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Towards Causality Extraction from Requirements [article]

Jannik Fischbach, Benedikt Hauptmann, Lukas Konwitschny, Dominik Spies, Andreas Vogelsang
2020 arXiv   pre-print
Our dataset contains 212,186 sentences from 463 publicly available requirement documents and is a first step towards a gold standard corpus for causality extraction.  ...  Existing NLP approaches fail to extract causality from natural language (NL) with reasonable performance.  ...  In this paper, we describe first steps towards building a new approach for causality extraction.  ... 
arXiv:2006.15871v1 fatcat:ct4gvro6zva2zlupjzpwlhukvq

Automatic Detection of Causality in Requirement Artifacts: the CiRA Approach [article]

Jannik Fischbach, Julian Frattini, Arjen Spaans, Maximilian Kummeth, Andreas Vogelsang, Daniel Mendez, Michael Unterkalmsteiner
2021 arXiv   pre-print
Based on our findings, we develop a tool-supported approach for causality detection (CiRA). This constitutes a first step towards causality extraction from NL requirements.  ...  We understand causality extraction from requirements as a two-step problem: First, we need to detect if requirements have causal properties or not.  ...  This constitutes a first step towards causality extraction from NL requirements.  ... 
arXiv:2101.10766v1 fatcat:oex3mrlasfhpxjvotswpnrkvle

Towards Causal Knowledge Graphs - Position Paper

Eva Blomqvist, Marjan Alirezaie, Marina Santini
2020 European Conference on Artificial Intelligence  
In the paper, we therefore argue for an approach to extract causal relations from text, and represent them in the form of Knowledge Graphs (KG), to empower downstream ML applications, or AI systems in  ...  (i) the use of Knowledge Patterns to guide the KG generation process towards a certain resulting knowledge structure, and (ii) the use of a semantic referee to automatically curate the extracted knowledge  ...  Relation Extraction from Text Causal relations can be extracted from running text by exploiting linguistic cues and then the detected relations can be formalized, for instance, in the form of simple facts  ... 
dblp:conf/ecai/BlomqvistAS20 fatcat:jc5jyx3kujck5mj4lkmxbbb75u

Causality for Question Answering

Manvi Breja, Sanjay Kumar Jain
2020 International Conference on Computational Linguistics and Intelligent Systems  
The concept of causality is studied and its important role for different modules in developing Why-type Question Answering System.  ...  There are various researchers who have used causality as a key component to focus on causes and effects involved in the sentences and ultimately answering Why-type Questions.  ...  Causal semantic relations are identified from the document using Rhetorical Structure theory. Answer candidates are extracted from these retrieved passages containing causal relations in it.  ... 
dblp:conf/colins/BrejaJ20 fatcat:vtqvxgq7t5fxnctvl6wfu45tmq

A Practical Approach towards Causality Mining in Clinical Text using Active Transfer Learning [article]

Musarrat Hussain, Fahad Ahmed Satti, Jamil Hussain, Taqdir Ali, Syed Imran Ali, Hafiz Syed Muhammad Bilal, Gwang Hoon Park, Sungyoung Lee
2020 arXiv   pre-print
This active transfer learning based framework along with its supplementary services, is able to extract and enrich, causal relationships and their corresponding entities from clinical text.  ...  Objective: Causality mining is an active research area, which requires the application of state-of-the-art natural language processing techniques.  ...  Automatic machine learning based approaches utilize labeled datasets for extracting causality relationships from unseen data and thereby requires less expert intervention, relatively.  ... 
arXiv:2012.07563v1 fatcat:nr5by5ko7faxbcqewimgxsn3oq

Causality in Requirements Artifacts: Prevalence, Detection, and Impact [article]

Julian Frattini, Jannik Fischbach, Daniel Mendez, Michael Unterkalmsteiner, Andreas Vogelsang, Krzystof Wnuk
2021 arXiv   pre-print
Method: In a first case study, we investigate 14.983 sentences from 53 requirements documents to understand the extent and form in which causality occurs.  ...  Automatically extracting such causal information enables multiple use cases, such as test case generation, but it also requires to reliably detect causal relations in the first place.  ...  (C 2) CiRA as an approach for the automatic detection of causality in requirements documents. This constitutes the first step towards causality extraction from NL requirements.  ... 
arXiv:2112.08265v1 fatcat:rgqeqqnrbraoxfrpzpea4xeh3e

Structure of Attitudes Towards Persons With Intellectual Disabilities : A Causal Analysis
知的障害者に対する態度構造 : 因果分析による検討

2004 Japanese Journal of Special Education Research  
The eflect indicators from "ideal goodwill'' towards item 1, item 2, and item 39 were O.53, O.46, and O.54, Examining the causal coefficients of the paths from the three latent variables to respectively  ...  The eflect indicators from "participation in the community" towards item 6 and item 9 were O.56 and O.53, respectively, and the effbct indicator from "ideal goodwill" towards item 2 was O  ... 
doi:10.6033/tokkyou.41.627 fatcat:icaoe5r335gqxizh5h7q3eprbi

Roles of Causality for Understanding the Behavior of System Stakeholders

Jaemun Sim, Kyoung-Yun Kim
2019 Journal of Integrated Design & Process Science  
Second, stakeholder's dissatisfaction inferred by (or evaluated from) the model can be a motivation for a new system.  ...  Understanding of stakeholders' behavior requires knowledge about how they work in the designed system and how they respond to the designed product.  ...  ., big data) has been highlighted and can extract potentially causality from data. However, data mining technology is still limited because of its limited data measurement capability.  ... 
doi:10.3233/jid180016 fatcat:ooubwrvonrggvlukau3namenai

Answering Causal Questions and Developing Tool Support [chapter]

Sodel Vazquez-Reyes, Perla Velasco-Elizondo
2012 Automation  
Thus, Question Answering is an attractive research area as a distinctive combination from a variety of disciplines, including artificial intelligence, information retrieval, information extraction, natural  ...  One reason why causal questions have not been successfully treated is due to their answers requiring more elaboration (explanations) instead of short answers.  ...  (a rule-based approach as a first step towards tackling the problem of question analysis of "why" questions using an Information Extraction Analyzer.  An original answer candidate extraction filter has  ... 
doi:10.5772/48047 fatcat:s5pd6u6y5jgo3kryjgqkc6x6yy

Event Causality Extraction from Natural Science Literature

Biswanath Barik, Erwin Marsi, Pinar Öztürk
2016 Research in Computing Science  
Automatic extraction of causal knowledge from text content is a challenging task.  ...  We aim to develop a text mining framework capable of identifying and extracting causal dependencies among changing variables (or events) from scientific publications in the cross-disciplinary field of  ...  Problem Description Causality extraction from text content is a fundamental task towards the desire of developing literature-based knowledge discovery support system.  ... 
doi:10.13053/rcs-117-1-8 fatcat:m6w7q7himrdshjs6tsd2uqf5jy

Spontaneous Metatool Use by New Caledonian Crows

Alex H. Taylor, Gavin R. Hunt, Jennifer C. Holzhaider, Russell D. Gray
2007 Current Biology  
This work was supported by a Commonwealth Doctoral Scholarship (to A.H.T.) and a grant from the New Zealand Marsden Fund (to G.R.H. and R.D.G.). We are grateful to M.  ...  Successful completion of the task required a crow to use the short stick to extract the long stick from the box and then transport the long stick to the hole and extract the food.  ...  This tool was too short to extract the meat but could be used to extract the long tool from the tool box.  ... 
doi:10.1016/j.cub.2007.07.057 pmid:17702575 fatcat:4jexi55i5zac3bnok3y3nx6rea

Minimally Supervised Event Causality Identification

Quang Do, Yee Seng Chan, Dan Roth
2011 Conference on Empirical Methods in Natural Language Processing  
information towards determining causality between events.  ...  We show that combining discourse relation predictions and distributional similarity methods in a global inference procedure provides additional improvements towards determining event causality.  ...  For a verb (verbal predicate), we extract its subject and object from its associated dependency parse.  ... 
dblp:conf/emnlp/DoCR11 fatcat:kniwofifebedleji6ovfcssmti

From Representation to Reasoning: Towards both Evidence and Commonsense Reasoning for Video Question-Answering [article]

Jiangtong Li, Li Niu, Liqing Zhang
2022 arXiv   pre-print
To facilitate deeper video understanding towards video reasoning, we present the task of Causal-VidQA, which includes four types of questions ranging from scene description (description) to evidence reasoning  ...  We hope that Causal-VidQA can guide the research of video understanding from representation learning to deeper reasoning. The dataset and related resources are available at .  ...  They mainly required representation learning towards the instances and actions to accomplish the corresponding tasks.  ... 
arXiv:2205.14895v1 fatcat:uwhnm2jp7ranhkl6ewttpnbt3a

Towards Fine-grained Causal Reasoning and QA [article]

Linyi Yang, Zhen Wang, Yuxiang Wu, Jie Yang, Yue Zhang
2022 arXiv   pre-print
This paper introduces a novel fine-grained causal reasoning dataset and presents a series of novel predictive tasks in NLP, such as causality detection, event causality extraction, and Causal QA.  ...  Understanding causality is key to the success of NLP applications, especially in high-stakes domains.  ...  LogiQA is more challenging than our dataset (39.3 vs. 85.6 in accuracy) because it requires heavy logical reasoning rather than identifying causal relations from text.  ... 
arXiv:2204.07408v1 fatcat:6jkbi2f55nfgxdxlq4e2nvikza

Finding Needles in the Haystack: Harnessing Syslogs for Data Center Management [article]

Chen Liang, Theophilus Benson, Partha Kanuparthy, Yihua He
2016 arXiv   pre-print
Log-Prophet infers causal relationships between syslog lines and constructs succinct but valuable problem graphs, summarizing root causes and their locality, including cascading problems.  ...  Prevalent approaches to understanding syslogs focus on simple correlation and abnormality detection and are often limited to detection providing little insight towards diagnosis and resolution.  ...  Extracting Structure From syslogs Several approaches have been proposed to impose structure on syslogs and extract "templates" from them.  ... 
arXiv:1605.06150v1 fatcat:uftnscgznna7hdqpzm5obez3ym
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