LIP6@CLEF2017: Multi-Modal Spatial Role Labeling using Word Embeddings Working notes
Abstract
We report our participation to the multi-modal Spatial Role Labeling (mSpRL) lab at CLEF 2017. The task consists in extracting and classifying spatial relationships from textual data and associated images. Our approach focuses on the classification part as we use a base-line system for the extraction of the relations: we train a linear Support Vector Machine (SVM) model to classify hand-crafted vectors representing spatial relations. We present the obtained experiments and discuss also the effect of model parameters. Finally, we conclude the paper and introduce ideas for future developments.
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