Extracting causal knowledge from a medical database using graphical patterns

Khoo, Christopher S. G.; Chan, Syin; Niu, Yun

doi:10.3115/1075218.1075261

Cited by 147 publications

(80 citation statements)

References 10 publications

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“…Our scores of precision (76.5%) and recall (82%) compare favorably with those reported by other state-of-the-art algorithms [2,4,7,8,9,14]. These latter techniques, however, extensively relied on manually-crafted knowledge (e.g.…”

Section: Causal Relation Extractionsupporting

confidence: 68%

“…The pattern-based algorithms in [8,9] extract text segments that match hand-crafted patterns, e.g. "is the result of".…”

Section: Related Workmentioning

confidence: 99%

“…Causal relations are fundamental in many disciplines, including philosophy, psychology and linguistics. In Natural Language Processing (NLP), algorithms have been developed for discovering causal relations from large generalpurpose [2,4,7,8,14] and bio-medical corpora [9]. These algorithms rely extensively on hand-coded knowledge (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Extracting Explicit and Implicit Causal Relations from Sparse, Domain-Specific Texts

Ittoo

Bouma

2011

Natural Language Processing and Information Systems

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Abstract. Various supervised algorithms for mining causal relations from large corpora exist. These algorithms have focused on relations explicitly expressed with causal verbs, e.g. "to cause". However, the challenges of extracting causal relations from domain-specific texts have been overlooked. Domain-specific texts are rife with causal relations that are implicitly expressed using verbal and non-verbal patterns, e.g. "reduce", "drop in", "due to". Also, readily-available resources to support supervised algorithms are inexistent in most domains. To address these challenges, we present a novel approach for causal relation extraction. Our approach is minimally-supervised, alleviating the need for annotated data. Also, it identifies both explicit and implicit causal relations. Evaluation results revealed that our technique achieves state-of-the-art performance in extracting causal relations from domain-specific, sparse texts. The results also indicate that many of the domain-specific relations were unclassifiable in existing taxonomies of causality.

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Section: Causal Relation Extractionsupporting

confidence: 68%

“…The pattern-based algorithms in [8,9] extract text segments that match hand-crafted patterns, e.g. "is the result of".…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Extracting Explicit and Implicit Causal Relations from Sparse, Domain-Specific Texts

Ittoo

Bouma

2011

Natural Language Processing and Information Systems

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“…Transitivity is an important logical property, in addition to antisymmetry and reflexivity, that underlies much of the inference-making in hierarchies, for example, query expansion (Nie 2003). These semantic primitives are often used to further improve the performance of decoding partwhole relations from natural language text by generating linguistic markers from some widely used keywords that convey the meaning associated with the semantic primitives underlying the propositional interpretation of part-whole relations (Girju and Moldovan 2002), (Khoo, Chan et al 2000). For instance, 'cause' as a keyword for functional dependence, 'component' or 'part' as keywords for independence of existence and 'such as' or 'for example' as keywords for similarity of type.…”

Section: Propositional Approach To Compositionmentioning

confidence: 99%

A Cognitive Reference based Model for Learning Compositional Hierarchies with Whole-composite Tags

Saxena

Bindal

Wegmann

2013

Proceedings of the International Conference on Knowledge Discovery and Information Retrieval and the International Conference O

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Abstract:A compositional hierarchy is the default organization of knowledge acquired for the purpose of specifying the design requirements of a service. Existing methods for learning compositional hierarchies from natural language text, interpret composition as an exclusively propositional form of part-whole relations. Nevertheless, the lexico-syntactic patterns used to identify the occurrence of part-whole relations fail to decode the experientially grounded information, which is very often embedded in various acts of natural language expression, e.g. construction and delivery. The basic idea is to take a situated view of conceptualization and model composition as the cognitive act of invoking one category to refer to another. Mutually interdependent set of categories are considered conceptually inseparable and assigned an independent level of abstraction in the hierarchy. Presence of such levels in the compositional hierarchy highlight the need to model these categories as a unified-whole wherein they can only be characterized in the context of the behavior of the set as a whole. We adopt an object-oriented representation approach that models categories as entities and relations as cognitive references inferred from syntactic dependencies. The resulting digraph is then analyzed for cyclic references, which are resolved by introducing an additional level of abstraction for each cycle.

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“…The approach of pattern matching was used to extract the marked causal relation which with cause indicator effect pattern in text [1,4]. Besides, the machine learning technique was also introduced to extract the marked causal relation [5].…”

Section: Introductionmentioning

confidence: 99%

Using dual-layer CRFs for event causal relation extraction

Liu

et al. 2011

IEICE Electron. Express

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Traditional methods for event causal relation extraction covered only part of the explicit causal relation in text. This paper presents a method for event causal relation extraction by using duallayer Conditional Random Fields (CRFs). The method casts the problem of event causal relation extraction as event sequence labeling and employs dual-layer CRFs model to label the causal relation of event sequence. The first layer of the CRFs model is used to label the semantic role of causal relation of the events, and then the output of the first layer is passed to the second layer for labeling the boundaries of the event causal relation. Experimental results show that our method not only covers each class of explicit event causal relation in the text, but also achieves good performance and the F-Measure of the overall performance arrives at 85.3%.

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Extracting causal knowledge from a medical database using graphical patterns

Cited by 147 publications

References 10 publications

Extracting Explicit and Implicit Causal Relations from Sparse, Domain-Specific Texts

Extracting Explicit and Implicit Causal Relations from Sparse, Domain-Specific Texts

A Cognitive Reference based Model for Learning Compositional Hierarchies with Whole-composite Tags

Using dual-layer CRFs for event causal relation extraction

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