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Precise geocoding and time normalization for text requires that location andtime phrases be identified. Many state-of-the-art geoparsers and temporalparsers suffer from low recall. Categories commonly missed by parsers are:nouns used in a non- spatiotemporal sense, adjectival and adverbial phrases,prepositional phrases, and numerical phrases. We collected and annotated dataset by querying commercial web searches API with such spatiotemporalexpressions as were missed by state-of-the- art parsers. Due to the high costof sentence annotation, active learning was used to label training data, and anew strategy was designed to better select training examples to reduce labelingcost. For the learning algorithm, we applied an average perceptron trainedFeaturized Hidden Markov Model (FHMM). Five FHMM instances were used to createan ensemble, with the output phrase selected by voting. Our ensemble model wastested on a range of sequential labeling tasks, and has shown competitiveperformance. Our contributions include (1) an new dataset annotated with namedentities and expanded spatiotemporal expressions; (2) a comparison of inferencealgorithms for ensemble models showing the superior accuracy of BeliefPropagation over Viterbi Decoding; (3) a new example re-weighting method foractive ensemble learning that 'memorizes' the latest examples trained; (4) aspatiotemporal parser that jointly recognizes expanded spatiotemporalexpressions as well as named entities.

Recognizing Extended Spatiotemporal Expressions by Actively Trained Average Perceptron Ensembles