A Hybrid Approach for the Automatic Extraction of Causal Relations from Text

Sorgente, Antonio; Vettigli, Giuseppe; Mele, Francesco

doi:10.1007/978-3-319-68392-8_2

Cited by 8 publications

(12 citation statements)

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“…An example of a downloaded disease document is shown in Figure 3 which contains seven different symptom-concept expressions based on verb phrases of EDU2-EDU7 and EDU11. There are several techniques in the literature [6][7][8][9][10][11][12][13][14][15] applied for determining the cause-effect/disease-symptom relation from the unstructured data, e.g., texts, without constructing the cause-effect/disease-symptom graph or network except [7,[12][13][14][15] (see Section 2) where each cause-effect/disease-symptom relation in the cause-effect/diseasesymptom graph or network of [7,[12][13][14][15] is based on a causative-concept feature, e.g., a disease-name concept feature, connecting to one effect-concept feature, e.g., a symptomconcept feature. In contrast, unlike the aforementioned literature, our DSKG is constructed by several CErel connections where each CErel connection is the link between There are several techniques in the literature [6][7][8][9][10][11][12][13][14][15] applied for determining the cause-effect/disease-symptom relation from the unstructured data, e.g., texts, without constructing the cause-effect/disease-symptom graph or network except [7,[12][13][14][15] (see Section 2) where each cause-effect/disease-symptom relation in the cause-effect/disease-symptom graph or network of [7,[12]…”

Section: Figure 2 a General Thai Linguistic Expression Including Thai...mentioning

confidence: 99%

“…Several strategies [6][7][8][9][10][11][12][13][14][15] have been proposed to determine the cause-effect/diseasesymptom relation from the documents as the unstructured data without concerning the cause-effect/disease-symptom knowledge graph construction except [7,[12][13][14][15]. Girju [6] determined a causal relation based on a lexico syntactic pattern (NP1 causal-verb NP2) by decision tree learning.…”

Section: Related Workmentioning

confidence: 99%

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Construction of Disease-Symptom Knowledge Graph from Web-Board Documents

Pechsiri

Piriyakul

2022

Applied Sciences

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The research aim is to construct a disease-symptom knowledge graph (DSKG) as a cause-effect knowledge graph containing disease-symptom relations as a cause-effect relation type determined from downloaded documents on medical web-board resources. Each disease-symptom relation connects a disease-name concept node (a causative-concept node) to a corresponding node having a group of correlated symptom-concept/effect-concept features as common symptom-concept/effect-concept features among some disease-name concepts. The DSKG benefits non-professionals in preliminary diagnosis through a recommender web-board. There are three main problems: how to determine symptom concepts from sentences without annotation on the documents having disease-name concepts as the documents’ topic-names; how to determine the disease-symptom relations from the documents with/without complications; and how to construct the DSKG involving high dimensional symptom-concept features after union of the correlated symptom-concept groups. Therefore, we apply a word co-occurrence pattern including medical-symptom expressions from Wikipedia including MeSH and the Lexitron Dictionary to determine the symptom concepts. The Cartesian product is applied for automatic-supervised machine learning to determine the disease-symptom relation. We propose using Principal Component Analysis for constructing the DSKG by dimensionality reduction in the symptom-concept features with minimized information loss. In contrast to previous works, the proposed approach enables the DSKG construction with precise and concise representation scores of 7.8 and 9, respectively.

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Section: Figure 2 a General Thai Linguistic Expression Including Thai...mentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Construction of Disease-Symptom Knowledge Graph from Web-Board Documents

Pechsiri

Piriyakul

2022

Applied Sciences

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“…Causal relation extraction tasks can be mainly categorized into unsupervised [ 13 , 14 ], supervised [ 15 , 16 ], and hybrid approaches [ 12 , 17 , 18 ]. The unsupervised approaches are mainly pattern-based approach, which use causative verbs, causal links, and causal relations between words or phrases to extract cause effect pairs.…”

Section: Related Workmentioning

confidence: 99%

“…This is a rule-based hybrid method proposed by Sorgente [ 18 ] where in the first step, a collection of cause-effect rules are used to extract cause-effect candidates in an unsupervised manner. Unlike the dependency patterns that we use in PatternCausality , these rules consist of different causative verbs in active or passive form, with or without preposition.…”

Section: Experimental Designmentioning

confidence: 99%

Informative Causality Extraction from Medical Literature via Dependency-Tree–Based Patterns

Kabir

Almulhim

Luo

et al. 2022

J Healthc Inform Res

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Extracting cause-effect entities from medical literature is an important task in medical information retrieval. A solution for solving this task can be used for compilation of various causality relations, such as causality between disease and symptoms, between medications and side effects, and between genes and diseases. Existing solutions for extracting cause-effect entities work well for sentences where the cause and the effect phrases are name entities, single-word nouns, or noun phrases consisting of two to three words. Unfortunately, in medical literature, cause and effect phrases in a sentence are not simply nouns or noun phrases, rather they are complex phrases consisting of several words, and existing methods fail to correctly extract the cause and effect entities in such sentences. Partial extraction of cause and effect entities conveys poor quality, non-informative, and often, contradictory facts, comparing to the one intended in the given sentence. In this work, we solve this problem by designing an unsupervised method for cause and effect phrase extraction, PatternCausality , which is specifically suitable for the medical literature. Our proposed approach first uses a collection of cause-effect dependency patterns as template to extract head words of cause and effect phrases and then it uses a novel phrase extraction method to obtain complete and meaningful cause and effect phrases from a sentence. Experiments on a cause-effect dataset built from sentences from PubMed articles show that for extracting cause and effect entities, PatternCausality is substantially better than the existing methods—with an order of magnitude improvement in the F -score metric over the best of the existing methods. We also build different variants of PatternCausality , which use different phrase extraction methods; all variants are better than the existing methods. PatternCausality and its variants also show modest performance improvement over the existing methods for extracting cause and effect entities in a domain-neutral benchmark dataset, in which cause and effect entities are nouns or noun phrases consisting of one to two words.

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“…I took a hybrid approach very similar to (Sorgente et al, 2018) which first identifies a set of plausible cause-effect pairs through a set of logical rules based on lexical and structural patterns then it uses Bayesian inference to reduce the number of pairs produced by ambiguous patterns. The SemEval-2010 task 8 dataset challenge has been used to evaluate that model.…”

Section: Related Workmentioning

confidence: 99%

ETHAN at SemEval-2020 Task 5: Modelling Causal Reasoning in Language Using Neuro-symbolic Cloud Computing

Yabloko¹

2020

Proceedings of the Fourteenth Workshop on Semantic Evaluation

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I present ETHAN: Experimental Testing of Hybrid AI Node implemented entirely on free cloud computing infrastructure. The ultimate goal of this research is to create modular reusable hybrid neuro-symbolic architecture for Artificial Intelligence. As a test case I model natural language comprehension of causal relations from open domain text corpus that combines semi-supervised language model (Huggingface Transformers) with constituency and dependency parsers (Allen Institute for Artificial Intelligence). The experimental results presented in this paper show potential for SOTA-level performance and demonstrate hybrid Artificial Intelligence in action. On SemEval-2020 Task 5 Subtask 1 ETHAN achieves F1 0.878 which is the second best result at the time of my writing. On Subtask 2 ETHAN currently scores 0.673 which is within the top ten results. All code used for experiments is available as open source and can be executed directly from the web browser without any installation or configuration. I briefly discuss some theoretical approaches that led to the proposed solutions and future directions of this research.

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A Hybrid Approach for the Automatic Extraction of Causal Relations from Text

Cited by 8 publications

References 8 publications

Construction of Disease-Symptom Knowledge Graph from Web-Board Documents

Construction of Disease-Symptom Knowledge Graph from Web-Board Documents

Informative Causality Extraction from Medical Literature via Dependency-Tree–Based Patterns

ETHAN at SemEval-2020 Task 5: Modelling Causal Reasoning in Language Using Neuro-symbolic Cloud Computing

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