WordNet: a lexical database for English
Communications of the ACM
Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Co-occurrence Retrieval: A Flexible Framework for Lexical Distributional Similarity
Computational Linguistics
Evaluating WordNet-based Measures of Lexical Semantic Relatedness
Computational Linguistics
The Google Similarity Distance
IEEE Transactions on Knowledge and Data Engineering
Discovering intermediate entities from two examples by using web search engine indices
Proceedings of the 4th International Conference on Uniquitous Information Management and Communication
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Extensive work has been done in recent years on automatically grouping words into categories. For example, {Wednesday, Monday, Tuesday} could be grouped into a 'days of week' category. However, not only grouping the words, but also ordering them is important, e.g. Monday?Tuesday?Wednesday. The order relation is an important aspect that could be used to enrich existing ontologies, to determine the sequence of actions for planning tasks, and to determine the order of user's preferences for a set of items, etc. However, automatically determining the order relation seems to have been ignored. Pairwise similarity metric commonly used to cluster words may not be well suited for the ordering task. Therefore, we propose a new metric designed for the ordering task. We utilize statistical proximity features of the terms in the documents (in a large corpus) in order to determine the order relations between terms. The effectiveness of the proposed method is verified in experimental settings against orders provided by human subjects.