Deriving concept hierarchies from text
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Inferring hierarchical descriptions
Proceedings of the eleventh international conference on Information and knowledge management
Automatic retrieval and clustering of similar words
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Word association norms, mutual information, and lexicography
ACL '89 Proceedings of the 27th annual meeting on Association for Computational Linguistics
Web-scale information extraction in knowitall: (preliminary results)
Proceedings of the 13th international conference on World Wide Web
Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Corpus-based Learning of Analogies and Semantic Relations
Machine Learning
Acquiring ontological knowledge from query logs
Proceedings of the 16th international conference on World Wide Web
A Thesaurus Construction Method from Large ScaleWeb Dictionaries
AINA '07 Proceedings of the 21st International Conference on Advanced Networking and Applications
The Google Similarity Distance
IEEE Transactions on Knowledge and Data Engineering
Weakly-supervised discovery of named entities using web search queries
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
INGS '08 Proceedings of the 2008 International Workshop on Information-Explosion and Next Generation Search
Unsupervised Discovery of Coordinate Terms for Multiple Aspects from Search Engine Query Logs
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
WWW sits the SAT: Measuring Relational Similarity on the Web
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
What you seek is what you get: extraction of class attributes from query logs
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Measuring semantic similarity by latent relational analysis
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Wikipedia mining for an association web thesaurus construction
WISE'07 Proceedings of the 8th international conference on Web information systems engineering
Searching coordinate terms with their context from the web
WISE'06 Proceedings of the 7th international conference on Web Information Systems
Extracting mnemonic names of people from the web
ICADL'06 Proceedings of the 9th international conference on Asian Digital Libraries: achievements, Challenges and Opportunities
Query by analogical example: relational search using web search engine indices
Proceedings of the 18th ACM conference on Information and knowledge management
Paraphrase alignment for synonym evidence discovery
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
On-the-Fly generation of facets as navigation signs for web objects
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part I
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We propose a method for detecting related terms of a given term quickly using a conventional Web search engine. There are many kinds of related terms. For example, hypernyms and hyponyms are basic related terms that are treated in dictionaries. Synonyms and coordinate terms are also well defined related terms. Topic terms and description terms represent topics of the given term and they are vaguely defined. There are other related terms such as abbreviations and nicknames. The proposed method can be used these many kinds of related terms. It extracts related terms from text resources only from Web search results, which consist of titles, snippets, and URLs of Web pages. We use two different kind of lexico-syntactic patterns to extract related terms from the search results, and they are called bi-directional lexico-syntactic patterns. The proposed method can be applied to both languages where words are separated by a space such as English and Korean and ones where words are not separated by a space such as Japanese and Chinese. The proposed method does not need any advanced natural language processing such as morphological analysis or syntactic parsing. It works relatively fast with good precision.