A Machine-Oriented Logic Based on the Resolution Principle
Journal of the ACM (JACM)
Slot Grammar: A System for Simpler Construction of Practical Natural Language Grammars
Proceedings of the International Symposium on Natural Language and Logic
The Journal of Machine Learning Research
The semantics of grammar formalisms seen as computer languages
ACL '84 Proceedings of the 10th International Conference on Computational Linguistics and 22nd annual meeting on Association for Computational Linguistics
Extracting relations with integrated information using kernel methods
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
A composite kernel to extract relations between entities with both flat and structured features
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
YAGO: A Large Ontology from Wikipedia and WordNet
Web Semantics: Science, Services and Agents on the World Wide Web
Convolution kernels on constituent, dependency and sequential structures for relation extraction
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
DBpedia: a nucleus for a web of open data
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Learning 5000 relational extractors
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Relation extraction with relation topics
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Introduction to "This is Watson"
IBM Journal of Research and Development
Question analysis: how watson reads a clue
IBM Journal of Research and Development
IBM Journal of Research and Development
Automatic knowledge extraction from documents
IBM Journal of Research and Development
Finding needles in the haystack: search and candidate generation
IBM Journal of Research and Development
Typing candidate answers using type coercion
IBM Journal of Research and Development
Textual evidence gathering and analysis
IBM Journal of Research and Development
Structured data and inference in DeepQA
IBM Journal of Research and Development
A framework for merging and ranking of answers in DeepQA
IBM Journal of Research and Development
Hypothesis Generation and Testing in Event Profiling for Digital Forensic Investigations
International Journal of Digital Crime and Forensics
Introduction to "This is Watson"
IBM Journal of Research and Development
Question analysis: how watson reads a clue
IBM Journal of Research and Development
IBM Journal of Research and Development
Automatic knowledge extraction from documents
IBM Journal of Research and Development
Finding needles in the haystack: search and candidate generation
IBM Journal of Research and Development
Typing candidate answers using type coercion
IBM Journal of Research and Development
Textual evidence gathering and analysis
IBM Journal of Research and Development
Structured data and inference in DeepQA
IBM Journal of Research and Development
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Law
Hi-index | 0.00 |
Detecting semantic relations in text is an active problem area in natural-language processing and information retrieval. For question answering, there are many advantages of detecting relations in the question text because it allows background relational knowledge to be used to generate potential answers or find additional evidence to score supporting passages. This paper presents two approaches to broad-domain relation extraction and scoring in the DeepQA question-answering framework, i.e., one based on manual pattern specification and the other relying on statistical methods for pattern elicitation, which uses a novel transfer learning technique, i.e., relation topics. These two approaches are complementary; the rule-based approach is more precise and is used by several DeepQA components, but it requires manual effort, which allows for coverage on only a small targeted set of relations (approximately 30). Statistical approaches, on the other hand, automatically learn how to extract semantic relations from the training data and can be applied to detect a large amount of relations (approximately 7,000). Although the precision of the statistical relation detectors is not as high as that of the rule-based approach, their overall impact on the system through passage scoring is statistically significant because of their broad coverage of knowledge.