A bootstrapping based approach for open geo-entity relation extraction

Extracting spatial relations and semantic relations between two geo-entities from Web texts, asks robust and effective solutions. This paper puts forward a novel approach: firstly, the characteristics of terms (part-of-speech, position and distance) are analyzed by means of bootstrapping. Secondly, the weight of each term is calculated and the keyword is picked out as the clue of geo-entity relations. Thirdly, the geo-entity pairs and their keywords are organized into structured information. Finally, an experiment is conducted with Baidubaike and Stanford CoreNLP. The study shows that the presented method can automatically explore part of the lexical features and find additional relational terms which neither the domain expert knowledge nor large scale corpora need. Moreover, compared with three classical frequency statistics methods, namely Frequency, TF-IDF and PPMI, the precision and recall are improved about 5% and 23% respectively.