Constructing and Embedding Abstract Event Causality Networks from Text Snippets

Zhao, Sendong; Wang, Quan; Massung, Sean; Qin, Bing; Liu, Ting; Wang, Bin; Zhai, ChengXiang

doi:10.1145/3018661.3018707

Cited by 63 publications

(35 citation statements)

References 27 publications

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“…This approach typically begins by extracting the events from the target texts using natural language processing techniques such as sanitization, tokenization, POS tag analysis, and name entity recognition. Several types of objects can be extracted to represent the events: i) Noun Phrase-based [39,94,111], where the noun-phrase corresponds to each event (for example, "2008 Sichuan Earthquake"); ii) Verbs and Nouns [112,168], where an event is represented as a set of noun-verb pairs extracted from news headlines (for example, "<capture, people>", "<escape, prison>", or "<send, prison>"); and iii) Tuple-based [249], where each event is represented by a tuple consisting of objects (such as actors, instruments, or receptors), a relationship (or property), and time. An RDF-based format has also been leveraged in some works [57].…”

Section: Semantic Predictionmentioning

confidence: 99%

Event Prediction in Big Data Era: A Systematic Survey

Zhao¹

2020

Preprint

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Events are occurrences in specific locations, time, and semantics that nontrivially impact either our society or the nature, such as earthquakes, civil unrest, system failures, pandemics, and crimes. It is highly desirable to be able to anticipate the occurrence of such events in advance in order to reduce the potential social upheaval and damage caused. Event prediction, which has traditionally been prohibitively challenging, is now becoming a viable option in the big data era and is thus experiencing rapid growth, also thanks to advances in high performance computers and new Artificial Intelligence techniques. There is a large amount of existing work that focuses on addressing the challenges involved, including heterogeneous multi-faceted outputs, complex (e.g., spatial, temporal, and semantic) dependencies, and streaming data feeds. Due to the strong interdisciplinary nature of event prediction problems, most existing event prediction methods were initially designed to deal with specific application domains, though the techniques and evaluation procedures utilized are usually generalizable across different domains. However, it is imperative yet difficult to cross-reference the techniques across different domains, given the absence of a comprehensive literature survey for event prediction. This paper aims to provide a systematic and comprehensive survey of the technologies, applications, and evaluations of event prediction in the big data era. First, systematic categorization and summary of existing techniques are presented, which facilitate domain experts’ searches for suitable techniques and help model developers consolidate their research at the frontiers. Then, comprehensive categorization and summary of major application domains are provided to introduce wider applications to model developers to help them expand the impacts of their research. Evaluation metrics and procedures are summarized and standardized to unify the understanding of model performance among stakeholders, model developers, and domain experts in various application domains. Finally, open problems and future directions for this promising and important domain are elucidated and discussed.

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Section: Semantic Predictionmentioning

confidence: 99%

Event Prediction in Big Data Era: A Systematic Survey

Zhao¹

2020

Preprint

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“…The relatively untouched task of extracting implicit cause-effect from sentences was tackled by Ittoo et.al (Ittoo and Bouma, 2011). More recently, Zhao et al (Zhao et al, 2017) have proposed novel causality network embeddings for the abstract representation of causal events from News headlines. Here, the authors have primarily used four common causal connectives namely, "because", "after", "because of" and "lead to" to extract causal mentions in news headlines and constructed a network of causal relations.…”

Section: Challenges In Causality Detection and The State Of The Artmentioning

confidence: 99%

“…However to build a meaningful application that can detect an event from texts and predict its possible effects, there is a need to curate large volume of cause-effect event pairs. Further, similar events need to be grouped and generalized to super classes, over which the predictive framework can be built (Zhao et al, 2017). In this paper, we have proposed a k-means clustering of causal and effect events detected from text, using word vector representations.…”

Section: Introductionmentioning

confidence: 99%

Automatic Extraction of Causal Relations from Text using Linguistically Informed Deep Neural Networks

Dasgupta¹,

Saha²,

Dey³

et al. 2018

Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue

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In this paper we have proposed a linguistically informed recursive neural network architecture for automatic extraction of cause-effect relations from text. These relations can be expressed in arbitrarily complex ways. The architecture uses word level embeddings and other linguistic features to detect causal events and their effects mentioned within a sentence. The extracted events and their relations are used to build a causal-graph after clustering and appropriate generalization, which is then used for predictive purposes. We have evaluated the performance of the proposed extraction model with respect to two baseline systems,one a rule-based classifier, and the other a conditional random field (CRF) based supervised model. We have also compared our results with related work reported in the past by other authors on SEMEVAL data set, and found that the proposed bidirectional LSTM model enhanced with an additional linguistic layer performs better. We have also worked extensively on creating new annotated datasets from publicly available data, which we are willing to share with the community.

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“…Given all detected medical concepts in documents, we can construct the edges in a DCG. It is straightforward to link the document and concept nodes by just checking whether the concept is included in the document as the same way in [40]. For the edges linking concept nodes, we extract them from external knowledge bases as in [42].…”

Section: Document-concept Graph Constructionmentioning

confidence: 99%

Graphene

Zhao

Sboner

et al. 2019

Proceedings of the 28th ACM International Conference on Information and Knowledge Management

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Effective biomedical literature retrieval (BLR) plays a central role in precision medicine informatics. In this paper, we propose GRAPHENE, which is a deep learning based framework for precise BLR. GRAPHENE consists of three main different modules 1) graph-augmented document representation learning; 2) query expansion and representation learning and 3) learning to rank biomedical articles. The graph-augmented document representation learning module constructs a document-concept graph containing biomedical concept nodes and document nodes so that global biomedical related concept from external knowledge source can be captured, which is further connected to a BiLSTM so both local and global topics can be explored. Query expansion and representation learning module expands the query with abbreviations and different names, and then builds a CNN-based model to convolve the expanded query and obtain a vector representation for each query. Learning to rank minimizes a ranking loss between biomedical articles with the query to learn the retrieval function. Experimental results on applying our system to TREC Precision Medicine track data are provided to demonstrate its effectiveness.

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Constructing and Embedding Abstract Event Causality Networks from Text Snippets

Cited by 63 publications

References 27 publications

Event Prediction in Big Data Era: A Systematic Survey

Event Prediction in Big Data Era: A Systematic Survey

Automatic Extraction of Causal Relations from Text using Linguistically Informed Deep Neural Networks

Graphene

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