct7nmcq

Working Memory-Driven Neural Networks with a Novel Knowledge Enhancement Paradigm for Implicit Discourse Relation Recognition

Fengyu Guo, Ruifang He, Jianwu Dang, Jian Wang
2020 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
Recognizing implicit discourse relation is a challenging task in discourse analysis, which aims to understand and infer the latent relations between two discourse arguments, such as temporal, comparison  ...  While implicitly stated knowledge in the arguments is retrieved from external knowledge source and encoded as inter-words semantic connection embeddings to further construct knowledge matrix, as long-term  ...  Acknowledgments We thank the anonymous reviewers for their valuable feedback. Our work is supported by the National Natural Science  ... 
doi:10.1609/aaai.v34i05.6287 fatcat:smjklclp2ncibbmqashawugnfe

A Survey of Implicit Discourse Relation Recognition [article]

Wei Xiang, Bang Wang
2022 arXiv   pre-print
Finally, we discuss future research directions for discourse relation analysis.  ...  Although sometimes a connective exists in raw texts for conveying relations, it is more often the cases that no connective exists in between two text segments but some implicit relation does exist in between  ...  the argument-pair level for relation classification.  ... 
arXiv:2203.02982v1 fatcat:ubublxw2fnfdpexgw4jslj76tm

Knowledge and Metadata Integration for Warehousing Complex Data [article]

Jean-Christian Ralaivao
2008 arXiv   pre-print
Warehousing and analyzing such data requires the joint exploitation of metadata and domain-related knowledge, which must thereby be integrated.  ...  In this paper, we survey the types of knowledge and metadata that are needed for managing complex data, discuss the issue of knowledge and metadata integration, and propose a CWM-compliant integration  ...  It is difficult for classical architectures to manage complex data without domain-related knowledge nor background knowledge.  ... 
arXiv:0809.1971v1 fatcat:47oml72nu5h6zbp5qyzjvusjdy

Why are You Taking this Stance? Identifying and Classifying Reasons in Ideological Debates

Kazi Saidul Hasan, Vincent Ng
2014 Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)  
for reason classification.  ...  Given the close interplay between stance classification and reason classification, we design computational models for examining how automatically computed stance information can be profitably exploited  ...  Acknowledgments We thank the three anonymous reviewers for their detailed and insightful comments on an earlier draft of this paper.  ... 
doi:10.3115/v1/d14-1083 dblp:conf/emnlp/HasanN14 fatcat:so5ug6nljrhq7i3nizpkiywp3i

Using a Generative Lexicon Resource to Compute Bridging Anaphora in Italian

Tommaso Caselli
2009 Revista de Procesamiento de Lenguaje Natural (SEPLN)  
The results also suggest a preference order for the different sources of bridging anaphora: lexical semantic relations are preferred over the use of common sense inferencing and background knowledge i.e  ...  a general problem of how much of background knowledge can be coded as part of the meaning of linguistic constituents.  ... 
dblp:journals/pdln/Caselli09 fatcat:5kpibvnograwbliv6jw44cnk24

Artificial Intelligence on Edge Computing: a Healthcare Scenario in Ambient Assisted Living

Andrea Pazienza, Giulio Mallardi, Corrado Fasciano, Felice Vitulano
2019 International Conference of the Italian Association for Artificial Intelligence  
The core idea is to exploit the proximity between computing and information-generation sources.  ...  The aging population brings many challenges surrounding the quality of life for older people and their carers, as well as impacts on the healthcare market.  ...  We also showed how all these AI techniques promote and reinforce each other by presenting a novel Edge Computing architecture and the eLifeCare platform, specifically designed for healthcare, outlining  ... 
dblp:conf/aiia/PazienzaMFV19 fatcat:7kvtxufnmnhw7govx35uzmfhp4

Scientia Potentia Est – On the Role of Knowledge in Computational Argumentation [article]

Anne Lauscher, Henning Wachsmuth, Iryna Gurevych, Goran Glavaš
2021 arXiv   pre-print
of knowledge, for each of the for main research areas in CA, and (3) outlining and discussing directions for future research efforts in CA.  ...  In this survey paper, we fill this gap by (1) proposing a pyramid of types of knowledge required in CA tasks, (2) analysing the state of the art with respect to the reliance and exploitation of these types  ...  Argumentative relation classification with background knowledge. In Computational Models of Argument, pages 319-330. IOS Press. Andreas Peldszus and Manfred Stede. 2015.  ... 
arXiv:2107.00281v2 fatcat:kbargvdhdjaf5eqdx2sxzgut4a
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