A maximum entropy approach to natural language processing
Computational Linguistics
Automatic identification of causal relations in text and their use for improving precision in information retrieval
Machine Learning
Introduction to the special issue on temporal information processing
ACM Transactions on Asian Language Information Processing (TALIP) - Special Issue on Temporal Information Processing
A framework for resolution of time in natural language
ACM Transactions on Asian Language Information Processing (TALIP) - Special Issue on Temporal Information Processing
An unsupervised approach to recognizing discourse relations
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
A model for processing temporal references in Chinese
TASIP '01 Proceedings of the workshop on Temporal and spatial information processing - Volume 13
Automatic detection of causal relations for Question Answering
MultiSumQA '03 Proceedings of the ACL 2003 workshop on Multilingual summarization and question answering - Volume 12
Maximum entropy models for FrameNet classification
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Causal relation extraction using cue phrase and lexical pair probabilities
IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing
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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.