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Deep Learning Methods for Human Behavior Recognition
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Lu, Jia. - : Auckland University of Technology, 2021
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Littérature et intelligence artificielle
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In: L'intelligence artificielle des textes ; https://hal.archives-ouvertes.fr/hal-03240145 ; D. Mayaffre, L. Vanni. L'intelligence artificielle des textes, Honoré Champion, pp.73-130, 2021, Lettres Numériques, 9782745356406 (2021)
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Impact of textual data augmentation on linguistic pattern extraction to improve the idiomaticity of extractive summaries
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In: Lecture Notes in Computer Science ; https://hal.archives-ouvertes.fr/hal-03271380 ; Matteo Golfarelli; Robert Wrembel. Lecture Notes in Computer Science, Springer, In press, Lecture Notes in Computer Science (2021)
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Multiword Expression Features for Automatic Hate Speech Detection
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In: NLDB 2021 - 26th International Conference on Natural Language & Information Systems ; https://hal.archives-ouvertes.fr/hal-03231047 ; NLDB 2021 - 26th International Conference on Natural Language & Information Systems, Jun 2021, Saarbrücken/Virtual, Germany ; http://nldb2021.sb.dfki.de/ (2021)
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Intelligence artificielle et discours politique. Quelles plus-values interprétatives ? Application aux corpus parlementaire et présidentiel contemporains
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In: L'intelligence artificielle des textes. Des algorithmes à l'interprétation ; https://hal.archives-ouvertes.fr/hal-03347997 ; L'intelligence artificielle des textes. Des algorithmes à l'interprétation, 17, Honoré Champion, pp.131-182, 2021, Lettres numériques, 9782815937467 (2021)
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Intelligence artificielle et discours politique Le cas d'Emmanuel Macron (2017-2021)
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In: https://hal.archives-ouvertes.fr/hal-03522615 ; 3ème cycle. Collège de France - Séminaire "Migrations et sociétés", France. 2021 ; Collège de France - Séminaire "Migrations et sociétés" (2021)
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Recognizing lexical units in low-resource language contexts with supervised and unsupervised neural networks
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In: https://hal.archives-ouvertes.fr/hal-03429051 ; [Research Report] LACITO (UMR 7107). 2021 (2021)
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Automatic risk detection system by audiovisual signal processing ; Système de détection automatique de risques par traitement de signaux audiovisuels
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In: https://tel.archives-ouvertes.fr/tel-03602318 ; Signal and Image processing. Université Polytechnique Hauts-de-France; Institut national des sciences appliquées Hauts-de-France, 2021. English. ⟨NNT : 2021UPHF0040⟩ (2021)
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Du groupe à l'individu, du corpus à l'expérimentation, du spectrogramme au deep learning pour la phonétique
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In: https://hal.archives-ouvertes.fr/tel-03283447 ; Linguistique. Aix-Marseille Université, 2021 (2021)
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BI-RADS Reading of Non-Mass Lesions on DCE-MRI and Differential Diagnosis Performed by Radiomics and Deep Learning.
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BI-RADS Reading of Non-Mass Lesions on DCE-MRI and Differential Diagnosis Performed by Radiomics and Deep Learning.
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Playing With Unicorns: AI Dungeon and Citizen NLP
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In: Digital Humanities Quarterly, vol 14, iss 4 (2021)
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Towards a Discourse-Level Natural Language Processing Algorithm: Characterizing Tumor Existence, Change of Existence, and its Progression from Unstructured Radiology Reports
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Huang, Ruiqi. - : eScholarship, University of California, 2021
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Geographic Question Answering with Spatially-Explicit Machine Learning Models
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Abstract:
As an important part of Artificial Intelligence (AI), Question Answering (QA) aims at generating answers to questions in natural language. With the advancement of deep learning technology, we have witnessed substantial progress in open-domain question answering. However, QA systems are still struggling to answer questions that involve geographic entities or concepts and that require spatial operations. In order to tackle these challenges, this dissertation specifically focuses on the problem of Geographic Question Answering (GeoQA) and develops a series of spatially-explicit machine learning models to handle different GeoQA tasks. First, in Chapter 1, we discuss the challenges of answering geographic questions and the uniqueness of GeoQA. A classification of geographic questions has been presented to facilitate the development of GeoQA. Next, in Chapter 2 a spatially-explicit query relaxation model is presented to demonstrate the usefulness of geographic information and spatial thinking in the geographic question answering and query relaxation process. To develop a more generalizable approach for GeoQA and other geospatial tasks, in Chapter 3, we present a general-purpose multi-scale representation learning model for geographic locations which can be utilized in multiple downstream tasks. It has been later on utilized to build a location-aware knowledge graph embedding model for a knowledge graph-based GeoQA model in Chapter 4. Only relying on points as the spatial representations for geographic entities is not sufficient to answer many geographic questions that involve spatial relations such as topological relations and cardinal direction relations. So in Chapter 5, we present a polygon encoder that can be used to answer multiple types of spatial relation questions. In the end, we draw a conclusion by listing several challenges of GeoQA which have not been solved in this dissertation and point out some future research directions. We hope this dissertation can reveal the importance of GeoQA and demonstrate the usefulness of spatially-explicit machine learning models on geospatial problems. We also hope GeoQA will become a unique research domain and serve as an important part of Geographic Artificial Intelligent (GeoAI) research.
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Keyword:
Artificial intelligence; Computer science; Geographic Artificial Intelligence; Geographic information science and geodesy; Geographic Question Answering; Geometric Deep Learning; Knowledge Graph; Spatially-Explicit Machine Learning
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URL: https://escholarship.org/uc/item/8cp3c11d
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Automatic Speech Recognition : from hybrid to end-to-end approach ; Reconnaissance automatique de la parole à large vocabulaire : des approches hybrides aux approches End-to-End
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In: https://tel.archives-ouvertes.fr/tel-03616588 ; Intelligence artificielle [cs.AI]. Université Paul Sabatier - Toulouse III, 2021. Français. ⟨NNT : 2021TOU30116⟩ (2021)
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Large vocabulary automatic speech recognition: from hybrid to end-to-end approaches ; Reconnaissance automatique de la parole à large vocabulaire : des approches hybrides aux approches End-to-End
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In: https://hal.archives-ouvertes.fr/tel-03269807 ; Son [cs.SD]. Université toulouse 3 Paul Sabatier, 2021. Français (2021)
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Neuro-computational models of language processing
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In: EISSN: 2333-9691 ; Annual Review of Linguistics ; https://hal.archives-ouvertes.fr/hal-03334485 ; Annual Review of Linguistics, Annual Reviews, In press, ⟨10.1146/lingbuzz/006147⟩ (2021)
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Representation learning of writing style, application to news recommendation ; Apprentissage de la représentation du style écrit, application à la recommandation d’articles d’actualité
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In: https://tel.archives-ouvertes.fr/tel-03420487 ; Apprentissage [cs.LG]. Université Paris-Saclay, 2021. Français. ⟨NNT : 2021UPASG010⟩ (2021)
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Hate speech and offensive language detection using transfer learning approaches ; Détection du discours de haine et du langage offensant utilisant des approches de Transfer Learning
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In: https://tel.archives-ouvertes.fr/tel-03276023 ; Document and Text Processing. Institut Polytechnique de Paris, 2021. English. ⟨NNT : 2021IPPAS007⟩ (2021)
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Automatic sentence simplification using controllable and unsupervised methods ; Simplification automatique de phrases à l'aide de méthodes contrôlables et non supervisées
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In: https://tel.archives-ouvertes.fr/tel-03543971 ; Computation and Language [cs.CL]. Sorbonne Université, 2021. English. ⟨NNT : 2021SORUS265⟩ (2021)
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