NeuroSPE: A neuro‐net spatial relation extractor for natural language text fusing gazetteers and pretrained models

Abstract Spatial relations are frequently described and used in natural language texts, and relations play a core role in a range of applications—from supporting geographic information retrieval in natural language texts to locating people and objects in natural disaster response situations. In this article, we present a neuro‐net spatial extraction model (NeuroSPE) designed to address various language irregularities (i.e., a variety of sentence structures) that occur in natural language texts. We also propose a two‐stage workflow to generate a training dataset based on a collection of words and their associated frequencies. The first stage of the proposed workflow focuses on processing the words in the input data and their associated frequencies; then, the words are segmented into a set of groups and used to accelerate model training. The second stage automatically generates a variety of sentences that include two geographic entities and related spatial relation terms through deep learning iteration based on a unigram language model. We evaluate our method both qualitatively and quantitatively using a real dataset. The experimental results demonstrate that the proposed two‐stage workflow effectively extracts spatial relations from natural language texts and outperforms other current state‐of‐the‐art approaches.