Paper: Identifying Constant and Unique Relations by using Time-Series Text

ACL ID D12-1081
Title Identifying Constant and Unique Relations by using Time-Series Text
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2012
Authors

Because the real world evolves over time, nu- merous relations between entities written in presently available texts are already obsolete or will potentially evolve in the future. This study aims at resolving the intricacy in con- sistently compiling relations extracted from text, and presents a method for identifying constancy and uniqueness of the relations in the context of supervised learning. We ex- ploit massive time-series web texts to induce features on the basis of time-series frequency and linguistic cues. Experimental results con- firmed that the time-series frequency distribu- tions contributed much to the recall of con- stancy identification and the precision of the uniqueness identification.