Causal Relation Extraction Using Cue Phrase and Lexical Pair Probabilities

Chang, Du-Seong; Choi, Key‐Sun

doi:10.1007/978-3-540-30211-7_7

Cited by 47 publications

(39 citation statements)

References 9 publications

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“…Previous researches concerned the discourse marker [11] and NP pairs with the cue phrase [14] . However, the problems of implicit and ambiguity discourse markers in combination with zero anaphora lead us to focus on verbs because verbs can express events with a consequence or a concurrence.…”

Section: Resultsmentioning

confidence: 99%

“…However, there are some implicit temporal markers or expressions in our corpora, to which the methods from [14,16,18] cannot be applied to extract automatically the effect-event patterns whether it is consequence or concurrence. Finally, we are aiming at constructing the graph for representing the extracted knowledge (which is the extracted inter-causal EDU) from Thai textual data (which has specific characteristics, e.g., the sentence-like name entity, zero anaphora, and the lack of sentence delimiter) in natural language description, by applying the statistical model and language processing to improve the effect-boundary determination and also to construct the explanation knowledge graph.…”

Section: Related Workmentioning

confidence: 96%

“…Chang and Choi's work [14] has been modified [10,15] , that aims to extract causal relations based on one complex sentence to construct the causal network/graph for the term protein with the two relations of the causal relation and the hypernym relation. The edge between a cause node and an effect node of each causal relation represents the causal probability with the directed/indirected causal relation.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Explanation Knowledge Graph Construction Through Causality Extraction from Texts

Pechsiri

Piriyakul

2010

J. Comput. Sci. Technol.

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Explanation knowledge expressed by a graph, especially in the graphical model, is essential to comprehend clearly all paths of effect events in causality for basic diagnosis. This research focuses on determining the effect boundary using a statistical based approach and patterns of effect events in the graph whether they are consequence or concurrence without temporal markers. All necessary causality events from texts for the graph construction are extracted on multiple clauses/EDUs (Elementary Discourse Units) which assist in determining effect-event patterns from written event sequences in documents. To extract the causality events from documents, it has to face the effect-boundary determination problems after applying verb pair rules (a causative verb and an effect verb) to identify the causality. Therefore, we propose Bayesian Network and Maximum entropy to determine the boundary of the effect EDUs. We also propose learning the effect-verb order pairs from the adjacent effect EDUs to solve the effect-event patterns for representing the extracted causality by the graph construction. The accuracy result of the explanation knowledge graph construction is 90% based on expert judgments whereas the average accuracy results from the effect boundary determination by Bayesian Network and Maximum entropy are 90% and 93%, respectively.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 96%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Explanation Knowledge Graph Construction Through Causality Extraction from Texts

Pechsiri

Piriyakul

2010

J. Comput. Sci. Technol.

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“…Therefore, we can create a feature encoding whether this is the case. In cases where such semantic lexicons are not available, they may be automatically constructed, although with noise, using causal mining 6 · Ryuichiro Higashinaka and Hideki Isozaki techniques such as [Marcu and Echihabi 2002;Girju 2003;Chang and Choi 2004].…”

Section: Causal Relation Featuresmentioning

confidence: 99%

Automatically Acquiring Causal Expression Patterns from Relation-annotated Corpora to Improve Question Answering for why-Questions

Higashinaka

Isozaki

2008

ACM Transactions on Asian Language Information Processing

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This paper describes our approach for answering why-questions that we initially introduced at NTCIR-6 QAC-4. The approach automatically acquires causal expression patterns from relationannotated corpora by abstracting text spans annotated with a causal relation and by mining syntactic patterns that are useful for distinguishing sentences annotated with a causal relation from those annotated with other relations. We use these automatically acquired causal expression patterns to create features to represent answer candidates, and use these features together with other possible features related to causality to train an answer candidate ranker that maximizes the QA performance with regards to the corpus of why-questions and answers. NAZEQA, a Japanese why-QA system based on our approach, clearly outperforms baselines with a Mean Reciprocal Rank (top-5) of 0.223 when sentences are used as answers and with a MRR (top-5) of 0.326 when paragraphs are used as answers, making it presumably the best-performing fully implemented why-QA system. Experimental results also verified the usefulness of the automatically acquired causal expression patterns.

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“…In 2003, [2] proposed decision tree learning the causal relation from a sentence based on the lexico syntactic pattern (NP1 causal-verb NP2). In 2004, [3] used cue-phrase and the statistical approach to NP-pair probabilities to solve the causal relation occurrence within two EDUs. In 2010, [4] applied verb-pair rules and machine learning techniques to extract the individual causality occurrence within several effect EDUs.…”

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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Causal Relation Extraction Using Cue Phrase and Lexical Pair Probabilities

Cited by 47 publications

References 9 publications

Explanation Knowledge Graph Construction Through Causality Extraction from Texts

Explanation Knowledge Graph Construction Through Causality Extraction from Texts

Automatically Acquiring Causal Expression Patterns from Relation-annotated Corpora to Improve Question Answering for why-Questions

Chain of Causation Determination from Texts

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