A Review of Dataset and Labeling Methods for Causality Extraction

Causality represents the most important kind of correlation between events. Extracting causality from text has become a promising hot topic in NLP. However, there is no mature research systems, evaluation rules and datasets for public evaluation. Moreover, there is a lack of unified causal sequence labeling methods, which constitute the key factors that hinder the progress of causality extraction research. We survey the limitations and shortcomings of existing causality research field comprehensively from the aspects of basic concepts, extraction methods, experimental data, and labeling methods, so as to provide reference for future research on causality extraction. We summarize the existing causality datasets, explore their practicability and extensibility from multiple perspectives. Aiming at the problem of causal sequence labeling, we analyze the existing methods of causal sequence labeling, with a summarizations of its regulation. Multiple candidate causal labeling sequences are put forward according to labeling controversy to explore the optimal labeling method through experiments, and suggestions are provided for selecting labeling method.

[1]  Paramita Mirza,et al.  An Analysis of Causality between Events and its Relation to Temporal Information , 2014, COLING.

[2]  C. Granger.  Some recent development in a concept of causality , 1988 .

[3]  Qi Li,et al.  Causality Extraction based on Self-Attentive BiLSTM-CRF with Transferred Embeddings , 2019, Neurocomputing.

[4]  Zheng Chen,et al.  Detecting and Explaining Causes From Text For a Time Series Event , 2017, EMNLP.

[5]  Michael J. Paul,et al.  Feature Selection as Causal Inference: Experiments with Text Classification , 2017, CoNLL.

[6]  Pengfei Li,et al.  Knowledge-oriented convolutional neural network for causal relation extraction from natural language texts , 2019, Expert Syst. Appl..

[7]  Kathy McKeown,et al.  Identifying Causal Relations Using Parallel Wikipedia Articles , 2016, ACL.

[8]  Roxana Gîrju,et al.  Automatic Detection of Causal Relations for Question Answering , 2003, ACL 2003.

[9]  Juliane Fluck,et al.  BioCreative V track 4: a shared task for the extraction of causal network information using the Biological Expression Language , 2016, Database J. Biol. Databases Curation.

[10]  P. White.  Ideas About Causation in Philosophy and Psychology , 1990 .

[11]  Fu Jian,et al.  Event Causal Relation Extraction Based on Cascaded Conditional Random Fields , 2011 .

[12]  Kira Radinsky,et al.  Learning causality for news events prediction , 2012, WWW.

[13]  Koldo Gojenola,et al.  Learning to extract adverse drug reaction events from electronic health records in Spanish , 2016, Expert Syst. Appl..

[14]  Kalpana Raja,et al.  PPInterFinder—a mining tool for extracting causal relations on human proteins from literature , 2013, Database J. Biol. Databases Curation.

[15]  Wei Xu,et al.  Bidirectional LSTM-CRF Models for Sequence Tagging , 2015, ArXiv.

[16]  Gosse Bouma,et al.  Extracting Explicit and Implicit Causal Relations from Sparse, Domain-Specific Texts , 2011, NLDB.

[17]  Yizhou Sun,et al.  Causal relation of queries from temporal logs , 2007, WWW '07.

[18]  Miriam Butt,et al.  Automatic Detection of Causal Relations in German Multilogs , 2014, EACL 2014.

[19]  Dan I. Moldovan,et al.  Causal Relation Extraction , 2008, LREC.

[20]  Dan Roth,et al.  Minimally Supervised Event Causality Identification , 2011, EMNLP.

[21]  Yutaka Kidawara,et al.  Toward Future Scenario Generation: Extracting Event Causality Exploiting Semantic Relation, Context, and Association Features , 2014, ACL.

[22]  S. Ge,et al.  Causality Extraction With GAN , 2018 .

[23]  Junhu Wang,et al.  On Event Causality Detection in Tweets , 2019, ArXiv.

[24]  Danushka Bollegala,et al.  Causality Patterns for Detecting Adverse Drug Reactions From Social Media: Text Mining Approach , 2018, JMIR public health and surveillance.

[25]  Seung-won Hwang,et al.  Commonsense Causal Reasoning between Short Texts , 2016, KR.

[26]  Kira Radinsky,et al.  Building Causal Graphs from Medical Literature and Electronic Medical Records , 2019, AAAI.

[27]  Daniela Garcia,et al.  COATIS, an NLP System to Locate Expressions of Actions Connected by Causality Links , 1997, EKAW.

[28]  Alexandros G. Dimakis,et al.  CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training , 2017, ICLR.

[29]  Zhao Rui,et al.  Causal Relation Identification Using Convolutional Neural Networks and Knowledge Based Features , 2017 .

[30]  Seung-won Hwang,et al.  Machine-Translated Knowledge Transfer for Commonsense Causal Reasoning , 2018, AAAI.

[31]  Farid Meziane,et al.  Extracting Arabic Causal Relations Using Linguistic Patterns , 2016, ACM Trans. Asian Low Resour. Lang. Inf. Process..

[32]  Osonde Osoba,et al.  Beyond DAGs: Modeling Causal Feedback with Fuzzy Cognitive Maps , 2019, ArXiv.

[33]  Jong-Hoon Oh,et al.  Excitatory or Inhibitory: A New Semantic Orientation Extracts Contradiction and Causality from the Web , 2012, EMNLP.

[34]  Ling Luo,et al.  Extracting Causal Relations from Emergency Cases Based on Conditional Random Fields , 2017, KES.

[35]  Juliane Fluck,et al.  Training and evaluation corpora for the extraction of causal relationships encoded in biological expression language (BEL) , 2016, Database J. Biol. Databases Curation.

[36]  Jong-Hoon Oh,et al.  Generating Event Causality Hypotheses through Semantic Relations , 2015, AAAI.

[37]  Lipika Dey,et al.  Automatic Extraction of Causal Relations from Text using Linguistically Informed Deep Neural Networks , 2018, SIGDIAL Conference.

[38]  Jian-Yun Nie,et al.  Event causality extraction based on connectives analysis , 2016, Neurocomputing.

[39]  Paramita Mirza,et al.  Extracting Temporal and Causal Relations between Events , 2014, ACL.

[40]  Syin Chan,et al.  Extracting Causal Knowledge from a Medical Database Using Graphical Patterns , 2000, ACL.

[41]  Christopher S. G. Khoo,et al.  Automatic Extraction of Cause-Effect Information from Newspaper Text Without Knowledge-based Inferencing , 1998 .

[42]  ChengXiang Zhai,et al.  Constructing and Embedding Abstract Event Causality Networks from Text Snippets , 2017, WSDM.