Human Annotated Data

This research is aimed at the problem of disambiguating toponyms (place names) in terms of a classification derived by merging information from two publicly available gazetteers. To establish the dif- …...

This paper investigates a machine learn- ing approach for temporally ordering and anchoring events in natural language texts. To address data sparseness, we used temporal reasoning as an over- sampling …...

AbstractLack of labeled training data is a major bottleneck for neural network based aspect and opinion term extraction on product reviews. To alleviate this problem, we first propose an algorithm …...

AbstractSupervised models for Relation Extraction (RE) typically require human-annotated training data. Due to the limited size, the human-annotated data is usually incapable of covering diverse relation expressions, which could limit …...