Mining Causality for Explanation Knowledge from Text

Pechsiri, Chaveevan; Kawtrakul, Asanee

doi:10.1007/s11390-007-9093-8

Cited by 7 publications

(30 citation statements)

References 6 publications

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Section: Related Workmentioning

confidence: 99%

“…Pechsiri and Kawtrakul (2007) [4] proposed verb-pair rules learned by two different machine learning techniques (NB and SVM) to extract causality with multiple EDUs of a causative unit and multiple EDUs of an effect unit. The verb-pair rules [4] have been represented by the following formula where V c is the causative verb concept set, V e is the effect verb concept set, C is the Boolean variables of causality and non-causality. Each causative verb concept (v c , where v c ∈ V c ) and each effect verb concept (v e , where v e ∈ V e ) are referred to WordNet [12] (http://wordnet.princeton.edu/) and the predefined plant disease information from the Department of Agriculture (http://www.doa.go.th/).…”

Section: Related Workmentioning

confidence: 99%

“…When to apply Centering Theory (CT) [4] is whenever the transition state of the center of attention is the smooth shift occurrence (the attention agent, mostly being a subject of a sentence, is changed), the boundary is ended. For example: "If the brown leaphopper aphids suck sap from rice plant, leaves will be yellow.…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, the causality in our research has been expressed through documents in the form of EDU (Elementary Discourse Unit) as in [4] where EDU is defined by [5] as a clause which is equivalent to a Thai simple sentence. This research emphasizes the main verb of each EDU.…”

Section: Introductionmentioning

confidence: 99%

“…This research emphasizes the main verb of each EDU. This causality expression has been classified as the inter-causal EDU and the intra-causal EDU by [4]. [4] defined the inter-causal EDU as a causality expression of multiple EDUs in both causative unit and effect unit, for example:…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Explanation Knowledge Graph Construction Through Causality Extraction from Texts

Pechsiri

Piriyakul

2010

J. Comput. Sci. Technol.

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Explanation knowledge expressed by a graph, especially in the graphical model, is essential to comprehend clearly all paths of effect events in causality for basic diagnosis. This research focuses on determining the effect boundary using a statistical based approach and patterns of effect events in the graph whether they are consequence or concurrence without temporal markers. All necessary causality events from texts for the graph construction are extracted on multiple clauses/EDUs (Elementary Discourse Units) which assist in determining effect-event patterns from written event sequences in documents. To extract the causality events from documents, it has to face the effect-boundary determination problems after applying verb pair rules (a causative verb and an effect verb) to identify the causality. Therefore, we propose Bayesian Network and Maximum entropy to determine the boundary of the effect EDUs. We also propose learning the effect-verb order pairs from the adjacent effect EDUs to solve the effect-event patterns for representing the extracted causality by the graph construction. The accuracy result of the explanation knowledge graph construction is 90% based on expert judgments whereas the average accuracy results from the effect boundary determination by Bayesian Network and Maximum entropy are 90% and 93%, respectively.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Explanation Knowledge Graph Construction Through Causality Extraction from Texts

Pechsiri

Piriyakul

2010

J. Comput. Sci. Technol.

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A survey on extraction of causal relations from natural language text

Han

Poon

2022

Knowl Inf Syst

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As an essential component of human cognition, cause–effect relations appear frequently in text, and curating cause–effect relations from text helps in building causal networks for predictive tasks. Existing causality extraction techniques include knowledge-based, statistical machine learning (ML)-based, and deep learning-based approaches. Each method has its advantages and weaknesses. For example, knowledge-based methods are understandable but require extensive manual domain knowledge and have poor cross-domain applicability. Statistical machine learning methods are more automated because of natural language processing (NLP) toolkits. However, feature engineering is labor-intensive, and toolkits may lead to error propagation. In the past few years, deep learning techniques attract substantial attention from NLP researchers because of its powerful representation learning ability and the rapid increase in computational resources. Their limitations include high computational costs and a lack of adequate annotated training data. In this paper, we conduct a comprehensive survey of causality extraction. We initially introduce primary forms existing in the causality extraction: explicit intra-sentential causality, implicit causality, and inter-sentential causality. Next, we list benchmark datasets and modeling assessment methods for causal relation extraction. Then, we present a structured overview of the three techniques with their representative systems. Lastly, we highlight existing open challenges with their potential directions.

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A survey on non-factoid question answering systems

Breja

Jain

2021

International Journal of Computers and Applications

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Mining Causality for Explanation Knowledge from Text

Cited by 7 publications

References 6 publications

Explanation Knowledge Graph Construction Through Causality Extraction from Texts

Explanation Knowledge Graph Construction Through Causality Extraction from Texts

A survey on extraction of causal relations from natural language text

A survey on non-factoid question answering systems

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