Investigating the causal relation in informative texts

Terminology ◽  
2001 ◽  
Vol 7 (2) ◽  
pp. 135-154 ◽  
Author(s):  
Caroline Barrière
Keyword(s):  
Causal Relation ◽  
Domain Model ◽  
Automatic Extraction ◽  
Causal Relations ◽  
Usage Frequency ◽  
Dynamic Dimension

Our work investigates the causal relation as it is expressed in informative texts. We view causal relations as important because of the dynamic dimension they bring to a domain model. Thorough study of a corpus leads us to distinguish two prominent classes of indicators of the causal relation: conjunctional phrases, and verbs. This paper identifies multiple knowledge-rich patterns within each class and studies their usage, frequency and noise. Results from this manual investigation informs a discussion on the feasibility of automatic extraction of the different forms of expression of the causal relation.

scholarly journals Extraction of causal relations based on SBEL and BERT model

Database ◽  
2021 ◽  
Vol 2021 ◽  
Author(s):  
Yifan Shao ◽  
Haoru Li ◽  
Jinghang Gu ◽  
Longhua Qian ◽  
Guodong Zhou
Keyword(s):  
State Of The Art ◽  
Causal Relation ◽  
Relation Extraction ◽  
The Other ◽  
Biomedical Text ◽  
Intermediate Form ◽  
Biomedical Text Mining ◽  
Causal Relations ◽  
The One ◽  
Stage 1

Abstract Extraction of causal relations between biomedical entities in the form of Biological Expression Language (BEL) poses a new challenge to the community of biomedical text mining due to the complexity of BEL statements. We propose a simplified form of BEL statements [Simplified Biological Expression Language (SBEL)] to facilitate BEL extraction and employ BERT (Bidirectional Encoder Representation from Transformers) to improve the performance of causal relation extraction (RE). On the one hand, BEL statement extraction is transformed into the extraction of an intermediate form—SBEL statement, which is then further decomposed into two subtasks: entity RE and entity function detection. On the other hand, we use a powerful pretrained BERT model to both extract entity relations and detect entity functions, aiming to improve the performance of two subtasks. Entity relations and functions are then combined into SBEL statements and finally merged into BEL statements. Experimental results on the BioCreative-V Track 4 corpus demonstrate that our method achieves the state-of-the-art performance in BEL statement extraction with F1 scores of 54.8% in Stage 2 evaluation and of 30.1% in Stage 1 evaluation, respectively. Database URL: https://github.com/grapeff/SBEL_datasets

On the Causal-Doxastic Theory of the Basing Relation

2002 ◽  
Vol 32 (4) ◽  
pp. 543-559 ◽  
Author(s):  
Daniel M. Mittag
Keyword(s):  
Recent Article ◽  
Causal Relation ◽  
The Other ◽  
Causal Relations ◽  
Basing Relation ◽  
In Virtue Of

If one is to believe that p justifiably, then one must believe p for, or because of, one's evidence or reasons in support of p. The basing relation is exactly this relation that obtains between one's belief and one's reasons for believing. Keith Allen Korcz, in a recent article published in this Journal, has argued that two conditions are each sufficient and are jointly necessary to establish basing relations between beliefs and reasons. One condition is formulated to account for basing relations that can obtain in virtue of causal relations between one's belief and reasons, and the other condition is supposed to account for basing relations which can be established independently of the instantiation of any such causal relation.

Granular Causality Applications

2012 ◽  
Vol 6 (3) ◽  
pp. 88-108
Author(s):  
Rutu Mulkar-Mehta
Keyword(s):  
Natural Language ◽  
Causal Relation ◽  
New Approach ◽  
Causal Relations ◽  
Syntactic Structures ◽  
Language Discourse ◽  
Important Phenomenon ◽  
Multiple Meanings

Causal markers, syntactic structures and connectives have been the sole identifying features for automatically extracting causal relations in natural language discourse. However, various connectives such as “and”, prepositions such as “as”, and other syntactic structures are highly ambiguous in nature, as they have multiple meanings besides causality. As a result, one cannot solely rely on lexico-syntactic markers for detection of causal phenomenon in discourse. This paper introduces the Theory of Granular Causality and describes a new approach to identify causality in natural language. Causality is often granular in nature (Mulkar-Mehta, 2011; Mazlack, 2004), and this property of causality is used to discover and infer the presence of causal relations in text. This is compared with causal relations identified using just causal markers. A precision of 0.91 and a recall of 0.79 is achieved using granularity for causal relation detection, as compared to a precision of 0.79 and a recall of 0.44 using text-based causal words for causality detection. Next, the author presents the findings for discovering causal relations between two sentences in an article. The system achieves a precision of 0.60 for discovering causality between two sentences using granular causality markers as features. The results are encouraging, and show that the granular causality is an important phenomenon in natural language

causal relations between asymmetries at the individual level?

2005 ◽  
Vol 28 (4) ◽  
pp. 596-597 ◽  
Author(s):  
rebecca g. deason ◽  
david r. andresen ◽  
chad j. marsolek
Keyword(s):  
Nervous System ◽  
Social Interactions ◽  
Causal Relation ◽  
Causal Relations ◽  
Individual Level ◽  
The Asymmetry ◽  
The Individual

studies with humans have failed to produce evidence that any direct causal relation exists between the asymmetry of one function in an individual and the asymmetry of a different function in that individual. without such evidence, factors external to an individual's nervous system, such as social interactions, may play crucial roles in explaining the directions of all asymmetries at all levels.

Hierarchical refinement and representation of the causal relation

Terminology ◽  
2002 ◽  
Vol 8 (1) ◽  
pp. 91-111 ◽  
Author(s):  
Caroline Barrière
Keyword(s):  
Knowledge Base ◽  
Causal Relation ◽  
Semantic Relation ◽  
Automatic Extraction ◽  
Conceptual Graphs ◽  
General Study ◽  
Manual Search ◽  
Degree Of Certainty ◽  
Representation Formalism

This research looks at the complexity inherent in the causal relation and the implications for its representation in a Terminological Knowledge Base (TKB). Supported by a more general study of semantic relation hierarchies, a hierarchical refinement of the causal relation is proposed. It results from a manual search of a corpus which shows that it efficiently captures and formalizes variations expressed in text. The feasibility of determining such categorization during automatic extraction from corpora is also explored. Conceptual graphs are used as a representation formalism to which we have added certainty information to capture the degree of certainty surrounding the interaction between two terms involved in a causal relation.

De Verwerving van de Negatief Causale Relatie

1995 ◽  
Vol 53 ◽  
pp. 83-94 ◽  
Author(s):  
Hanny den Ouden
Keyword(s):  
Causal Relation ◽  
Psychological Status ◽  
World Knowledge ◽  
Causal Relations ◽  
Test Base ◽  
Coherence Relations ◽  
Coherence Relation ◽  
The Right

Sanders, Spooren and Noordman (1992) provide a classification of coherence relations that is based on four primitives. These primitives are claimed to have a psychological status, in that hearers and speakers use their knowledge of these primitives to infer the right coherence relation between two clauses. The order in which children acquire coherence relations provides a test base for the classification: the classification predicts that negative causal relations are the most complex and that children therefore acquire these relations later than any of the others. This hypothesis was investigated in an experiment with 8- and 11-year-old children. In one task the children had to infer the right relation, in another task the children had to produce the right relation. Negative causal relations were compared with negative additive and positive causal relations. The items were constructed with nonsense words to eliminate the factor of world knowledge. In several respects the negative causal relation turned out to be the most complex.

scholarly journals Usage and processing of the French causal connectives ‘car’ and ‘parce que’

2017 ◽  
Vol 28 (1) ◽  
pp. 85-112 ◽  
Author(s):  
SANDRINE ZUFFEREY ◽  
WILLEM MAK ◽  
SARA VERBRUGGE ◽  
TED SANDERS
Keyword(s):  
Empirical Data ◽  
Causal Relation ◽  
Online Reading ◽  
Causal Relations ◽  
Corpus Studies ◽  
Completion Task ◽  
The Status ◽  
Reading Experiment ◽  
The Difference ◽  
Formal Equivalent

ABSTRACTThe difference between ‘car’ and ‘parce que’ is often explained in the literature by the type of causal relation (objective or subjective) that each connective prototypically conveys. Recent corpus studies have demonstrated, however, that this distinction does not hold in speech, and is fluctuating in writing. In this article, we present new empirical data to assess the status of this pair of connectives. In Experiment 1, we test French-speakers’ intuitions about ‘car’ and ‘parce que’ in a completion task, and compare these results with those of a similar experiment in Dutch. In Experiment 2, we measure the processing of objective and subjective causal relations containing ‘car’ and ‘parce que’ in an online reading experiment. Experiments 1 and 2 lead us to conclude that ‘car’ has to a large extent lost its specific procedural meaning. In the literature, the difference between ‘car’ and ‘parce que’ is also linked to a difference of register, ‘car’ being perceived as a formal equivalent of ‘parce que’. We assess the strength of this distinction in Experiment 3, by means of a completion task involving sentences from different registers. Results confirm the effect of register as a distinguishing factor between ‘car’ and ‘parce que’.

scholarly journals An Analysis of Conjunctive Relations Found on Oprah Winfrey’s Speech at Spelman University

2019 ◽  
Vol 10 (2) ◽  
Author(s):  
Tutut Setyaningrum ◽  
Dias Andris Susanto
Keyword(s):  
Causal Relation ◽  
Temporal Relation ◽  
Oprah Winfrey ◽  
Dominant Type ◽  
Causal Relations ◽  
Qualitative Design ◽  
Additive Relation

This study is focused on the Conjunctive Relations found in Oprah Winfrey's speech. In this case, conjunctive relations were analyzed through how Oprah Winfrey realized them. The objectives of this study were (1) to find out types of conjunctive relations found on Oprah Winfrey's speech (2) to find out the dominant type of conjunctive relations found on Oprah Winfrey's speech and what is that mean. To reach those two objectives, the writer used both Halliday and J. R. Martin's theory. There are two categories of conjunction namely external and internal conjunction. Later on, those two categories classified themselves into four types of relations namely additive relation, adversative relation, causal relation, and temporal relation. This study used qualitative design because it is framed in terms of using words instead of numbers. The result showed that there were 106 clauses with conjunctions in Oprah's speech. The internal conjunction found were about 52 clauses, while the external conjunction found were about 16 clauses. Both external and internal conjunction was dominated by the causal relation. The causal relation indicated that Oprah Winfrey used a lot of reasoning because causal relations made her able to convince the audience to believe with her statements. Furthermore, causal relation made her speech seems natural, influential, and emotionally convincing to the hearer.

scholarly journals An Analysis of Conjunctive Relations Found on Oprah Winfrey’s Speech at Spelman University

2019 ◽  
Vol 10 (2) ◽  
Author(s):  
Tutut Setyaningrum ◽  
Dias Andris Susanto
Keyword(s):  
Causal Relation ◽  
Temporal Relation ◽  
Oprah Winfrey ◽  
Dominant Type ◽  
Causal Relations ◽  
Qualitative Design ◽  
Additive Relation

This study is focused on the Conjunctive Relations found in Oprah Winfrey's speech. In this case, conjunctive relations were analyzed through how Oprah Winfrey realized them. The objectives of this study were (1) to find out types of conjunctive relations found on Oprah Winfrey's speech (2) to find out the dominant type of conjunctive relations found on Oprah Winfrey's speech and what is that mean. To reach those two objectives, the writer used both Halliday and J. R. Martin's theory. There are two categories of conjunction namely external and internal conjunction. Later on, those two categories classified themselves into four types of relations namely additive relation, adversative relation, causal relation, and temporal relation. This study used qualitative design because it is framed in terms of using words instead of numbers. The result showed that there were 106 clauses with conjunctions in Oprah's speech. The internal conjunction found were about 52 clauses, while the external conjunction found were about 16 clauses. Both external and internal conjunction was dominated by the causal relation. The causal relation indicated that Oprah Winfrey used a lot of reasoning because causal relations made her able to convince the audience to believe with her statements. Furthermore, causal relation made her speech seems natural, influential, and emotionally convincing to the hearer.

Rethinking Temporal Contiguity and the Judgement of Causality: Effects of Prior Knowledge, Experience, and Reinforcement Procedure

2003 ◽  
Vol 56 (5) ◽  
pp. 865-890 ◽  
Author(s):  
Marc J. Buehner ◽  
Jon May
Keyword(s):  
Causal Relation ◽  
Causal Learning ◽  
Temporal Contiguity ◽  
Learning Theories ◽  
Causal Induction ◽  
Causal Relations ◽  
Free Operant ◽  
The Impact ◽  
Causal Cognition ◽  
Reinforcement Procedure

Time plays a pivotal role in causal inference. Nonetheless most contemporary theories of causal induction do not address the implications of temporal contiguity and delay, with the exception of associative learning theory. Shanks, Pearson, and Dickinson (1989) and several replications (Reed, 1992, 1999) have demonstrated that people fail to identify causal relations if cause and effect are separated by more than two seconds. In line with an associationist perspective, these findings have been interpreted to indicate that temporal lags universally impair causal induction. This interpretation clashes with the richness of everyday causal cognition where people apparently can reason about causal relations involving considerable delays. We look at the implications of cause-effect delays from a computational perspective and predict that delays should generally hinder reasoning performance, but that this hindrance should be alleviated if reasoners have knowledge of the delay. Two experiments demonstrated that (1) the impact of delay on causal judgement depends on participants’ expectations about the timeframe of the causal relation, and (2) the free-operant procedures used in previous studies are ill-suited to study the direct influences of delay on causal induction, because they confound delay with weaker evidence for the relation in question. Implications for contemporary causal learning theories are discussed.

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