The structure-mapping engine: algorithm and examples
Artificial Intelligence
Constant interaction-time scatter/gather browsing of very large document collections
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
DIRT @SBT@discovery of inference rules from text
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Coupled clustering: a method for detecting structural correspondence
The Journal of Machine Learning Research
Kernel methods for relation extraction
The Journal of Machine Learning Research
Automatic word sense discrimination
Computational Linguistics - Special issue on word sense disambiguation
Automatic retrieval and clustering of similar words
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Semi-automatic recognition of noun modifier relationships
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
IEEE Transactions on Knowledge and Data Engineering
Finding parts in very large corpora
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Learning surface text patterns for a Question Answering system
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Corpus-based Learning of Analogies and Semantic Relations
Machine Learning
Similarity of Semantic Relations
Computational Linguistics
Dependency tree kernels for relation extraction
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Expressing implicit semantic relations without supervision
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
The competence of sub-optimal theories of structure mapping on hard analogies
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
Measuring semantic similarity by latent relational analysis
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Query by analogical example: relational search using web search engine indices
Proceedings of the 18th ACM conference on Information and knowledge management
Exploiting macro and micro relations toward web intelligence
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
Cross-Language Latent Relational Search between Japanese and English Languages Using a Web Corpus
ACM Transactions on Asian Language Information Processing (TALIP)
Hi-index | 0.00 |
Measuring the similarity between implicit semantic relations is an important task in information retrieval and natural language processing. For example, consider the situation where you know an entity-pair (e.g. Google, YouTube), between which a particular relation holds (e.g. acquisition), and you are interested in retrieving other entity-pairs for which the same relation holds (e.g. Yahoo, Inktomi). Existing keyword-based search engines cannot be directly applied in this case because in keyword-based search, the goal is to retrieve documents that are relevant to the words used in the query -- not necessarily to the relations implied by a pair of words. Accurate measurement of relational similarity is an important step in numerous natural language processing tasks such as identification of word analogies, and classification of noun-modifier pairs. We propose a method that uses Web search engines to efficiently compute the relational similarity between two pairs of words. Our method consists of three components: representing the various semantic relations that exist between a pair of words using automatically extracted lexical patterns, clustering the extracted lexical patterns to identify the different semantic relations implied by them, and measuring the similarity between different semantic relations using an inter-cluster correlation matrix. We propose a pattern extraction algorithm to extract a large number of lexical patterns that express numerous semantic relations. We then present an efficient clustering algorithm to cluster the extracted lexical patterns. Finally, we measure the relational similarity between word-pairs using inter-cluster correlation. We evaluate the proposed method in a relation classification task. Experimental results on a dataset covering multiple relation types show a statistically significant improvement over the current state-of-the-art relational similarity measures.