A Hybrid Model of Classification and Generation for Spatial Relation Extraction (2022)

Abstract: Extracting spatial relations from texts is a fundamental task for natural language understanding and previous studies only regard it as a classification task, ignoring those spatial relations with null roles due to their poor information. To address the above issue, we first view spatial relation extraction as a generation task and propose a novel hybrid model HMCGR for this task. HMCGR contains a generation and a classification model, while the former can generate those null-role relations and the latter can extract those non-null-role relations to complement each other. Moreover, a reflexivity evaluation mechanism is applied to further improve the accuracy based on the reflexivity principle of spatial relation. Experimental results on SpaceEval show that HMCGR outperforms the SOTA baselines significantly.

## introduction spatial relation extraction focuses on identifying the relationship between two geographical entities in natural language texts. currently, only a few studies focused on this task in the nlp community, while most studies aimed at the other tasks of relation extraction, such as temporal and causal relation extraction. however, spatial information is one kind of critical information for natural language understanding, which can benefit the downstream nlp applications, such as spatial domain query @xcite , spatial reference @xcite and data forecasting @xcite . various kinds of schemes have been proposed to represent spatial relation. as one of the semeval evaluation tasks, spaceeval @xcite proposes an annotation scheme adopted from iso-space @xcite and its goals include identifying and classifying items from an inventory of spatial concepts, such as topological relations, orientational relations, and motion, etc. commonly, this task needs to extract the spatial elements and classify static and dynamic spatial relations into three types: the move link (movelink), the qualitative spatial link (qslink), and the orientation link (olink). movelink connects motion-events with corresponding mover-participants as a triplet of three roles (mover, goal, trigger), while qslink and olink refer to the topological relation and nontopological relation between two spatial elements, respectively, and are formalized as a triplet of three roles @xcite . following previous work, we also simplified the whole task as figure 1 and only focus on extracting qslink/olink/movelink from texts. thus, a spatial relation is defined as a triplet with three spatial types. the spatial relation can be divided into two classes: null-role and non-null-role relations. the former refers to a relation containing null-value roles, such as two movelinks and one olink in figure 1 , while the latter (e.g., qslink in figure 1 ) refers to a relation whose three roles are fulfilled the values extracted from sentences. almost all previous studies regarded spatial relation extraction as a classification task using traditional machine learning @xcite or neural network methods @xcite . those classification models work well on extracting those non-null-role relations due to their rich information. however, they often suffer from those null-role relations. the reason is that some information is missing in these relations. moreover, they also cannot benefit from the knowledge of the spatial schema, such as the roles and their relations. in the annotation stage, annotators usually not only annotate relations and relation types, but also provide a description or basis for their annotation implicitly. therefore, we hope the model can simulate a human and provide a target sentence instead of a simple label index for understanding the spatial relation deeper. the target sentence in generation models can describe the relation between all spatial elements and it allows null slots (i.e., roles) to exist. thus, the generation model not only can more explicitly learn the semantics of the spatial relations through such a form of the learning goal, but also can generate those null-role relations. moreover, the classification model and the generation model have their complementary advantages. the former usually has better performance on nonull-role relations, while the latter can introduce prior knowledge to capture the semantics of nullrole relations better and its results are in a natural language expression with stronger interpretability @xcite . therefore, we combine the advantages of the classification and generation models to further capture different knowledge. in this paper, we propose a novel hybrid model hmcgr (hybrid model of classification, generation and reflexivity) for spatial relation extraction, which contains a generation model and a classification model. specially, the former can generate those null-role relations and the latter can extract those non-null-role relations to complement each other. moreover, a reflexivity evaluation mechanism is applied to further improve the accuracy based on the reflexivity principle of spatial relation. experimental results on the spaceeval dataset shows that our hmcgr outperforms the sota baselines significantly. ## related work various kinds of schemes have been proposed to represent spatial relations. spatialml @xcite characterized directional and topological relations among locations in terms of a region calculus. the sprl task @xcite developed a semantic role labeling scheme focus-ing on the main roles in spatial relations. spatial relation extraction was introduced as subtask at se-meval 2012 @xcite , semeval 2013 @xcite and semeval 2015 @xcite . as the task 8 of se-meval 2015, spaceeval proposed an annotation scheme adopted from iso-space, and it enriched sprl's semantics by refining the granularity. most of previous studies were evaluated on this dataset. the task of spatial relation extraction can be divided into traditional machine learning and neural network methods. the former highly relies on manual features or explicit syntactic structures. nichols and botros (2015) used a crf layer to extract spatial elements, and then introduced svm to classify spatial relations. d'souza and ng (2015) proposed a sieve-based model where various kinds of manual features are generated by a greedy feature selection technique. salaberri et al. (2015) introduced external knowledge as a supplement to spatial information, in which wordnet and propbank provided information on many spatial elements. kim and lee (2016) proposed a korean spatial relation extraction model using dependency relations to find the proper elements to fulfill roles. with the wide application of neural network, ramrakhiyani et al. (2019) generated candidate relations by dependency parsing and classified the candidates with a bilstm model. shin et al. (2020) first used bert-crf to extract the spatial roles and then introduced r-bert (wu and he, 2019) to extract the spatial relations. besides, a few studies focused on multi-modal spatial relation extraction. for example, dan et al. (2020) proposed a spatial bert which gives two spatial entities and a picture to determine spatial relations. ## hmcgr figure 2 shows the overall architecture of our model hmcgr. as a whole, hmcgr can be divided into four modules, i.e., candidate triplet ex- ## experimentation in this section, we first introduce the experimental settings and then report the experimental results. ## analysis in this section, we analysis our model hmcgr on different aspects, such as null-role relations, three modules, components in cls and errors. ## conclusion in this paper, we propose a novel hybrid model hm-cgr for spatial relation extraction. the generation model gen can generate those null-role relations, while the classification model cls can extract those non-null-role relations to complement each other. moreover, a reflexivity evaluation mechanism is applied to further improve the accuracy based on the reflexivity of spatial relation. experimental results on the spaceeval dataset show that our hmcgr outperforms the sota baseline significantly. our future work will focus on how to extract those null-role relations effectively.