Interpretation Of Event Spatio-temporal And Attribute Information In Chinese Text

This thesis is supported by the national "863" project "The Research of Associated Updating of the Ubiquitous Spatial Information and Mining of Subject-oriented Spatio-temporal Information". An interpretation approach of event spatio-temporal and attribute information in Chinese Text is explored in this thesis. The contributions will provide a data and technology support for the associated updating of the ubiquitous spatial information, the spatial information and knowledge services under an unified spatio-temporal framework, and the spatio-temporal mining analysis of event information. Furthermore, it will provide decision-making services for the event risk assessment, public safety, and the other major issues. In Chinese text, descriptions of event spatio-temporal and attribute information are unstructured, qualitative and uncertain. According to the above description characters, this research is carried out according to the main idea of "text description, normalization expression, structured extraction, visualization reconstruction" of event information in Chinese text. The main research contents and results are described as follows:(1) Structured expression of event spatio-temporal and attribute information in Chinese textWith an analysis of the linguistic features and semantic structures of event, spatial, temporal, attribute information described in Chinese text, a representation framework and annotation schema are identified and specified. Moreover, GATE (General Architecture for Text Engineering) is introduced as an annotation platform, and an annotated corpus based on the Web data source is developed in case of events of public emergencies. The annotation schema and annotated corpus will provide a standard training and testing data support for the extraction of event information.(2) Extraction of event spatio-temporal and attribute information in Chinese textBased on description regularities of temporal information in Chinese text, a interpretation approach is illustrated for extraction, reasoning and standardization of temporal information, which combines trigger words and rule-based model. The values of precision, recall and F-measure are75.00%,88.24%and40.54%respectively. Place names and event names are recognized with a Condition Random Field model, and spatial relations are extracted with a rule-based model. For the recognition of event names, the values of precision, recall and F-measure were respectively82.08%,80.18% and81.12%. Moreover, A Bootstrapping method is explored for the extraction of event attributes. For the quantitative attribute information, the values of precision, recall and F-measure can reach80.80%,85.16%respectively.(3) Automatic event classification based on spatio-temporal informationEvery event has temporal, spatial and attribute properties. A classification method of event information is developed which integrates contextual and semantic information. It emphasizes the spatial and temporal elements for event tracking, and discovers that feature items of trigger words, part of speech, place names, temporal information, event names and attributes have an important contribution for event classification. Moreover, some special phenomenons of abbreviation and alias are reasoned according to different language units, i.e. sentence, paragraph and chapter. The experiment results show that it can reach a classification accuracy of92.30%and80.60%in a closed and open testing respectively.(4) Matching and visualization of event spatio-temporal informationBased on the spatial data source of national gazetteer, a matching and visualization method for event information is presented. With a hierarchical matching of place names, spatial relations and temporal information, event information are expressed in a GIS spatio-temporal framework. Moreover, with a consistency constraint of "temporal information-spatial information-concept type", a judgement method of theme event, and the reconstruction of spatio-temporal process are presented. Finally, a clustering analysis of the spatio-temporal pattern for event information is finished.The studies proposed in this thesis suggest that the combination of rule-model and statistical model can effectively extract event information from Chinese text, however, reasonable and effective feature items play an important role in the learning process of statistical models. For different types of events, the extraction models of temporal information, place names, spatial relations, event names and event types are universal and transplantable, however, their attribute information are with many differences. Therefore, the knowledge base and learning model need to be modified for specific types of events. The judgement of event type is flexible, complex, semantic ambigous and uncertain, in other words it is a multi-label classification problem. This paper integrates the contextual and semantic information of part of speech, place names, temporal information, event names, attributes and trigger words, which can effectively improve the event classification performance. Among the Matching and visualization of spatio-temporal information, the coverage and quality of spatial data, as well as the interpretation model of spatial relations have a large impact on the performance. Overall, the proposed approach in this dissertation for the interpretation of spatio-temproal information, attributes and event classification in Chinese text is effective, but its integration with GIS is greatly depended on the mapping spatial data.