Extraction, analysis and representation of imperfect conditional and causal sentences by means of a semi-automatic process

Puente, Cristina; Sobrino, Alejandro; Olivas, José Á.; Merlo, Roberto

doi:10.1109/fuzzy.2010.5584115

Cited by 19 publications

(9 citation statements)

References 13 publications

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“…In [5] we showed 48 different patterns of causal and conditional sentences in English. We select the 20 ones with less ambiguity problems.…”

Section: Mining Causal Relationsmentioning

confidence: 93%

“…Causes, effects and the cause-effect link are often qualified by degrees (percentages) or words denoting different levels of strength. Puente et al [5] evidenced that, even in scientist texts, causality is not expressed categorically, but in a qualified manner. Short papers extracted from the Hawking's Physics Colloquium were processed and the mined phrases showed that, even in the realm of Physics, causal relations are gradual, in so far as the causal link is frequently tinged with semantic hedges or vague adjectives, as 'too', 'definitely', 'nearly', and so on (in bold in table 1).…”

Section: Introduction: Time and Causationmentioning

confidence: 99%

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Mining Temporal Causal Relations in Medical Texts

Sobrino

Puente

Olivas

2017

International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding

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Causal sentences are a main part of the medical explanations, providing the causes of diseases or showing the effects of medical treatments. In medicine, causal association is frequently related to time restrictions. So, some drugs must be taken before or after meals, being 'after' and 'before' temporary constraints. Thus, we conjecture that medical papers include a lot of time causal sentences. Causality involves a transfer of qualities from the cause to the effect, denoted by a directed arrow. An arrow connecting the node cause with the node effect is a causal graph. Causal graphs are an imagery way to show the causal dependencies that a sentence shows using plain text. In this paper, we will provide several programs to extract time causal sentences from medical Internet resources and to convert the obtained sentences in their equivalent causal graphs, providing an enlightening image of the relations that a text describes, showing the causeeffect links and the temporary constraints affecting their interpretation.

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“…In [5] we showed 48 different patterns of causal and conditional sentences in English. We select the 20 ones with less ambiguity problems.…”

Section: Mining Causal Relationsmentioning

confidence: 93%

Section: Introduction: Time and Causationmentioning

confidence: 99%

Mining Temporal Causal Relations in Medical Texts

Sobrino

Puente

Olivas

2017

International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding

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“…In conditional statements, causality emerges generally from the relationship between antecedent and consequence. In [14], Puente et al described a procedure to automatically display a causal graph from medical knowledge included in several medical texts, particularly those identified by the presence of certain interrogative particles [16]. These sentences are pre-processed in a convenient way in order to achieve single cause-effect structures, or causal chains from a prior cause to a final effect.…”

Section: Causality Applied To Fake News and Related Workmentioning

confidence: 99%

Fake News Detection by means of Uncertainty Weighted Causal Graphs

Garrido-Merchán¹,

Puente²,

Palacios³

2020

Preprint

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Society is experimenting changes in information consumption, as new information channels such as social networks let people share news that do not necessarily be trust worthy. Sometimes, these sources of information produce fake news deliberately with doubtful purposes and the consumers of that information share it to other users thinking that the information is accurate. This transmission of information represents an issue in our society, as can influence negatively the opinion of people about certain figures, groups or ideas. Hence, it is desirable to design a system that is able to detect and classify information as fake and categorize a source of information as trust worthy or not. Current systems experiment difficulties performing this task, as it is complicated to design an automatic procedure that can classify this information independent on the context. In this work, we propose a mechanism to detect fake news through a classifier based on weighted causal graphs. These graphs are specific hybrid models that are built through causal relations retrieved from texts and consider the uncertainty of causal relations. We take advantage of this representation to use the probability distributions of this graph and built a fake news classifier based on the entropy and KL divergence of learned and new information. We believe that the problem of fake news is accurately tackled by this model due to its hybrid nature between a symbolic and quantitative methodology. We describe the methodology of this classifier and add empirical evidence of the usefulness of our proposed approach in the form of synthetic experiments and a real experiment involving lung cancer.

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“…Let us define a causal sentence as a text representation T that includes a semantic meaning involving a causal relation between objects r(X, Y ). Causal sentences t automatically mined from the short papers included in Hawking's Physics Colloquium [31] show that, even in hard science, causal descriptions are not crisp, but imprecise, in so far as the causal links are frequently tinged with semantic hedges or vague adjectives, as 'too', 'definitely' or 'nearly'. Thus, physical causality is frequently rather approximate than categorical.…”

Section: Imprecisionmentioning

confidence: 99%

Fuzzy Stochastic Timed Petri Nets for Causal properties representation

Sobrino¹,

Garrido-Merchán²,

Puente³

2020

Preprint

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Imagery is frequently used to model, represent and communicate knowledge. In particular, graphs are one of the most powerful tools, being able to represent relations between objects. Causal relations are frequently represented by directed graphs, with nodes denoting causes and links denoting causal influence. A causal graph is a skeletal picture, showing causal associations and impact between entities. Common methods used for graphically representing causal scenarios are neurons, truth tables, causal Bayesian networks, cognitive maps and Petri Nets. Causality is often defined in terms of precedence (the cause precedes the effect), concurrency (often, an effect is provoked simultaneously by two or more causes), circularity (a cause provokes the effect and the effect reinforces the cause) and imprecision (the presence of the cause favors the effect, but not necessarily causes it). We will show that, even though the traditional graphical models are able to represent separately some of the properties aforementioned, they fail trying to illustrate indistinctly all of them. To approach that gap, we will introduce Fuzzy Stochastic Timed Petri Nets as a graphical tool able to represent time, co-occurrence, looping and imprecision in causal flow.

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Extraction, analysis and representation of imperfect conditional and causal sentences by means of a semi-automatic process

Cited by 19 publications

References 13 publications

Mining Temporal Causal Relations in Medical Texts

Mining Temporal Causal Relations in Medical Texts

Fake News Detection by means of Uncertainty Weighted Causal Graphs

Fuzzy Stochastic Timed Petri Nets for Causal properties representation

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