Food Hazard Event Extraction based on News and Social Media / Gueliani,Slimane Nadjmeddine  

Public ISBD Titre : Food Hazard Event Extraction based on News and Social Media Type de document : texte imprimé Auteurs : Gueliani,Slimane Nadjmeddine, Auteur ; Harrag,Fouzi, Directeur de thèse Editeur : Setif:UFA Année de publication : 2019 Importance : 1 vol (74 f .) Format : 29 cm Langues : Français (fre) Catégories : Thèses & Mémoires:Informatique Mots-clés : Social media Event Extraction Food Hazards Named Entity Recurrent Neural Networks Index. décimale : 004 - Informatique Résumé : Exchanging textual data is the most popular communication among social media users. It has become a necessity for treatment. Event extraction indicates an understanding of events across social media posts streams. Event extraction helps to take faster corrective action in natural disasters, and may save lives. The main objective of the system is to develop specific model to detect and extract the events (incidents) identified in the digital text. We proposed here a model based on Deep recurrent networks a to extract the events related to food hazard from news and social media feeds, and detect named entities for those food hazards. Then filled a food hazard template. Later, the output data which is the food hazard template could be displayed as a warning system, or used as decision support for stakeholders. Note de contenu : Sommaire pter 1 ........................................................................................................................................................1 Introduction ....................................................................................................................................................1 Problematic and motivation.........................................................................................................................1 Project goals ...............................................................................................................................................2 Thesis structure............................................................................................................................................2 Chapter 2 State of the art................................................................................................................................3 State of the art ................................................................................................................................................3 Food hazards................................................................................................................................................3 Physical hazards ...........................................................................................................................................4 chemical hazards .........................................................................................................................................4 biological hazards...........................................................................................................................................4 HACCP...........................................................................................................................................................5 Social media ...................................................................................................................................................8 Data mining ....................................................................................................................................................9 Deep learning ...............................................................................................................................................15 Neural networks ...........................................................................................................................................16 Convolutional Neural Network ....................................................................................................................17 Recurrent Neural Network ...........................................................................................................................18 Text mining ..................................................................................................................................................21 Natural Language Processing ......................................................................................................................21 Information Retrieval ...................................................................................................................................21 Information Extraction .................................................................................................................................22 Named entity recognition .............................................................................................................................25 Related Works ..............................................................................................................................................30 Conclusion....................................................................................................................................................37 Chapter 3 Proposed System..........................................................................................................................38 System description .......................................................................................................................................38 System architecture ......................................................................................................................................38 System components......................................................................................................................................45 Evaluations metrics ......................................................................................................................................46 Languages and tools .....................................................................................................................................47 Conclusion....................................................................................................................................................49 Chapter 4 Implementation and Results.........................................................................................................50 Data collection process.................................................................................................................................50 Text analysis process....................................................................................................................................52 Information extraction process.....................................................................................................................56 Named entity recognition .............................................................................................................................56 Preparing input for the model.......................................................................................................................59 Preparing output for model...........................................................................................................................62 Turning data into sequences .........................................................................................................................63 Prepare the test data for scoring ...................................................................................................................63 Create the model...........................................................................................................................................63 Train the model ............................................................................................................................................64 Results and experiments...............................................................................................................................65 General Conclusion and perspectives...........................................................................................................73 Bibliography ................................................................................................................................................73 Côte titre : MAI/0323 En ligne : https://drive.google.com/file/d/1TLYW830cezsU-EYJiKnsjIFFzZCdQxst/view?usp=shari [...] Format de la ressource électronique : pdf Food Hazard Event Extraction based on News and Social Media [texte imprimé] / Gueliani,Slimane Nadjmeddine, Auteur ; Harrag,Fouzi, Directeur de thèse . - [S.l.] : Setif:UFA, 2019 . - 1 vol (74 f .) ; 29 cm. Langues : Français (fre) Catégories : Thèses & Mémoires:Informatique Mots-clés : Social media Event Extraction Food Hazards Named Entity Recurrent Neural Networks Index. décimale : 004 - Informatique Résumé : Exchanging textual data is the most popular communication among social media users. It has become a necessity for treatment. Event extraction indicates an understanding of events across social media posts streams. Event extraction helps to take faster corrective action in natural disasters, and may save lives. The main objective of the system is to develop specific model to detect and extract the events (incidents) identified in the digital text. We proposed here a model based on Deep recurrent networks a to extract the events related to food hazard from news and social media feeds, and detect named entities for those food hazards. Then filled a food hazard template. Later, the output data which is the food hazard template could be displayed as a warning system, or used as decision support for stakeholders. Note de contenu : Sommaire pter 1 ........................................................................................................................................................1 Introduction ....................................................................................................................................................1 Problematic and motivation.........................................................................................................................1 Project goals ...............................................................................................................................................2 Thesis structure............................................................................................................................................2 Chapter 2 State of the art................................................................................................................................3 State of the art ................................................................................................................................................3 Food hazards................................................................................................................................................3 Physical hazards ...........................................................................................................................................4 chemical hazards .........................................................................................................................................4 biological hazards...........................................................................................................................................4 HACCP...........................................................................................................................................................5 Social media ...................................................................................................................................................8 Data mining ....................................................................................................................................................9 Deep learning ...............................................................................................................................................15 Neural networks ...........................................................................................................................................16 Convolutional Neural Network ....................................................................................................................17 Recurrent Neural Network ...........................................................................................................................18 Text mining ..................................................................................................................................................21 Natural Language Processing ......................................................................................................................21 Information Retrieval ...................................................................................................................................21 Information Extraction .................................................................................................................................22 Named entity recognition .............................................................................................................................25 Related Works ..............................................................................................................................................30 Conclusion....................................................................................................................................................37 Chapter 3 Proposed System..........................................................................................................................38 System description .......................................................................................................................................38 System architecture ......................................................................................................................................38 System components......................................................................................................................................45 Evaluations metrics ......................................................................................................................................46 Languages and tools .....................................................................................................................................47 Conclusion....................................................................................................................................................49 Chapter 4 Implementation and Results.........................................................................................................50 Data collection process.................................................................................................................................50 Text analysis process....................................................................................................................................52 Information extraction process.....................................................................................................................56 Named entity recognition .............................................................................................................................56 Preparing input for the model.......................................................................................................................59 Preparing output for model...........................................................................................................................62 Turning data into sequences .........................................................................................................................63 Prepare the test data for scoring ...................................................................................................................63 Create the model...........................................................................................................................................63 Train the model ............................................................................................................................................64 Results and experiments...............................................................................................................................65 General Conclusion and perspectives...........................................................................................................73 Bibliography ................................................................................................................................................73 Côte titre : MAI/0323 En ligne : https://drive.google.com/file/d/1TLYW830cezsU-EYJiKnsjIFFzZCdQxst/view?usp=shari [...] Format de la ressource électronique : pdf

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
MAI/0323	MAI/0323	Mémoire	Bibliothéque des sciences	Français	Disponible Disponible

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