Paper: Clustering Clauses for High-Level Relation Detection: An Information-theoretic Approach

ACL ID P07-1057
Title Clustering Clauses for High-Level Relation Detection: An Information-theoretic Approach
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2007
Authors

Recently, there has been a rise of in- terest in unsupervised detection of high- level semantic relations involving com- plex units, such as phrases and whole sentences. Typically such approaches are faced with two main obstacles: data sparseness and correctly generalizing from the examples. In this work, we describe the Clustered Clause represen- tation, which utilizes information-based clustering and inter-sentence dependen- cies to create a simplified and generalized representation of the grammatical clause. We implement an algorithm which uses this representation to detect a predefined set of high-level relations, and demon- strate our model’s effectiveness in over- coming both the problems mentioned.