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Relevant bibliographies by topics / Contradiction detection

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Academic literature on the topic 'Contradiction detection'

Author: Grafiati

Published: 5 June 2025

Last updated: 16 July 2025

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Journal articles on the topic "Contradiction detection"

1

Sepúlveda-Torres, Robiert, Alba Bonet-Jover, and Estela Saquete. "“Here Are the Rules: Ignore All Rules”: Automatic Contradiction Detection in Spanish." Applied Sciences 11, no. 7 (2021): 3060. http://dx.doi.org/10.3390/app11073060.

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This paper tackles automatic detection of contradictions in Spanish within the news domain. Two pieces of information are classified as compatible, contradictory, or unrelated information. To deal with the task, the ES-Contradiction dataset was created. This dataset contains a balanced number of each of the three types of information. The novelty of the research is the fine-grained annotation of the different types of contradictions in the dataset. Presently, four different types of contradictions are covered in the contradiction examples: negation, antonyms, numerical, and structural. However, future work will extend the dataset with all possible types of contradictions. In order to validate the effectiveness of the dataset, a pretrained model is used (BETO), and after performing different experiments, the system is able to detect contradiction with a F1m of 92.47%. Regarding the type of contradictions, the best results are obtained with negation contradiction (F1m = 98%), whereas structural contradictions obtain the lowest results (F1m = 69%) because of the smaller number of structural examples, due to the complexity of generating them. When dealing with a more generalistic dataset such as XNLI, our dataset fails to detect most of the contradictions properly, as the size of both datasets are very different and our dataset only covers four types of contradiction. However, using the classification of the contradictions leads us to conclude that there are highly complex contradictions that will need external knowledge in order to be properly detected and this will avoid the need for them to be previously exposed to the system.

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Li, Luyang, Bing Qin, and Ting Liu. "Contradiction Detection with Contradiction-Specific Word Embedding." Algorithms 10, no. 2 (2017): 59. http://dx.doi.org/10.3390/a10020059.

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Gärtner, Alexander Elenga, and Dietmar Göhlich. "Towards an automatic contradiction detection in requirements engineering." Proceedings of the Design Society 4 (May 2024): 2049–58. http://dx.doi.org/10.1017/pds.2024.207.

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AbstractThis paper presents a novel method for automatic contradiction detection in requirements engineering using a hybrid approach combining formal logic with Large Language Models (LLMs), specifically GPT-3. Our three-phase process detects contradictions by identifying conditionals and pseudo-grammatical elements, and employing LLMs for nuanced contradiction detection. Tested extensively, including on a real-world electric bus project, our method achieved 99% accuracy and 60% recall. This approach significantly reduces manual effort, enhances quality, and is scalable for future advancements.

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Rahimi, Zeinab, and Mehrnoush Shamsfard. "A Neuro Symbolic Approach for Contradiction Detection in Persian Text." JUCS - Journal of Universal Computer Science 29, no. 3 (2023): 242–64. http://dx.doi.org/10.3897/jucs.90646.

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Detection of semantic contradictory sentences is a challenging and fundamental issue for some NLP applications, such as textual entailments recognition. In this study, contradiction means different types of semantic confrontation, such as negation, antonymy, and numerical. Due to the lack of sufficient data to apply precise machine learning and, specifically, deep learning methods to Persian and other low-resource languages, rule-based approaches are of great interest. Also, recently, the emergence of new methods such as transfer learning has opened up the possibility of deep learning for low-resource languages. This paper introduces a hybrid contradiction detection approach for detecting seven categories of contradictions in Persian texts: Antonymy, negation, numerical, factive, structural, lexical and world knowledge. The proposed method consists of 1) a novel data mining method and 2) a transformer-based deep neural method for contradiction detection . Also, a simple baseline is presented for comparison. The data mining method uses frequent rule mining to extract appropriate contradiction detection rules employing a development set. Extracted rules are tested for different categories of contradictory sentences. In the first step, a classifier checks whether the rules work for an input sentence pair. Then, according to the result, rules are used for three categories of negation, numerical, and antonym. In this part, the highest F-measure is obtained for detecting the negation category (90%), the average F-measure for these three categories is 86%, and for the other four categories, in which the rules have a lower F-measure of 62%, the transformer-based method achieved 76%. The proposed hybrid approach has an overall f-measure of higher than 80%.&nbsp;

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Rahimi, Zeinab, and Mehrnoush Shamsfard. "A Neuro Symbolic Approach for Contradiction Detection in Persian Text." JUCS - Journal of Universal Computer Science 29, no. (3) (2023): 242–64. https://doi.org/10.3897/jucs.90646.

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Detection of semantic contradictory sentences is a challenging and fundamental issue for some NLP applications, such as textual entailments recognition. In this study, contradiction means different types of semantic confrontation, such as negation, antonymy, and numerical. Due to the lack of sufficient data to apply precise machine learning and, specifically, deep learning methods to Persian and other low-resource languages, rule-based approaches are of great interest. Also, recently, the emergence of new methods such as transfer learning has opened up the possibility of deep learning for low-resource languages. This paper introduces a hybrid contradiction detection approach for detecting seven categories of contradictions in Persian texts: Antonymy, negation, numerical, factive, structural, lexical and world knowledge. The proposed method consists of 1) a novel data mining method and 2) a transformer-based deep neural method for contradiction detection . Also, a simple baseline is presented for comparison. The data mining method uses frequent rule mining to extract appropriate contradiction detection rules employing a development set. Extracted rules are tested for different categories of contradictory sentences. In the first step, a classifier checks whether the rules work for an input sentence pair. Then, according to the result, rules are used for three categories of negation, numerical, and antonym. In this part, the highest F-measure is obtained for detecting the negation category (90%), the average F-measure for these three categories is 86%, and for the other four categories, in which the rules have a lower F-measure of 62%, the transformer-based method achieved 76%. The proposed hybrid approach has an overall f-measure of higher than 80%.

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Gärtner, Alexander Elenga, Dietmar Göhlich, and Tu-Anh Fay. "AUTOMATED CONDITION DETECTION IN REQUIREMENTS ENGINEERING." Proceedings of the Design Society 3 (June 19, 2023): 707–16. http://dx.doi.org/10.1017/pds.2023.71.

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AbstractIn product development, it is of great importance that a complete, unambiguous, and, as far as possible, contradiction-free target system is defined. Requirements documents of complex systems can contain several thousand individual requirements, derived in an interdisciplinary manner and written in natural language by many different stakeholders. Hence, errors, in the form of contradictions, cannot be completely avoided in these documents and today they must be corrected manually with high effort.This paper presents an important building block for automated contradiction detection and quality analysis of requirements documents. We discuss the necessary identification of conditions in requirements and the extraction of the verbal expressions associated with condition and effect, respectively. We applied and analyzed natural language processing methods based on grammatical versus machine learning models. The models have been applied to 1,861 real-world requirements. Both approaches generate promising results, with an accuracy partly over 98%. However, in structured specification texts, a grammatical model is preferable due to lower effort in preprocessing and better usability.

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Gärtner, Alexander Elenga, Tu-Anh Fay, and Dietmar Göhlich. "Fundamental Research on Detecting Contradictions in Requirements: Taxonomy and Semi-Automated Approach." Applied Sciences 12, no. 15 (2022): 7628. http://dx.doi.org/10.3390/app12157628.

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Requirements documents can contain several thousand individual requirements. They must be error-free to avoid unnecessary complications and costs in the later product development stages. An important part of this is to identify contradictions between two requirements. The first step is therefore to define what contradictions are and in what form they can occur in requirement documents. In this paper the scientific theories regarding contradictions are discussed, concerning to their usefulness for the topic. In doing so, the Aristotelian Logic proved to provide the best basis for an application in the Requirements Engineering context. Based on this theory, we have created specific subtypes of contradictions to match them to the requirements engineering field. The identification of these subtypes is done by a formalization of the requirement sentences and a subsequent analysis by means of simple questions. To validate the method, industrial requirement documents were searched for contradictions. For each detected type of contradiction, we present an example of the detection process. Thereby, we show that the method is easy to apply and may also be used by non-specialists. Thus, our method provides a taxonomy as a basis for further research on automated contradiction detection as well as on automated quality analysis of requirements documents.

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Huang, Shen Gui, Nian Duan, Xiu Yu Chen, and Xi Peng Xu. "Application of Image Mosaic Method in the Detection of Grinding Wheel Topography." Advanced Materials Research 500 (April 2012): 302–7. http://dx.doi.org/10.4028/www.scientific.net/amr.500.302.

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The topography of a grinding wheel can be obtained quickly and exactly through applying the computer vision method to detect the wheel topography. However, the application of computer vision in detecting wheel topography is restricted due to the contradiction between vision field and resolution while using traditional computer vision detecting method. In the present paper, the 3D topography of a diamond grinding wheel was reconstructed by combining image mosaic technique, corner detection algorithm, image matching algorithm and image fusion algorithm. The image mosaic technique was found to be effective in solving the contradiction between visual field and resolution and rapidly obtain high resolution image of the wheel topography in a wider range of vision field, thereby providing a valuable reference for quantitative evaluation of the performance of grinding wheels.

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Qu, Kai, Guhao Zhao, Yarong Wu, and Liang Tong. "Research on Airspace Conflict Detection Method Based on Spherical Discrete Grid Representation." Applied Sciences 13, no. 11 (2023): 6493. http://dx.doi.org/10.3390/app13116493.

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With the continuous development of general aviation, the contradiction between the air demand of general aviation low-altitude airspace and civil aviation routes is sharp. The difficulty of airspace planning is complex and changeable, and the existing working mode of simply using computer mapping and manually finding airspace conflict contradictions can no longer meet the large-scale air use demand. In response to the existing spatial representation model of longitude and latitude grid, which has large grid deformation in high latitude areas, and the problem of slow computation speed of the conflict detection (CD) algorithm that determines whether the airspace boundary coordinates overlap, we propose a grid model that represents airspace with a spherical rhombic discrete grid of positive icosahedron and design a matrix-based digital representation method of airspace, which uses matrix product operation. The matrix product operation is used to quickly determine whether there is a conflict between airspace and airspace and between airspace and routes.

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Yeh, Chi Hao, Cheng Yu Hsieh, and Ful Chiang Wu. "The Synergy of TRIZ and Automatic Optical Inspection (AOI) for Detecting Surface Defects on Small Metal Parts." Advanced Materials Research 838-841 (November 2013): 2030–33. http://dx.doi.org/10.4028/www.scientific.net/amr.838-841.2030.

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The aim of this paper is to show how to apply TRIZ to resolve difficulties and conflicts in Automatic Optical Inspection (AOI) for detecting surface defects on small metal parts and saving effort-spent (energy saving). TRIZ has been well-known as a creative and innovative thinking theory in solving engineering and technology contradictions in the last two decades. However, few studies and practical usage were proposed in AOI area. Conflicts occurring in AOI are discussed to demonstrate the ideas guided by 39 TRIZ management parameters, 40 innovative principles, and contradiction matrix. The results show that TRIZ is able to provide direct, quick and effective alternatives to resolve the conflicts in AOI. In this manner, huge effort and cost can be saved in further execution stage. In this paper, the detection for surface defects on small and precise metal part are utilized as a case study to describe the proposed TRIZ-based conflicts-solution in AOI implementation.

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Dissertations / Theses on the topic "Contradiction detection"

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Karlova-Bourbonus, Natali [Verfasser]. "Automatic detection of contradictions in texts / Natali Karlova-Bourbonus." Gießen : Universitätsbibliothek, 2019. http://d-nb.info/1184788324/34.

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Vargas, Danny Suarez. "Detecting contrastive sentences for sentiment analysis." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2016. http://hdl.handle.net/10183/148304.

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A análise de contradições é uma área relativamente nova, multidisciplinar e complexa que tem por objetivo principal identificar pedaços contraditórios de texto. Ela pode ser abordada a partir das perspectivas de diferentes áreas de pesquisa, tais como processamento de linguagem natural, mineração de opinioes, recuperação de informações e extração de Informações. Este trabalho foca no problema de detectar contradições em textos – mais especificamente, nas contradições que são o resultado da diversidade de sentimentos entre as sentenças de um determinado texto. Ao contrário de outros tipos de contradições, a detecção de contradições baseada em sentimentos pode ser abordada como uma etapa de pós-processamento na tarefa tradicional de análise de sentimentos. Neste contexto, este trabalho apresenta duas contribuições principais. A primeira é um estudo exploratório da tarefa de classificação, na qual identificamos e usamos diferentes ferramentas e recursos. A segunda contribuição é a adaptação e a extensão de um framework de análise contradição existente, filtrando seus resultados para remover os comentários erroneamente rotulados como contraditórios. O método de filtragem baseia-se em dois algoritmos simples de similaridade entre palavras. Uma avaliação experimental em comentários sobre produtos reais mostrou melhorias proporcionais de até 30 % na acurácia da classificação e 26 % na precisão da detecção de contradições.<br>Contradiction Analysis is a relatively new multidisciplinary and complex area with the main goal of identifying contradictory pieces of text. It can be addressed from the perspectives of different research areas such as Natural Language Processing, Opinion Mining, Information Retrieval, and Information Extraction. This work focuses on the problem of detecting sentiment-based contradictions which occur in the sentences of a given review text. Unlike other types of contradictions, the detection of sentiment-based contradictions can be tackled as a post-processing step in the traditional sentiment analysis task. In this context, we make two main contributions. The first is an exploratory study of the classification task, in which we identify and use different tools and resources. Our second contribution is adapting and extending an existing contradiction analysis framework by filtering its results to remove the reviews that are erroneously labeled as contradictory. The filtering method is based on two simple term similarity algorithms. An experimental evaluation on real product reviews has shown proportional improvements of up to 30% in classification accuracy and 26% in the precision of contradiction detection.

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Correia, Catarina Pinheiro. "Dynamic Detection of Ambiguities and Contradictions in Job Posting." Master's thesis, 2019. https://hdl.handle.net/10216/121259.

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Han, Nai-Hsuan, and 韓乃軒. "A Study of Selecting Machine Learning Features for Detecting Entailment, Paraphrase and Contradiction in Texts." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/44594957024872746237.

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碩士<br>國立雲林科技大學<br>資訊工程系碩士班<br>101<br>NTCIR-9 RITE task evaluates systems which automatically detect entailment, paraphrase, and contradiction in texts. We developed a preliminary system for the NTCIR-9 RITE task based on rules. In NTCIR-10, we tried machine learning approaches. We transformed the existing rules into features and then added additional syntactic and semantic features for SVM. The straightforward assumption was still kept in NTCIR-10: the relation between two sentences was determined by the different parts between them instead of the identical parts. Therefore, features in NTCIR-9 including sentence lengths, the content of matched keywords, quantities of matched keywords, and their parts of speech together with new features such as parsing tree information, dependency relations, negation words and synonyms were considered. We found that some features were useful for the BC subtask while some help more in the MC subtask.

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Book chapters on the topic "Contradiction detection"

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Tawfik, Noha S., and Marco R. Spruit. "Automated Contradiction Detection in Biomedical Literature." In Machine Learning and Data Mining in Pattern Recognition. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96136-1_12.

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Damásio, Carlos Viegas, and Luís Moniz Pereira. "A paraconsistent semantics with contradiction support detection." In Logic Programming And Nonmonotonic Reasoning. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63255-7_18.

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Li, Luyang, Bing Qin, and Ting Liu. "Generating Triples Based on Dependency Parsing for Contradiction Detection." In Communications in Computer and Information Science. Springer Singapore, 2015. http://dx.doi.org/10.1007/978-981-10-0080-5_19.

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Oliverio, Vinicius, and Estevam R. Hruschka. "Contradiction Detection and Ontology Extension in a Never-Ending Learning System." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34654-5_1.

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Kharrat, Ala Eddine, Lobna Hlaoua, and Lotfi Ben Romdhane. "Contradiction Detection Approach Based on Semantic Relations and Evidence of Uncertainty." In Computational Collective Intelligence. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16014-1_19.

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Tikhomirov, O. K., and V. E. Klochko. "The Detection of a Contradiction as the Initial Stage of Problem Formation." In The Concept of Activity in Soviet Psychology. Routledge, 2024. http://dx.doi.org/10.4324/9781003575429-11.

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Esch, John, and Robert Levinson. "Propagating truth and detecting contradiction in conceptual graph databases." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-61534-2_15.

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Feng, Xinguo, Yanjun Zhang, Mark Huasong Meng, and Sin G. Teo. "Detecting Contradictions from CoAP RFC Based on Knowledge Graph." In Network and System Security. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-23020-2_10.

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Sree Harsha, Sai, K. Krishna Swaroop, and B. R. Chandavarkar. "Natural Language Inference: Detecting Contradiction and Entailment in Multilingual Text." In Communications in Computer and Information Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-91244-4_25.

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Heffer, Chris. "Claims of and Evidence for Untruthfulness." In All Bullshit and Lies? Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780190923280.003.0004.

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Chapter 3 focuses on the first two steps of the TRUST untruthfulness heuristic: CLAIM and EVIDENCE. It begins by noting four principal rational motives for calling out lies and bullshit (confession, detection, self-contradiction, and falsification), but stresses that in the majority of cases one relies primarily on falsification. This is problematic because Chapters 1 and 2 stress that both discursive insincerity and epistemic irresponsibility are subjective rather than objective notions. The reliance on falsification as a starting point for analysis restricts the application of the framework primarily to “factually significant” and “falsifiable” claims. A distinction is made between “salty-type” statements that invite further investigation and “tasty-type” statements that invite agreement or disagreement but not further investigation. Only “salty-type” claims are open to a TRUST analysis. Finally, the challenge of anti-realism is taken up, and it is argued that there is more consensus about evidence than the “truth wars” would suggest.

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Conference papers on the topic "Contradiction detection"

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Derakhshan, Amirhossein, Hossein Rahmani, and Milad Allahgholi. "CODAL: Contradiction Detection in the Duties of Actors in Legal Documents." In 2025 11th International Conference on Web Research (ICWR). IEEE, 2025. https://doi.org/10.1109/icwr65219.2025.11006256.

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Schumann, Gerrit, and Jorge Marx Gómez. "Detection of Contradictions and Inconsistencies in German Regulatory Documents." In 2024 6th International Conference on Natural Language Processing (ICNLP). IEEE, 2024. http://dx.doi.org/10.1109/icnlp60986.2024.10692679.

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Schumann, Gerrit, and Jorge Marx Gómex. "Detection of Conflicts, Contradictions and Inconsistencies in Regulatory Documents: A Literature Review." In 2024 Fifth International Conference on Intelligent Data Science Technologies and Applications (IDSTA). IEEE, 2024. http://dx.doi.org/10.1109/idsta62194.2024.10747003.

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Vargas, Danny Suarez, and Viviane Moreira. "Identifying Sentiment-Based Contradictions." In Simpósio Brasileiro de Banco de Dados. Sociedade Brasileira de Computação - SBC, 2016. http://dx.doi.org/10.5753/sbbd.2016.24310.

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Contradiction Analysis is a relatively new multidisciplinary and complex area with the main goal of identifying contradictory pieces of text. It can be addressed from the perspectives of different research areas such as Natural Language Processing, Opinion Mining, Information Retrieval, and Information Extraction. This paper focuses on the problem of detecting sentiment-based contradictions which occur in the sentences of a given review text. Unlike other types of contradictions, the detection of sentiment-based contradictions can be tackled as a post-processing step in the traditional sentiment analysis task. In this context, we adapted and extended an existing contradiction analysis framework by filtering its results to remove the reviews that are erroneously labeled as contradictory. The filtering method is based on two simple term similarity algorithms. An experimental evaluation on real product reviews has shown proportional improvements of up to 30% in classification accuracy and 26% in the precision of contradiction detection.

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Makhervaks, Dave, Plia Gillis, and Kira Radinsky. "Clinical Contradiction Detection." In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.emnlp-main.80.

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Schumann, Gerrit, and Jorge Marx Gómez. "Unsupervised Contradiction Detection using Sentence Transformations." In 2023 5th International Conference on Natural Language Processing (ICNLP). IEEE, 2023. http://dx.doi.org/10.1109/icnlp58431.2023.00065.

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Kutty, Rida Javed, Roshni P N, and Shreya S. Adiga. "DisContNet: Contradiction Detection in Texts using Transformers." In 2023 Fourth International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE). IEEE, 2023. http://dx.doi.org/10.1109/icstcee60504.2023.10585189.

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Jin, Di, Sijia Liu, Yang Liu, and Dilek Hakkani-Tur. "Improving Bot Response Contradiction Detection via Utterance Rewriting." In Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue. Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.sigdial-1.56.

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Sifa, Rafet, Maren Pielka, Rajkumar Ramamurthy, Anna Ladi, Lars Hillebrand, and Christian Bauckhage. "Towards Contradiction Detection in German: a Translation-Driven Approach." In 2019 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2019. http://dx.doi.org/10.1109/ssci44817.2019.9003090.

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Vancea, Bogdan, Alexandru Marchis, Mihaela Dinsoreanu, and Rodica Potolea. "Contradiction detection between opinions: From a big data perspective." In 2013 IEEE International Conference on Intelligent Computer Communication and Processing (ICCP). IEEE, 2013. http://dx.doi.org/10.1109/iccp.2013.6646118.

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Reports on the topic "Contradiction detection"

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Perlis, Donald, Michael Anderson, Darsana Josyula, Waiyian Chong, and Scott Fults. Detecting, Classifying, and Handling Contradictions in a Large, Dynamic Information Environment. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada457343.

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