Extracting interrogative intents and concepts from geo-analytic questions

Abstract

Abstract. Understanding syntactic and semantic structure of geographic questions is a necessary step towards true geographic question-answering (GeoQA) machines. The empirical basis for the understanding of the capabilities expected from GeoQA systems are geographic question corpora. Available corpora in English have been mostly drawn from generic Web search logs or limited user studies, supporting the focus of GeoQA systems on retrieving factoids: factual knowledge about particular places and everyday processes. Yet, the majority of questions enquired about in the spatial sciences go beyond simple place facts, with more complex analytical intents informing the questions. In this paper, we introduce a new corpus of geo-analytic questions drawn from English textbooks and scientific articles. We analyse and compare this corpus with two general-purpose GeoQA corpora in terms of grammatical complexity and semantic concepts, using a new parsing method that allows us to differentiate and quantify patterns of a question’s intent.

Geographic Question Answering: Challenges, Uniqueness, Classification, and Future Directions

“…Compared with the above GeoQA work that mainly focus on answering factoid geographic questions, geo-analytical question answering proposed by Scheider et al (2020) went beyond simple geographic facts but focuses more on questions with complex spatial analytical intents (Xu et al, 2020). A simple factoid geographic question such as Question A1 can be answered by executing one or two spatial operations on the respective spatial footprints of geographic entities.…”

“…In contrast, geo-analytical questions usually require generating a GIS analytic workflows. Example questions include how much green space will Tom see while running through Amsterdam (Question M) Scheider et al (2020) and what is the best site for a new landfill in the Netherlands (Question N) (Xu et al, 2020).…”

“…Section 4 discussed key work on GeoQA which focus on certain types of geographic questions. Some of them (Chen et al, 2013;Punjani et al, 2018;Hamzei et al, 2019;Xu et al, 2020) provided a classification of geographic questions within the scope of question types they can handle. In this section, we provide a general classification of geographic questions which attempts to cover all aspects of GeoQA.…”

Neural factoid geospatial question answering

“…In GQA, answering geographic questions can be based on diverse information sources such as textual information [18,50], spatial databases [8], and spatially-enabled knowledge bases [19]. Based on the types of geographic questions, existing work on geographic question answering can be classified into four types [48]: (1) factoid GQA [55,68], which focuses on answering questions based on geographic factoids; (2) geo-analytical QA [60,64], which focuses on questions with complex spatial analytical intent; (3) scenario-based GQA [10,11,32], which associates questions with a scenario described with a map or a paragraph of text; and (4) visual GQA [33,46], that links questions to an image or video. In this paper, we focus on the first of these: namely factoid GQA, and aim to answer questions through a spatially-enabled knowledge base.…”

“…The selected variables are related to the intent of the questions. Here, we extended the heuristic for extracting intent of geographic questions developed by Xu et al [64]. The intent is derived using the following rules:…”