SpRL-CWW: Spatial Relation Classification with Independent Multi-class Models

Abstract

In this paper we describe the SpRL-CWW entry into SemEval 2015: Task 8 SpaceEval. It detects spatial and motion relations as defined by the ISO-Space specifications in two phases: (1) it detects spatial elements and spatial/motion signals with a Conditional Random Field model that uses a combination of distributed word representations and lexicosyntactic features; (2) given relation candidate tuples, it simultaneously detects relation types and labels the spatial roles of participating elements by using a combination of syntactic and semantic features in independent multi-class classification models for each relation type. In evaluation on the shared task data, our system performed particularly well on detection of elements and relations in unannotated data.

BERT-based Spatial Information Extraction

“…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).…”

“…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).…”

“…Many spatial information extraction systems have been developed based on the SpaceEval annotation scheme. Nichols and Botros (2015) proposed the SpRL-CWW model, which uses a CRF model (Lafferty et al, 2001) for spatial element extraction and an SVM model (Suykens and Vandewalle, 1999) for spatial relation extraction. It uses many input features for element extraction, such as word embedding using GloVe (Pennington et al, 2014), named entities, part of speech tags and dependency parse labels.…”

“…A sentence is segmented into word pieces and they are inputted to BERT to extract the spatial elements and spatial roles jointly. In previous methods, many features are extracted through preprocessing for learning by CRFs (Nichols and Botros, 2015). However, the BERT-based spatial element extraction module does not require any preprocessing for feature extraction; rather, it requires only raw text as input for fine tuning.…”

Sieve-Based Spatial Relation Extraction with Expanding Parse Trees

“…In other words, the classifier will output a 1 if and only if (1) the elements in the set form a relation and (2) their roles in the relation are correct. The systems participating in SpaceEval all seem to be in favor of joint approaches (D'Souza and Ng, 2015;Nichols and Botros, 2015;Salaberri et al, 2015).…”