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Causal Knowledge Extraction through Large-Scale Text Mining

Oktie Hassanzadeh, Debarun Bhattacharjya, Mark Feblowitz, Kavitha Srinivas, Michael Perrone, Shirin Sohrabi, Michael Katz
2020 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
In this demonstration, we present a system for mining causal knowledge from large corpuses of text documents, such as millions of news articles.  ...  Our system uses generic unsupervised and weakly supervised methods of causal relation extraction that do not impose semantic constraints on causes and effects.  ...  Causal Extraction Framework Causal Knowledge Extraction Engine This component first identifies sentences that are likely to be causal statements, and then extracts cause and effect text spans and phrases  ... 
doi:10.1609/aaai.v34i09.7092 fatcat:ns7l3zzkpvezjhjp7tfl67nrsq

Big Data and Causality

Hossein Hassani, Xu Huang, Mansi Ghodsi
2017 Annals of Data Science  
an overview table of Data Mining applications in causality analysis domain as a reference directory.  ...  Cause Effect Association (CEA)-based feature, distributional similarity methods, discourse relation prediction by Ruby-based discourse extraction system [62], event causality test (ECT), Penn Discourse  ...  on real, large-scale data set, mine the causal relationship between drugs and their associated adverse drug reactions.  ... 
doi:10.1007/s40745-017-0122-3 fatcat:y5fixid4ujgsjlgrcfceq5nosu

Construction of biological networks from unstructured information based on a semi-automated curation workflow

Justyna Szostak, Sam Ansari, Sumit Madan, Juliane Fluck, Marja Talikka, Anita Iskandar, Hector De Leon, Martin Hofmann-Apitius, Manuel C. Peitsch, Julia Hoeng
2015 Database: The Journal of Biological Databases and Curation  
The text mining pipeline can be tested through a web service via the URL: http://www.scaiview. com/belief Conclusions Creating structured knowledge from unstructured text in publications is an important  ...  To retrieve a large number of causal relationships from these documents we extracted text from the abstract, material and method and result sections (32, 33) .  ... 
doi:10.1093/database/bav057 pmid:26200752 pmcid:PMC5630939 fatcat:5sgpnfvinjgsxdfbgfvuadvjne

A New Statistical and Verbal-Semantic Approach to Pattern Extraction in Text Mining Applications

Dildre Georgiana Vasques, Paulo Sérgio Martins, Solange Oliveira Rezende
2019 CLEI Electronic Journal  
To assist in this process, this approach can count on thehelp of Text Mining techniques.  ...  Thus, the objective of this work is to support the user with theidentification of implicit relationships between concepts present in different texts, consideringthe causal relationships between concepts  ...  They extracted relevant keywords for each article through text mining, building a semantic classification.  ... 
doi:10.19153/cleiej.22.3.5 fatcat:qbivqzou4ras7mpad7vncoyqle

Back to Prior Knowledge: Joint Event Causality Extraction via Convolutional Semantic Infusion [article]

Zijian Wang, Hao Wang, Xiangfeng Luo, Jianqi Gao
2021 arXiv   pre-print
Joint event and causality extraction is a challenging yet essential task in information retrieval and data mining.  ...  Considering the prior knowledge of frequent n-grams that represent cause/effect events may benefit both event and causality extraction, in this paper, we propose convolutional knowledge infusion for frequent  ...  -Even if BERT trained on a large-scale corpus contains commonsense knowledge, it may not be sufficient for specific-domain such as financial.  ... 
arXiv:2102.09923v1 fatcat:fwcjpfdn6re5pksbuseyjlbtla

Inter-sentence and Implicit Causality Extraction from Chinese Corpus [chapter]

Xianxian Jin, Xinzhi Wang, Xiangfeng Luo, Subin Huang, Shengwei Gu
2020 Lecture Notes in Computer Science  
Automatically extracting causal relations from texts is a challenging task in Natural Language Processing (NLP).  ...  knowledge.  ...  In this paper we focus on mining causality in text, and make effect to improve the result of extracting implicitly or inter-sentence causality by building deep learning model.  ... 
doi:10.1007/978-3-030-47426-3_57 fatcat:zwplxjcvszg5bmzdq2wk6fmmeu

Causality Mining in Natural Languages Using Machine and Deep Learning Techniques: A Survey

Wajid Ali, Wanli Zuo, Rahman Ali, Xianglin Zuo, Gohar Rahman
2021 Applied Sciences  
, future scenario generation, medical text mining, behavior prediction, and textual prediction entailment.  ...  Among them, causality mining (CM) from textual data has become a significant area of concern and has more attention from researchers.  ...  In Figure 1 the more concise representation of causality in the natural language text is presented. causality in text, Applications of causality, Data types, Extraction/Mining techniq Comparison among  ... 
doi:10.3390/app112110064 fatcat:btv66da5x5a73auogv5d3lp2bi

Evaluation of the Performance of BioNLP Tools for Discovering Causal Genes in Terms with Pathway Enrichment

Xuan Qin, Shuguang Wang, Yongze Wu, Jingbo Xia
2018 Journal of Physics, Conference Series  
To meet the aim of large-scale knowledge discovery, biomedical natural language processing is regarded as an effective tool to curate hidden information in research papers or related texts.  ...  Actually, pathway enrichment analysis plays an important role in overlying functioning mechanism of disease and causal genes.  ...  Introduction Knowledge discovery based on text mining technique has played a key role in the knowledge curation and new relation extraction in the field of bio-medical research [1] .  ... 
doi:10.1088/1742-6596/1069/1/012037 fatcat:icre5vlrova4xdph3fyoio3j3y

Semantic culturomics

Fabian M. Suchanek, Nicoleta Preda
2014 Proceedings of the VLDB Endowment  
We predict that this could open up a new field of research, "Semantic Culturomics", in which no longer human text helps machines build up knowledge bases, but knowledge bases help humans understand their  ...  In this vision paper, we argue that the advent of large knowledge bases (such as YAGO [37], NELL [5], DBpedia [3], and Freebase) will revolutionize the field.  ...  This work was the first large-scale study of culture through digitized texts. Yet, as explained above, it remains bound to the words of the text.  ... 
doi:10.14778/2732977.2732994 fatcat:c2jvs6jl4rarzaptaqyi4iu4da

Data Analytics and Mining in Healthcare with Emphasis on Causal Relationship Mining

2019 International journal of recent technology and engineering  
The knowledge extracted from these data sources guiding patients and healthcare personnel towards improved health conditions.  ...  The present models for causality have limitations in terms of scalability and reliability. The present study is targeted to study causal models for causal relationship mining.  ...  Stream data mining Stream data mining is a method of extracting useful knowledge from continuously moving large amounts data. Stream data consists of ordered sequences of data records.  ... 
doi:10.35940/ijrte.d6492.118419 fatcat:zkzif7glbvawhmqualnehr7se4

GTX.Digest.VCF: an online NGS data interpretation system based on intelligent gene ranking and large-scale text mining

Yanhuang Jiang, Chengkun Wu, Yanghui Zhang, Shaowei Zhang, Shuojun Yu, Peng Lei, Qin Lu, Yanwei Xi, Hua Wang, Zhuo Song
2019 BMC Medical Genomics  
Therefore, we developed GTX.Digest.VCF, an online DNA sequencing interpretation system, which prioritizes genes and variants for novel disease-gene relation discovery and integrates text mining results  ...  Its phenotype-driven ranking and biological data mining approach significantly speed up the whole interpretation process.  ...  CW and SY implemented the NER and RE text mining approach to obtain the mined knowledge base. SZ and PL implemented dynamic filtering functions.  ... 
doi:10.1186/s12920-019-0637-x pmid:31856831 pmcid:PMC6923899 fatcat:cbr4wtugizdxrn2ybs4slp3xlu

Knowledge-based Extraction of Cause-Effect Relations from Biomedical Text [article]

Sachin Pawar, Ravina More, Girish K. Palshikar, Pushpak Bhattacharyya, Vasudeva Varma
2021 arXiv   pre-print
We propose a knowledge-based approach for extraction of Cause-Effect (CE) relations from biomedical text.  ...  As compared to the existing knowledge base - SemMedDB (Kilicoglu et al., 2012), the number of extractions are almost twice.  ...  Efficient text mining algorithms will help to improve the knowledge extraction from biomedical text and will be of real value in developing knowledge discovery graphs, QA systems and knowledge summarization  ... 
arXiv:2103.06078v1 fatcat:vjrrati5wfbytlop4h27hbldwa

Introducing a New Scalable Data-as-a-Service Cloud Platform for Enriching Traditional Text Mining Techniques by Integrating Ontology Modelling and Natural Language Processing [chapter]

Alexey Cheptsov, Axel Tenschert, Paul Schmidt, Birte Glimm, Mauricio Matthesius, Thorsten Liebig
2014 Lecture Notes in Computer Science  
The technique is inherently designed with parallelism in mind, which allows for high performance on large-scale Cloud computing infrastructures.  ...  to obtain some useful, often not explicitly stated knowledge and facts, related to a particular domain of interest.  ...  However, automatic knowledge extraction from large text collections, also considering external data sources (e.g., Wikipedia), is a nontrivial and very challenging task.  ... 
doi:10.1007/978-3-642-54370-8_6 fatcat:qoxc6xntnbczlp247iak3fb7dm

A Correlation Analysis of Construction Site Fall Accidents Based on Text Mining

Xixi Luo, Quanlong Liu, Zunxiang Qiu
2021 Frontiers in Built Environment  
In this paper, an analysis method based on text mining was chosen to analyze and process the collected data of 557 investigation reports of construction site fall accidents in China from 2013 to 2019.  ...  but evaluating the relationship between accident causal factors and unstructured texts remains an area in urgent need of further study.  ...  data from large-scale text databases into digital data to extract potential useful information.  ... 
doi:10.3389/fbuil.2021.690071 fatcat:mic53bqlenfm5ka6js5z2aue2e

A Multi-level Neural Network for Implicit Causality Detection in Web Texts [article]

Shining Liang, Wanli Zuo, Zhenkun Shi, Sen Wang, Junhu Wang, Xianglin Zuo
2021 arXiv   pre-print
Mining causality from text is a complex and crucial natural language understanding task corresponding to the human cognition.  ...  To the best of our knowledge, with regards to the causality tasks, this is the first time that the Relation Network is applied.  ...  When dealing with large-scale web text, detecting causalities is a foundation system before extracting cause-effect pairs.  ... 
arXiv:1908.07822v4 fatcat:2osc5g5ha5fbrnezmyao6e7hda
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