Explorations in Automatic Thesaurus Discovery
Explorations in Automatic Thesaurus Discovery
Automatic retrieval and clustering of similar words
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Learning random walk models for inducing word dependency distributions
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Ranking algorithms for named-entity extraction: boosting and the voted perceptron
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Frequency estimates for statistical word similarity measures
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Query expansion using random walk models
Proceedings of the 14th ACM international conference on Information and knowledge management
Discriminative Reranking for Natural Language Parsing
Computational Linguistics
Contextual search and name disambiguation in email using graphs
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
A shortest path dependency kernel for relation extraction
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Dependency-Based Construction of Semantic Space Models
Computational Linguistics
Structured retrieval for question answering
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Learning to rank typed graph walks: local and global approaches
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
Learning web page scores by error back-propagation
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Semantic classification with WordNet kernels
NAACL-Short '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
Measuring semantic relatedness with vector space models and random walks
TextGraphs-4 Proceedings of the 2009 Workshop on Graph-based Methods for Natural Language Processing
Fast query execution for retrieval models based on path-constrained random walks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Distributional memory: A general framework for corpus-based semantics
Computational Linguistics
Multi-view random walk framework for search task discovery from click-through log
Proceedings of the 20th ACM international conference on Information and knowledge management
Random walk inference and learning in a large scale knowledge base
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Graph-based term weighting for information retrieval
Information Retrieval
Graph based similarity measures for synonym extraction from parsed text
TextGraphs-7 '12 Workshop Proceedings of TextGraphs-7 on Graph-based Methods for Natural Language Processing
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We consider a parsed text corpus as an instance of a labelled directed graph, where nodes represent words and weighted directed edges represent the syntactic relations between them. We show that graph walks, combined with existing techniques of supervised learning, can be used to derive a task-specific word similarity measure in this graph. We also propose a new path-constrained graph walk method, in which the graph walk process is guided by high-level knowledge about meaningful edge sequences (paths). Empirical evaluation on the task of named entity coordinate term extraction shows that this framework is preferable to vector-based models for small-sized corpora. It is also shown that the path-constrained graph walk algorithm yields both performance and scalability gains.