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This book provides an overview of multiple aspects of spatial language understanding and explores recent trends of modeling based on very large foundational models and their applications. The authors address the following five main themes: spatial semantic representations in both symbolic and sub-symbolic spaces; spatial information extraction computational models; reasoning over spatial language; commonsense spatial understanding; and multiple modern and state-of-the-art downstream applications of spatial language understanding including dialogue systems, narrative discourse, and grounding language in the physical world with the multi-modal problem settings. One of the essential functions of natural language is to express spatial relationships between objects. Linguistic constructs can encode highly complex structures of objects, spatial relations between them, and patterns of motion through space relative to a reference point. The complexity of spatial language understanding and its importance in downstream tasks that involve grounding the language in the physical world has become evident and important to the natural language processing research community. In addition, this topic has recently attracted the attention of various sub-communities in the intersection of natural language, computer vision, and robotics.
Table des matières
Introduction.- Spatial Representations.- Spatial Information Extraction.- Spatial Reasoning.- Spatial Commonsense Knowledge.- Spatial Language Applications.
A propos de l'auteur
Parisa Kordjamshidi, Ph.D, is an Associate Professor in the Department of Computer Science and Engineering at Michigan State University. She received her Ph.D. in Computer Science from KU Leuven. Dr. Kordjamshidi's main research interests are natural language processing, combining vision and language, and neurosymbolic AI.
Marie-Francine (Sien) Moens, Ph.D, is Professor Emerita in the Department of Computer Science and Director of the Language Intelligence and Information Retrieval Lab (LIIR) at KU Leuven. Her research interests include machine learning for natural language, speech, and image processing; multimedia information retrieval; deep learning and latent variable models; and language understanding, information extraction from text, and content recognition.
James Pustejovsky, Ph.D., is the TJX Feldberg Chair in Computer Science at Brandeis University, where he is also Chair of the Linguistics Program, Chair of the Computational Linguistics M.S. Program, and Director of the Lab for Linguistics and Computation. He has authored numerous books on lexical and computational semantics, linguistic annotation, and temporal and spatial reasoning. He conducts research in areas of computational linguistics, lexical semantics, multimodal interactions and reasoning, situated grounding, and developing standards and annotated datasets for machine learning.
Résumé
This book provides an overview of multiple aspects of spatial language understanding and explores recent trends of modeling based on very large foundational models and their applications. The authors address the following five main themes: spatial semantic representations in both symbolic and sub-symbolic spaces; spatial information extraction computational models; reasoning over spatial language; commonsense spatial understanding; and multiple modern and state-of-the-art downstream applications of spatial language understanding including dialogue systems, narrative discourse, and grounding language in the physical world with the multi-modal problem settings. One of the essential functions of natural language is to express spatial relationships between objects. Linguistic constructs can encode highly complex structures of objects, spatial relations between them, and patterns of motion through space relative to a reference point. The complexity of spatial language understanding and its importance in downstream tasks that involve grounding the language in the physical world has become evident and important to the natural language processing research community. In addition, this topic has recently attracted the attention of various sub-communities in the intersection of natural language, computer vision, and robotics.
