BERT -based Spatial Information Extraction (2020)

Abstract: Spatial information extraction is essential to understand geographical information in text. This task is largely divided to two subtasks: spatial element extraction and spatial relation extraction. In this paper, we utilize BERT (Devlin et al., 2018), which is very effective for many natural language processing applications. We propose a BERT-based spatial information extraction model, which uses BERT for spatial element extraction and R-BERT (Wu and He, 2019) for spatial relation extraction. The model was evaluated with the SemEval 2015 dataset. The result showed a 15.4% point increase in spatial element extraction and an 8.2% point increase in spatial relation extraction in comparison to the baseline model (Nichols and Botros, 2015).

## introduction extracting spatial relations from text is a type of relation extraction, focusing on the static and dynamic spatial relations in the text. it is essential for natural language understanding systems, such as robot navigation systems and questionanswering systems, to understand geographical relations or to track moving objects. for example, in the sentence, "tom is on the box," we find a static relation in which tom is the trajector, box is the landmark, and on denotes their static spatial relation. in the following sentence, "he steps down from the box to the ground," we also find a dynamic spatial relation, in which he (tom) is the mover, steps down is the trigger, box is 1 the subtasks are defined in more detail for evaluation in semeval-2015 task 8 @xcite . spatial element extraction and spatial relation extraction correspond to 1.b and 1.d tasks, respectively, in the definition. however, the source, and ground is the destination. using simple inference based on the extracted relations, we can infer a new relation: "tom is on the ground now." the task is largely divided into two subtasks 1 : spatial element extraction and spatial relation extraction. finding candidate elements for spatial relations roles, such as the trajector, landmark, and trigger, defines spatial element extraction. finding or verifying relations among the role candidates defines spatial relation extraction. many natural language processing techniques and machine learning methods have been applied to spatial information extraction. for example, a conditional random field (crf) model @xcite is used for spatial element extraction, and support vector machine (svm) @xcite and convolutional neural net (cnn) @xcite models are used for spatial relation extraction. various language resources, such as glove @xcite , wordnet @xcite , and propbank @xcite are also used for spatial information extraction. in this paper, we propose a bert-based spatial information extraction model that utilizes bert @xcite extensively for both spatial element extraction and spatial relation extraction. recently, many context-aware language models have been developed, including not only bert, but also elmo @xcite , xlnet @xcite , and gpt @xcite , among others. we chose bert simply because many downstream applications of the bert system have been developed for named entity in this paper, spatial element extraction task is extended to extract not only spatial elements such as paths, places, motions, spatial entities, for instance, but also spatial signals and motion signals. ## related works an early method of spatial information extraction was introduced as spatial role labeling (sprl) by @xcite . semeval-2012 introduced a spatial role labeling task mainly focusing on static spatial relations. semeval-2013 expanded static spatial relations to capture finegrained semantics and to include dynamic spatial relations. semeval-2015 was the first shared task conference to evaluate implementation systems for the spaceeval annotation scheme, which is the current spatial information annotation scheme @xcite . many spatial information extraction systems have been developed based on the spaceeval annotation scheme. @xcite proposed the sprl-cww model, which uses a crf model @xcite for spatial element extraction and an svm model @xcite for spatial relation extraction. it uses many input features for element extraction, such as word embedding using glove @xcite , named entities, part of speech tags and dependency parse labels. svm is used to filter out correct triples from all possible combinations of triples. d'souza and ng (2015) proposed the utd-sprl model based on svm, which includes more than 100 different features generated by a greedy feature selection technique and uses the joint detection of a relation's arguments. the x-space model proposed by @xcite uses node information, such as the place, position, location and so forth, included in wordnet for spatial element extraction. it also uses argument information in propbank for spatial relation classification. a multimodal approach that uses image and text information simultaneously in a multimodal spatial role labeling (msprl) shared task was also presented in clef 2017 @xcite , but the result was not satisfactory @xcite . @xcite extracted spatial roles and their relations by adapting a convolutional neural network based system developed for semantic role labeling. the pre-existing system was successfully adapted to spatial information extraction. dan et al. (2020) proposed spatial bert to predict the spatial relation between two entities given an image involving them. the spatial bert was composed of a spatial model, implemented with a feed forward network, and a language model, which were implemented with bert. the language model is used as complementary features to predict unseen (untrained) relations in images. despite the fact that bert is used as the language model in this approach, spatial relation extraction is limited to relation detection for the given subject and object entities in the image. our approach also uses bert but deals instead with the entire process of relation extraction from raw text; we extract spatial elements from raw text, determine their corresponding spatial roles, and find spatial relations from the spatial roles. ## spatial information extraction model we divide the spatial information extraction task into two subtasks: spatial element extraction and spatial relation extraction, according to the isospace annotation scheme @xcite . for the integrated system, we pipelined the two subtasks via a triple candidate generator. figure 1 shows the overall architecture of our system. a sentence is inputted to the element extractor, which is jointed with one of link's role modules. the element extractor outputs the spatial elements and spatial roles jointly. the spatial roles are combined to triples as spatial relation candidates by the triple candidate generator. the triple candidates are classified as either valid relations or invalid relations by the relation extractor. each module is described in the following sections. for the general architecture of spatial relation extraction, two restrictions are imposed in this work. first, only three arguments are allowed. according to isospace, we have certain arguments for each relation, as shown second, only one prime spatial role is determined in the element extraction stage. in multiple relations in a sentence, an entity may be related to multiple relations in multiple roles. in this case, it is necessary to choose only one role in a typical case. for example, in the sentence in figure 2 , 'vase' has two roles for each relation: trajector and landmark. because sharing these two different roles most frequently occurs, we decided to include these roles as one role label, traland. the triple candidate generator interprets this role label as two roles separately, trajector and landmark, for triple candidate generation. figure 2: example of sharing trajector and landmark roles: vase has two roles, trajector and landmark ## conclusion spatial information extraction is necessary for many applications, such as robot navigation and question-answering systems, to understand geographical information in text. this task is processed largely with two subtasks: spatial element extraction and spatial relation extraction. in this paper, we proposed a bert-based spatial information extraction model that uses bert @xcite for spatial element extraction and r-bert @xcite for spatial relation extraction. the two modules are connected with a pipeline through a triple candidate generator. spatial elements are extracted jointly with spatial roles that are input for spatial relation extraction. the joint model contributes to increase the performance of spatial role extraction in some cases, which is more useful for relation extraction. r-bert, which was originally used for semantic role labeling, was modified here to handle three arguments and a null argument for spatial relation extraction. our model was evaluated with the semeval 2015 dataset. the result showed a 15.4% point improvement in spatial element extraction and an 8.2% point improvement in spatial relation extraction in comparison to the baseline model @xcite . this proves that our bert-based model is very effective for spatial information extraction.