Event causality extraction based on connectives analysis

Zhao, Sendong; Liu, Ting; Zhao, Sicheng; Chen, Yiheng; Nie, Jian‐Yun

doi:10.1016/j.neucom.2015.09.066

Cited by 80 publications

(37 citation statements)

References 24 publications

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“…Several strategies [5][6][7][8][9][10][11][12] have been proposed to determine the cause-effect relation from texts without the cause-effect series consideration except [12]. Reference [5] applied Text Mining to cluster the effects/symptoms of the causes/diseases from pathology reports having effect expressions as complicated technical terms based on NP.…”

Section: Related Workmentioning

confidence: 99%

“…Reference [9] applied verb-pair rules and machine learning techniques to extract the causality occurrence within several effect EDUs. There are more research works based on the lexico syntactic pattern with the causal concept as in [10] proposed the Restricted Hidden Naïve Bayes model to learn and extract the causality from the English documents. The learning features as in [10] include contextual, syntactic, position, and connective features.…”

Section: Related Workmentioning

confidence: 99%

“…Verbstrong{'ท าให ้/cause','เกิ ด/occur','ตั น/block- There are several techniques [5][6][7][8][9][10][11][12] having been applied for determining the causeeffect/causality/causal relation but not including the stimulation relation from texts (see Section 2). However, the Thai documents have several specific characteristics, such as zero anaphora or the implicit noun phrase, without word and sentence delimiters, and etc.…”

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confidence: 99%

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Extraction of cause-effect-concept pair series from web documents

Pechsiri

Mekbunditkul

2020

IJEECS

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<span>This research aims to extract a cause-effect-concept pair series of consequent event occurrences in health information of hospital web-boards. The extracted cause-effect-concept pair series representing a disease causation pathway benefits for the automatic diagnosis and solving system. Where each causative/effect event concept is expressed by an elementary discourse unit (EDU which is a simple sentence). The research has three problems; how to determine causative/effect concept EDUs from the documents containing some EDU occurrences with both causative concepts and effect concepts, how to determine the cause-effect relation between two adjacent EDUs having the discourse cue ambiguity, and how to extract cause-effect-concept pair series mingled with either a stimulation relation EDU or other non-cause-effect relation EDUs from the documents. Therefore, we apply annotated NWordCo pairs with causative-effect concepts to represent EDU pairs with causative-effect concept where the NWordCo size solved by Naïve Bayes. We also apply Naïve Bayes to solve NWordCo-concept pairs having the cause-effect relation from the adjacent EDU pairs. We then propose using cue words and the collected NWordCo-concept pairs with the cause-effect relation to extract the cause-effect-concept pair series. The research results provide the high precision of the cause-effect-concept pair series determination from the documents. </span>

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

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Extraction of cause-effect-concept pair series from web documents

Pechsiri

Mekbunditkul

2020

IJEECS

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“…In 2010, [4] applied verb-pair rules and machine learning techniques to extract the individual causality occurrence within several effect EDUs. There are more research works based on the lexico syntactic pattern with the causal concept as in [5] proposed the Restricted Hidden Naï ve Bayes model to learn and extract the causality from the English documents. The learning features [5] include contextual, syntactic, position, and connective features.…”

Section: Related Workmentioning

confidence: 99%

“…There are more research works based on the lexico syntactic pattern with the causal concept as in [5] proposed the Restricted Hidden Naï ve Bayes model to learn and extract the causality from the English documents. The learning features [5] include contextual, syntactic, position, and connective features. In 2016, [6] applied the rule-based Support Vector Machine and the temporal reasoning to extract the causal relation on a complex sentence or two simple sentences from English documents.…”

Section: Related Workmentioning

confidence: 99%

Chain of Causation Determination from Texts

Pechsiri¹,

Sukharomana²

2017

IJMLC

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Abstract-This research aims to determine the chain of causation of problem events, especially drug-addiction, expressed by several simple sentences from web documents. The chain of causation determination benefits for the problem-solving system. The research has three problems; how to determine a sentence having causative/effect event concept, how to determine the causative/effect event-concept vector size, and how to determine several consecutive causality relations (each causality is a relation between a causative-event-concept vector and an effect-event-concept vector) occurring as the chain of causation. Therefore, we apply WordCo to solve the cause/effect event concepts. We also use Support Vector Machine and WordCo features to solve the causative-event/effect-event vector size/boundary. We then propose using Naï ve Bayes to determine the consecutive causality relations between causative event-concept vectors and effect event-concept vectors. The research results provide the high precision of the chain of causation determination from the documents.Index Terms-Chain of causation, effect boundary, elementary discourse unit, WordCo.

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