Improved CasRel model for joint extraction of geographic entity and overlapping space relation

doi:10.11947/j.AGCS.2023.20210722

Abstract

Abstract: Geospatial text contains rich location information, which provides important support for the location of geographic entities. The extraction of geographic entities and spatial relationships is the key to obtaining location information. Aiming at the construction of the geospatial relation corpus, we take the sentences containing the spatial relation as the unit from the Encyclopedia of China Geography, and complete the construction of the geospatial relation corpus by marking the spatial relation in the sentence. For the pipeline relation extraction model which ignores the correlation between geographic entities and spatial relations, we use enhanced representation through knowledge integration (ERNIE) and BiLSTM+self-attention mechanism+BiLSTM (BAB) layers to improve the CasRel model to achieve joint extraction of geographic entities and spatial relationships, and solve the extraction of overlapping spatial relationships in geospatial texts by cascading annotation. Experiments show that on the DuIE dataset and our constructed geospatial corpus, compared with the CasRel joint extraction model, the F₁ value of our model is increased by 4.81% and 1.97%, respectively, and the extraction effect of overlapping spatial relationships is effectively improved.

Key words: spatial relation extraction, ERNIE, geographic entities, corpus of geospatial relations, relationship overlap

CLC Number:

P227

JIANG Meng, YANG Chuncheng, SHANG Haibin, QIN Zhilong, WANG Zefan. Improved CasRel model for joint extraction of geographic entity and overlapping space relation[J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(8): 1387-1397.

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