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LA PISCINE 1969 (Alain Delon, Romy Schneider, Maurice Ronet, Jane Birkin) #delon #romy #ronet
La Piscine. 1969.
Jean-Paul (Alain Delon) et Marianne (Romy Schneider) vivent heureux dans une villa avec piscine au dessus de Saint-Tropez. Harry (Maurice Ronet), play-boy vieillissant passe avec sa fille de 18 ans Pénélope (Jane Birkin)...
Réalisation : Jacques Deray
Scénario : Jean-Emmanuel Conil & Jean-Claude Carrière
Musique : Michel Legrand
published: 18 Jun 2022
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La Piscine | Extended Trailer & Excerpts (Klara Tavakoli Goesche)
SPOILER ALERT (Do not watch if you haven't seen the movie.:)) An original trailer I made for the film 'La Piscine'. Edited by me. Film from 1969, directed by Jacques Deray, starring Romy Schneider, Alain Delon, Maurice Ronet, Jane Birkin. I used music from the film, as well as Mozart's Piano Concerto No. 21: Andante.
published: 09 Sep 2010
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La piscine, 1969 – Ending
La piscine, 1969 by Jacques Demy featuring Romy Schneider and Alain Delon
published: 13 Feb 2022
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MOVIE SCENE | La Piscine | Alain Delon & Romy Schneider
La Piscine (The Swimming Pool) is a 1969 Italian-French film directed by Jacques Deray, starring Alain Delon, Romy Schneider, Maurice Ronet and Jane Birkin.
published: 05 May 2022
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La Piscine 1969 trailer
published: 25 Apr 2017
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LA PISCINA - 1969 Drama/Crimen - Alain Delon - Romy Schneider .
published: 08 Mar 2023
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La Piscine (1969) En Français Streaming
La Piscine
published: 05 Mar 2017
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LA PISCINE | Behind the scenes | Alain Delon, Romy Schneider, Maurice Ronet & Jane Birkin
La Piscine (The Swimming Pool) is a 1969 Italian-French film directed by Jacques Deray, starring Alain Delon, Romy Schneider, Maurice Ronet and Jane Birkin.
Song: Ask Yourself Why
Music by Michel Legrand
Lyrics by Alan Bergman and Marilyn Bergman
Song by Ruth Price
published: 19 May 2019
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CINEMA REBORN 2023 - THE SWIMMING POOL/LA PISCINE Trailer
Premiere Australian screening of the restoration of Jacques Deray's sleek and sexy thriller starring Alain Delon and Romy Schneider. Check the Randwick Ritz website for session times and bookings
published: 01 Feb 2023
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La Piscine Trailer
The new 4K restoration of Jacques Deray's LA PISCINE, starring Alain Delon, Romy Schneider, Maurice Ronet, and Jane Birkin, opening Sep 3 at the Avalon Theatre.
published: 30 Aug 2021
7:03
LA PISCINE 1969 (Alain Delon, Romy Schneider, Maurice Ronet, Jane Birkin) #delon #romy #ronet
La Piscine. 1969.
Jean-Paul (Alain Delon) et Marianne (Romy Schneider) vivent heureux dans une villa avec piscine au dessus de Saint-Tropez. Harry (Maurice Rone...
La Piscine. 1969.
Jean-Paul (Alain Delon) et Marianne (Romy Schneider) vivent heureux dans une villa avec piscine au dessus de Saint-Tropez. Harry (Maurice Ronet), play-boy vieillissant passe avec sa fille de 18 ans Pénélope (Jane Birkin)...
Réalisation : Jacques Deray
Scénario : Jean-Emmanuel Conil & Jean-Claude Carrière
Musique : Michel Legrand
https://wn.com/La_Piscine_1969_(Alain_Delon,_Romy_Schneider,_Maurice_Ronet,_Jane_Birkin)_Delon_Romy_Ronet
La Piscine. 1969.
Jean-Paul (Alain Delon) et Marianne (Romy Schneider) vivent heureux dans une villa avec piscine au dessus de Saint-Tropez. Harry (Maurice Ronet), play-boy vieillissant passe avec sa fille de 18 ans Pénélope (Jane Birkin)...
Réalisation : Jacques Deray
Scénario : Jean-Emmanuel Conil & Jean-Claude Carrière
Musique : Michel Legrand
- published: 18 Jun 2022
- views: 425180
5:11
La Piscine | Extended Trailer & Excerpts (Klara Tavakoli Goesche)
SPOILER ALERT (Do not watch if you haven't seen the movie.:)) An original trailer I made for the film 'La Piscine'. Edited by me. Film from 1969, directed by Ja...
SPOILER ALERT (Do not watch if you haven't seen the movie.:)) An original trailer I made for the film 'La Piscine'. Edited by me. Film from 1969, directed by Jacques Deray, starring Romy Schneider, Alain Delon, Maurice Ronet, Jane Birkin. I used music from the film, as well as Mozart's Piano Concerto No. 21: Andante.
https://wn.com/La_Piscine_|_Extended_Trailer_Excerpts_(Klara_Tavakoli_Goesche)
SPOILER ALERT (Do not watch if you haven't seen the movie.:)) An original trailer I made for the film 'La Piscine'. Edited by me. Film from 1969, directed by Jacques Deray, starring Romy Schneider, Alain Delon, Maurice Ronet, Jane Birkin. I used music from the film, as well as Mozart's Piano Concerto No. 21: Andante.
- published: 09 Sep 2010
- views: 480719
1:29
La piscine, 1969 – Ending
La piscine, 1969 by Jacques Demy featuring Romy Schneider and Alain Delon
La piscine, 1969 by Jacques Demy featuring Romy Schneider and Alain Delon
https://wn.com/La_Piscine,_1969_–_Ending
La piscine, 1969 by Jacques Demy featuring Romy Schneider and Alain Delon
- published: 13 Feb 2022
- views: 50822
1:35
MOVIE SCENE | La Piscine | Alain Delon & Romy Schneider
La Piscine (The Swimming Pool) is a 1969 Italian-French film directed by Jacques Deray, starring Alain Delon, Romy Schneider, Maurice Ronet and Jane Birkin.
La Piscine (The Swimming Pool) is a 1969 Italian-French film directed by Jacques Deray, starring Alain Delon, Romy Schneider, Maurice Ronet and Jane Birkin.
https://wn.com/Movie_Scene_|_La_Piscine_|_Alain_Delon_Romy_Schneider
La Piscine (The Swimming Pool) is a 1969 Italian-French film directed by Jacques Deray, starring Alain Delon, Romy Schneider, Maurice Ronet and Jane Birkin.
- published: 05 May 2022
- views: 152122
3:06
LA PISCINE | Behind the scenes | Alain Delon, Romy Schneider, Maurice Ronet & Jane Birkin
La Piscine (The Swimming Pool) is a 1969 Italian-French film directed by Jacques Deray, starring Alain Delon, Romy Schneider, Maurice Ronet and Jane Birkin.
So...
La Piscine (The Swimming Pool) is a 1969 Italian-French film directed by Jacques Deray, starring Alain Delon, Romy Schneider, Maurice Ronet and Jane Birkin.
Song: Ask Yourself Why
Music by Michel Legrand
Lyrics by Alan Bergman and Marilyn Bergman
Song by Ruth Price
https://wn.com/La_Piscine_|_Behind_The_Scenes_|_Alain_Delon,_Romy_Schneider,_Maurice_Ronet_Jane_Birkin
La Piscine (The Swimming Pool) is a 1969 Italian-French film directed by Jacques Deray, starring Alain Delon, Romy Schneider, Maurice Ronet and Jane Birkin.
Song: Ask Yourself Why
Music by Michel Legrand
Lyrics by Alan Bergman and Marilyn Bergman
Song by Ruth Price
- published: 19 May 2019
- views: 169523
2:22
CINEMA REBORN 2023 - THE SWIMMING POOL/LA PISCINE Trailer
Premiere Australian screening of the restoration of Jacques Deray's sleek and sexy thriller starring Alain Delon and Romy Schneider. Check the Randwick Ritz web...
Premiere Australian screening of the restoration of Jacques Deray's sleek and sexy thriller starring Alain Delon and Romy Schneider. Check the Randwick Ritz website for session times and bookings
https://wn.com/Cinema_Reborn_2023_The_Swimming_Pool_La_Piscine_Trailer
Premiere Australian screening of the restoration of Jacques Deray's sleek and sexy thriller starring Alain Delon and Romy Schneider. Check the Randwick Ritz website for session times and bookings
- published: 01 Feb 2023
- views: 892
1:40
La Piscine Trailer
The new 4K restoration of Jacques Deray's LA PISCINE, starring Alain Delon, Romy Schneider, Maurice Ronet, and Jane Birkin, opening Sep 3 at the Avalon Theatre....
The new 4K restoration of Jacques Deray's LA PISCINE, starring Alain Delon, Romy Schneider, Maurice Ronet, and Jane Birkin, opening Sep 3 at the Avalon Theatre.
https://wn.com/La_Piscine_Trailer
The new 4K restoration of Jacques Deray's LA PISCINE, starring Alain Delon, Romy Schneider, Maurice Ronet, and Jane Birkin, opening Sep 3 at the Avalon Theatre.
- published: 30 Aug 2021
- views: 41294
-
Gorgeous Laminar Water Features
Beautiful Hobert Pool with awesome laminars water features!!
published: 13 Aug 2015
-
Laminar Flow DISAMBIGUATION
Captain Disillusion gets his hands wet with some experiments, and lets you watch.
Please consider supporting my videos on: http://www.patreon.com/CaptainDisillusion
published: 14 Feb 2019
-
Improving Polyphone Disambiguation for Mandarin Chinese by Combining Mix-pooling Strategy and Wi...
Title: Improving Polyphone Disambiguation for Mandarin Chinese by Combining Mix-pooling Strategy and Window-based Attention - (3 minutes introduction)
Authors: Junjie Li (Ping An Technology, China), Zhiyu Zhang (National Tsing Hua University, Taiwan), Minchuan Chen (Ping An Technology, China), Jun Ma (Ping An Technology, China), Shaojun Wang (Ping An Technology, China), Jing Xiao (Ping An Technology, China)
Category: Speech Synthesis: Linguistic processing, paradigms and other topics
Abstract: In this paper, we propose a novel system based on word-level features and window-based attention for polyphone disambiguation, which is a fundamental task for Grapheme-to-phoneme (G2P) conversion of Mandarin Chinese. The framework aims to combine a pre-trained language model with explicit word-lev...
published: 03 Feb 2022
-
Improving Polyphone Disambiguation for Mandarin Chinese by Combining Mix-pooling Strategy and Wi...
Title: Improving Polyphone Disambiguation for Mandarin Chinese by Combining Mix-pooling Strategy and Window-based Attention - (longer introduction)
Authors: Junjie Li (Ping An Technology, China), Zhiyu Zhang (National Tsing Hua University, Taiwan), Minchuan Chen (Ping An Technology, China), Jun Ma (Ping An Technology, China), Shaojun Wang (Ping An Technology, China), Jing Xiao (Ping An Technology, China)
Category: Speech Synthesis: Linguistic processing, paradigms and other topics
Abstract: In this paper, we propose a novel system based on word-level features and window-based attention for polyphone disambiguation, which is a fundamental task for Grapheme-to-phoneme (G2P) conversion of Mandarin Chinese. The framework aims to combine a pre-trained language model with explicit word-level ...
published: 03 Feb 2022
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093 Keyword Disambiguation Using Transformers and Clustering to Build Cleaner Knowledge - NODES2022
Natural language processing is an indispensable toolkit to build knowledge graphs from unstructured data. However, it comes with a price. Keywords and entities in unstructured texts are ambiguous - the same concept can be expressed by many different linguistic variations. The resulting knowledge graph would thus be polluted with many nodes representing the same entity without any order. In this session, we show how the semantic similarity based on transformer embeddings and agglomerative clustering can help in the domain of academic disciplines and research fields and how Neo4j improves the browsing experience of this knowledge graph.
Speakers: Federica Ventruto, Alessia Melania Lonoce
Format: Full Session 30-45 min
Level: Advanced
Topics: #KnowledgeGraph, #MachineLearning...
published: 30 Nov 2022
-
Disambiguation, In-Jokes, and Name Collisions: What You Need to Know When Naming a Python Project
Thursday Bram
https://2018.northbaypython.org/schedule/presentation/15/
This talk covers key issues Python programmers run into when naming new projects. We'll go over the following:
* Commonly used naming schemas in the Python community
* Current and past project names (including those that many newcomers to Python struggle with)
* Techniques to avoid similar confusion in the future (covering both name selection and documentation)
We'll even talk about Monty Python and its long-term impact on the Python programming language.
A Python conference north of the Golden Gate
North Bay Python is a single-track conference with a carefully curated set of talks representing the diverse Python community and their different areas of interest.
If a topic is less to your interest, or...
published: 16 Nov 2018
-
Top 10 Worst TV Shows of All Time
Boy, there sure is some terrible television in the world…. Join http://www.WatchMojo.com as we count down our picks for theTop 10 Worst TV Shows of All Time. Subscribe►►http://www.youtube.com/subscription_center?add_user=watchmojo Facebook►►http://www.Facebook.com/WatchMojo. Twitter►►http://www.Twitter.com/WatchMojo Instagram►►http://instagram.com/watchmojo Suggestion Tool►►http://www.WatchMojo.com/suggest Channel Page►►http://www.youtube.com/watchmojo
For this list, we'll be scouring TV's lengthy history in search of the programs that are universally viewed as lacking in quality.
Special thanks to our users Liza Davydzenkava, SuperSaiyanKirby100, DonovanTPS, Jerome Magajes, Aeryk Marcellus Bacon, TylerKienzlen@gmail., sarahjessicaparkerth, mac121mr0, Brody Nicholas Eiffel Jay, jhwoe6, P...
published: 19 Jan 2016
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Deduplication and Author Disambiguation of Streaming Records via Supervised Models -Reza Karimi
"Here we present a general supervised framework for record deduplication and author-disambiguation via Spark. This work differentiates itself by - Application of Databricks and AWS makes this a scalable implementation. Compute resources are comparably lower than traditional legacy technology using big boxes 24/7. Scalability is crucial as Elsevier's Scopus data, the biggest scientific abstract repository, covers roughly 250 million authorships from 70 million abstracts covering a few hundred years. - We create a fingerprint for each content by deep learning and/or word2vec algorithms to expedite pairwise similarity calculation. These encoders substantially reduce compute time while maintaining semantic similarity (unlike traditional TFIDF or predefined taxonomies). We will briefly discuss ...
published: 30 Oct 2017
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Mod-01 Lec-31 Wordnet; Metonymy and Word Sense Disambiguation
Natural Language Processing by Prof. Pushpak Bhattacharyya, Department of Computer science & Engineering,IIT Bombay.For more details on NPTEL visit http://nptel.iitm.ac.in
published: 03 Jul 2012
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Chatbot Disambiguation Demonstration
Your Chatbot Must Be Able To Disambiguate. Disambiguation Is Part & Parcel Of Human Conversations And Should Be Part Of Your Chatbot Experience.
IBM Watson has a built-in feature which allows for the configuration of disambiguation. In this practical example you can toggle the feature on or off.
Apart from this you can set the message explaining the clarification, the default is, "Did you mean"…this could be changed to "This might help" or, "This is what I could find".
An option is also available for none of the above and the maximum number of suggestions can be limited. The scope and size of the dialog will determine what this number might be.
This also provides a central point where disambiguation can be switched off; this of this as a global toggle switch to enable or disable thi...
published: 11 Feb 2020
0:23
Gorgeous Laminar Water Features
Beautiful Hobert Pool with awesome laminars water features!!
Beautiful Hobert Pool with awesome laminars water features!!
https://wn.com/Gorgeous_Laminar_Water_Features
Beautiful Hobert Pool with awesome laminars water features!!
- published: 13 Aug 2015
- views: 207
9:18
Laminar Flow DISAMBIGUATION
Captain Disillusion gets his hands wet with some experiments, and lets you watch.
Please consider supporting my videos on: http://www.patreon.com/CaptainDisill...
Captain Disillusion gets his hands wet with some experiments, and lets you watch.
Please consider supporting my videos on: http://www.patreon.com/CaptainDisillusion
https://wn.com/Laminar_Flow_Disambiguation
Captain Disillusion gets his hands wet with some experiments, and lets you watch.
Please consider supporting my videos on: http://www.patreon.com/CaptainDisillusion
- published: 14 Feb 2019
- views: 9885559
3:03
Improving Polyphone Disambiguation for Mandarin Chinese by Combining Mix-pooling Strategy and Wi...
Title: Improving Polyphone Disambiguation for Mandarin Chinese by Combining Mix-pooling Strategy and Window-based Attention - (3 minutes introduction)
Authors:...
Title: Improving Polyphone Disambiguation for Mandarin Chinese by Combining Mix-pooling Strategy and Window-based Attention - (3 minutes introduction)
Authors: Junjie Li (Ping An Technology, China), Zhiyu Zhang (National Tsing Hua University, Taiwan), Minchuan Chen (Ping An Technology, China), Jun Ma (Ping An Technology, China), Shaojun Wang (Ping An Technology, China), Jing Xiao (Ping An Technology, China)
Category: Speech Synthesis: Linguistic processing, paradigms and other topics
Abstract: In this paper, we propose a novel system based on word-level features and window-based attention for polyphone disambiguation, which is a fundamental task for Grapheme-to-phoneme (G2P) conversion of Mandarin Chinese. The framework aims to combine a pre-trained language model with explicit word-level information in order to get meaningful context extraction. Particularly, we employ a pre-trained bidirectional encoder from Transformers (BERT) model to extract character-level features, and an external Chinese word segmentation (CWS) tool is used to obtain the word units. We adopt a mixed pooling mechanism to convert character-level features into word-level features based on the segmentation results. A window-based attention module is utilized to incorporate contextual word-level features for the polyphonic characters. Experimental results show that our method achieves an accuracy of 99.06% on an open benchmark dataset for Mandarin Chinese polyphone disambiguation, which outperforms the baseline systems.
For more details and PDF version of the paper visit: https://www.isca-speech.org/archive/interspeech_2021/li21n_interspeech.html
d04s07t04trim
https://wn.com/Improving_Polyphone_Disambiguation_For_Mandarin_Chinese_By_Combining_Mix_Pooling_Strategy_And_Wi...
Title: Improving Polyphone Disambiguation for Mandarin Chinese by Combining Mix-pooling Strategy and Window-based Attention - (3 minutes introduction)
Authors: Junjie Li (Ping An Technology, China), Zhiyu Zhang (National Tsing Hua University, Taiwan), Minchuan Chen (Ping An Technology, China), Jun Ma (Ping An Technology, China), Shaojun Wang (Ping An Technology, China), Jing Xiao (Ping An Technology, China)
Category: Speech Synthesis: Linguistic processing, paradigms and other topics
Abstract: In this paper, we propose a novel system based on word-level features and window-based attention for polyphone disambiguation, which is a fundamental task for Grapheme-to-phoneme (G2P) conversion of Mandarin Chinese. The framework aims to combine a pre-trained language model with explicit word-level information in order to get meaningful context extraction. Particularly, we employ a pre-trained bidirectional encoder from Transformers (BERT) model to extract character-level features, and an external Chinese word segmentation (CWS) tool is used to obtain the word units. We adopt a mixed pooling mechanism to convert character-level features into word-level features based on the segmentation results. A window-based attention module is utilized to incorporate contextual word-level features for the polyphonic characters. Experimental results show that our method achieves an accuracy of 99.06% on an open benchmark dataset for Mandarin Chinese polyphone disambiguation, which outperforms the baseline systems.
For more details and PDF version of the paper visit: https://www.isca-speech.org/archive/interspeech_2021/li21n_interspeech.html
d04s07t04trim
- published: 03 Feb 2022
- views: 8
14:55
Improving Polyphone Disambiguation for Mandarin Chinese by Combining Mix-pooling Strategy and Wi...
Title: Improving Polyphone Disambiguation for Mandarin Chinese by Combining Mix-pooling Strategy and Window-based Attention - (longer introduction)
Authors: Ju...
Title: Improving Polyphone Disambiguation for Mandarin Chinese by Combining Mix-pooling Strategy and Window-based Attention - (longer introduction)
Authors: Junjie Li (Ping An Technology, China), Zhiyu Zhang (National Tsing Hua University, Taiwan), Minchuan Chen (Ping An Technology, China), Jun Ma (Ping An Technology, China), Shaojun Wang (Ping An Technology, China), Jing Xiao (Ping An Technology, China)
Category: Speech Synthesis: Linguistic processing, paradigms and other topics
Abstract: In this paper, we propose a novel system based on word-level features and window-based attention for polyphone disambiguation, which is a fundamental task for Grapheme-to-phoneme (G2P) conversion of Mandarin Chinese. The framework aims to combine a pre-trained language model with explicit word-level information in order to get meaningful context extraction. Particularly, we employ a pre-trained bidirectional encoder from Transformers (BERT) model to extract character-level features, and an external Chinese word segmentation (CWS) tool is used to obtain the word units. We adopt a mixed pooling mechanism to convert character-level features into word-level features based on the segmentation results. A window-based attention module is utilized to incorporate contextual word-level features for the polyphonic characters. Experimental results show that our method achieves an accuracy of 99.06% on an open benchmark dataset for Mandarin Chinese polyphone disambiguation, which outperforms the baseline systems.
For more details and PDF version of the paper visit: https://www.isca-speech.org/archive/interspeech_2021/li21n_interspeech.html
d04s07t04lng
https://wn.com/Improving_Polyphone_Disambiguation_For_Mandarin_Chinese_By_Combining_Mix_Pooling_Strategy_And_Wi...
Title: Improving Polyphone Disambiguation for Mandarin Chinese by Combining Mix-pooling Strategy and Window-based Attention - (longer introduction)
Authors: Junjie Li (Ping An Technology, China), Zhiyu Zhang (National Tsing Hua University, Taiwan), Minchuan Chen (Ping An Technology, China), Jun Ma (Ping An Technology, China), Shaojun Wang (Ping An Technology, China), Jing Xiao (Ping An Technology, China)
Category: Speech Synthesis: Linguistic processing, paradigms and other topics
Abstract: In this paper, we propose a novel system based on word-level features and window-based attention for polyphone disambiguation, which is a fundamental task for Grapheme-to-phoneme (G2P) conversion of Mandarin Chinese. The framework aims to combine a pre-trained language model with explicit word-level information in order to get meaningful context extraction. Particularly, we employ a pre-trained bidirectional encoder from Transformers (BERT) model to extract character-level features, and an external Chinese word segmentation (CWS) tool is used to obtain the word units. We adopt a mixed pooling mechanism to convert character-level features into word-level features based on the segmentation results. A window-based attention module is utilized to incorporate contextual word-level features for the polyphonic characters. Experimental results show that our method achieves an accuracy of 99.06% on an open benchmark dataset for Mandarin Chinese polyphone disambiguation, which outperforms the baseline systems.
For more details and PDF version of the paper visit: https://www.isca-speech.org/archive/interspeech_2021/li21n_interspeech.html
d04s07t04lng
- published: 03 Feb 2022
- views: 10
35:11
093 Keyword Disambiguation Using Transformers and Clustering to Build Cleaner Knowledge - NODES2022
Natural language processing is an indispensable toolkit to build knowledge graphs from unstructured data. However, it comes with a price. Keywords and entities ...
Natural language processing is an indispensable toolkit to build knowledge graphs from unstructured data. However, it comes with a price. Keywords and entities in unstructured texts are ambiguous - the same concept can be expressed by many different linguistic variations. The resulting knowledge graph would thus be polluted with many nodes representing the same entity without any order. In this session, we show how the semantic similarity based on transformer embeddings and agglomerative clustering can help in the domain of academic disciplines and research fields and how Neo4j improves the browsing experience of this knowledge graph.
Speakers: Federica Ventruto, Alessia Melania Lonoce
Format: Full Session 30-45 min
Level: Advanced
Topics: #KnowledgeGraph, #MachineLearning, #Visualization, #General, #Advanced
Region: EMEA
Slides: https://dist.neo4j.com/nodes-20202-slides/093%20Keyword%20Disambiguation%20Using%20Transformers%20and%20Clustering%20to%20Build%20Cleaner%20Knowledge%20Graphs%20-%20NODES2022%20EMEA%20Advanced%206%20-%20Federica%20Ventruto%2C%20Alessia%20Melania%20Lonoce.pdf
Visit https://neo4j.com/nodes-2022 learn more at https://neo4j.com/developer/get-started and engage at https://community.neo4j.com
https://wn.com/093_Keyword_Disambiguation_Using_Transformers_And_Clustering_To_Build_Cleaner_Knowledge_Nodes2022
Natural language processing is an indispensable toolkit to build knowledge graphs from unstructured data. However, it comes with a price. Keywords and entities in unstructured texts are ambiguous - the same concept can be expressed by many different linguistic variations. The resulting knowledge graph would thus be polluted with many nodes representing the same entity without any order. In this session, we show how the semantic similarity based on transformer embeddings and agglomerative clustering can help in the domain of academic disciplines and research fields and how Neo4j improves the browsing experience of this knowledge graph.
Speakers: Federica Ventruto, Alessia Melania Lonoce
Format: Full Session 30-45 min
Level: Advanced
Topics: #KnowledgeGraph, #MachineLearning, #Visualization, #General, #Advanced
Region: EMEA
Slides: https://dist.neo4j.com/nodes-20202-slides/093%20Keyword%20Disambiguation%20Using%20Transformers%20and%20Clustering%20to%20Build%20Cleaner%20Knowledge%20Graphs%20-%20NODES2022%20EMEA%20Advanced%206%20-%20Federica%20Ventruto%2C%20Alessia%20Melania%20Lonoce.pdf
Visit https://neo4j.com/nodes-2022 learn more at https://neo4j.com/developer/get-started and engage at https://community.neo4j.com
- published: 30 Nov 2022
- views: 515
26:51
Disambiguation, In-Jokes, and Name Collisions: What You Need to Know When Naming a Python Project
Thursday Bram
https://2018.northbaypython.org/schedule/presentation/15/
This talk covers key issues Python programmers run into when naming new projects. We'l...
Thursday Bram
https://2018.northbaypython.org/schedule/presentation/15/
This talk covers key issues Python programmers run into when naming new projects. We'll go over the following:
* Commonly used naming schemas in the Python community
* Current and past project names (including those that many newcomers to Python struggle with)
* Techniques to avoid similar confusion in the future (covering both name selection and documentation)
We'll even talk about Monty Python and its long-term impact on the Python programming language.
A Python conference north of the Golden Gate
North Bay Python is a single-track conference with a carefully curated set of talks representing the diverse Python community and their different areas of interest.
If a topic is less to your interest, or you've met some people you really want to sit down and chat with, we'll have plenty of areas away from the main theatre to catch up and chat.
Our goal is to keep prices as low as possible. That means we won't be catering lunch. Instead, you can look forward to extra-long lunch breaks you can use to explore all of the great food options around the venue.
https://wn.com/Disambiguation,_In_Jokes,_And_Name_Collisions_What_You_Need_To_Know_When_Naming_A_Python_Project
Thursday Bram
https://2018.northbaypython.org/schedule/presentation/15/
This talk covers key issues Python programmers run into when naming new projects. We'll go over the following:
* Commonly used naming schemas in the Python community
* Current and past project names (including those that many newcomers to Python struggle with)
* Techniques to avoid similar confusion in the future (covering both name selection and documentation)
We'll even talk about Monty Python and its long-term impact on the Python programming language.
A Python conference north of the Golden Gate
North Bay Python is a single-track conference with a carefully curated set of talks representing the diverse Python community and their different areas of interest.
If a topic is less to your interest, or you've met some people you really want to sit down and chat with, we'll have plenty of areas away from the main theatre to catch up and chat.
Our goal is to keep prices as low as possible. That means we won't be catering lunch. Instead, you can look forward to extra-long lunch breaks you can use to explore all of the great food options around the venue.
- published: 16 Nov 2018
- views: 175
13:58
Top 10 Worst TV Shows of All Time
Boy, there sure is some terrible television in the world…. Join http://www.WatchMojo.com as we count down our picks for theTop 10 Worst TV Shows of All Time. Su...
Boy, there sure is some terrible television in the world…. Join http://www.WatchMojo.com as we count down our picks for theTop 10 Worst TV Shows of All Time. Subscribe►►http://www.youtube.com/subscription_center?add_user=watchmojo Facebook►►http://www.Facebook.com/WatchMojo. Twitter►►http://www.Twitter.com/WatchMojo Instagram►►http://instagram.com/watchmojo Suggestion Tool►►http://www.WatchMojo.com/suggest Channel Page►►http://www.youtube.com/watchmojo
For this list, we'll be scouring TV's lengthy history in search of the programs that are universally viewed as lacking in quality.
Special thanks to our users Liza Davydzenkava, SuperSaiyanKirby100, DonovanTPS, Jerome Magajes, Aeryk Marcellus Bacon, TylerKienzlen@gmail., sarahjessicaparkerth, mac121mr0, Brody Nicholas Eiffel Jay, jhwoe6, PenName102, Francis FNT, MineNotCraft, Johnny B. Goode, Freightliner66Produc, Aidansreviews, bruce wayne, raccoon, Red Alejandro andSuperMonkeyGyrados for submitting the idea on our Interactive Suggestion Tool at http://www.WatchMojo.com/suggest
Check out the voting page here,
http://watchmojo.com/suggest/Top+10+Bad+Shows+That+Should+Stay+Dead
Want a WatchMojo cup, mug, t-shirts, pen, sticker and even a water bottle? Get them all when you order your MojoBox gift set here:
http://watchmojo.com/store/
WatchMojo is a leading producer of reference online video content, covering the People, Places and Trends you care about.
We update DAILY with 4-5 Top 10 lists, Origins, Biographies, Versus clips on movies, video games, music, pop culture and more!
https://wn.com/Top_10_Worst_Tv_Shows_Of_All_Time
Boy, there sure is some terrible television in the world…. Join http://www.WatchMojo.com as we count down our picks for theTop 10 Worst TV Shows of All Time. Subscribe►►http://www.youtube.com/subscription_center?add_user=watchmojo Facebook►►http://www.Facebook.com/WatchMojo. Twitter►►http://www.Twitter.com/WatchMojo Instagram►►http://instagram.com/watchmojo Suggestion Tool►►http://www.WatchMojo.com/suggest Channel Page►►http://www.youtube.com/watchmojo
For this list, we'll be scouring TV's lengthy history in search of the programs that are universally viewed as lacking in quality.
Special thanks to our users Liza Davydzenkava, SuperSaiyanKirby100, DonovanTPS, Jerome Magajes, Aeryk Marcellus Bacon, TylerKienzlen@gmail., sarahjessicaparkerth, mac121mr0, Brody Nicholas Eiffel Jay, jhwoe6, PenName102, Francis FNT, MineNotCraft, Johnny B. Goode, Freightliner66Produc, Aidansreviews, bruce wayne, raccoon, Red Alejandro andSuperMonkeyGyrados for submitting the idea on our Interactive Suggestion Tool at http://www.WatchMojo.com/suggest
Check out the voting page here,
http://watchmojo.com/suggest/Top+10+Bad+Shows+That+Should+Stay+Dead
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WatchMojo is a leading producer of reference online video content, covering the People, Places and Trends you care about.
We update DAILY with 4-5 Top 10 lists, Origins, Biographies, Versus clips on movies, video games, music, pop culture and more!
- published: 19 Jan 2016
- views: 2635722
30:45
Deduplication and Author Disambiguation of Streaming Records via Supervised Models -Reza Karimi
"Here we present a general supervised framework for record deduplication and author-disambiguation via Spark. This work differentiates itself by - Application o...
"Here we present a general supervised framework for record deduplication and author-disambiguation via Spark. This work differentiates itself by - Application of Databricks and AWS makes this a scalable implementation. Compute resources are comparably lower than traditional legacy technology using big boxes 24/7. Scalability is crucial as Elsevier's Scopus data, the biggest scientific abstract repository, covers roughly 250 million authorships from 70 million abstracts covering a few hundred years. - We create a fingerprint for each content by deep learning and/or word2vec algorithms to expedite pairwise similarity calculation. These encoders substantially reduce compute time while maintaining semantic similarity (unlike traditional TFIDF or predefined taxonomies). We will briefly discuss how to optimize word2vec training with high parallelization. Moreover, we show how these encoders can be used to derive a standard representation for all our entities namely such as documents, authors, users, journals, etc. This standard representation can simplify the recommendation problem into a pairwise similarity search and hence it can offer a basic recommender for cross-product applications where we may not have a dedicate recommender engine designed. - Traditional author-disambiguation or record deduplication algorithms are batch-processing with small to no training data. However, we have roughly 25 million authorships that are manually curated or corrected upon user feedback. Hence, it is crucial to maintain historical profiles and hence we have developed a machine learning implementation to deal with data streams and process them in mini batches or one document at a time. We will discuss how to measure the accuracy of such a system, how to tune it and how to process the raw data of pairwise similarity function into final clusters. Lessons learned from this talk can help all sort of companies where they want to integrate their data or deduplicate their user/customer/product databases.
Session hashtag: #EUai2"
About: Databricks provides a unified data analytics platform, powered by Apache Spark™, that accelerates innovation by unifying data science, engineering and business.
Read more here: https://databricks.com/product/unified-data-analytics-platform
Connect with us:
Website: https://databricks.com
Facebook: https://www.facebook.com/databricksinc
Twitter: https://twitter.com/databricks
LinkedIn: https://www.linkedin.com/company/databricks
Instagram: https://www.instagram.com/databricksinc/ Databricks is proud to announce that Gartner has named us a Leader in both the 2021 Magic Quadrant for Cloud Database Management Systems and the 2021 Magic Quadrant for Data Science and Machine Learning Platforms. Download the reports here. https://databricks.com/databricks-named-leader-by-gartner
https://wn.com/Deduplication_And_Author_Disambiguation_Of_Streaming_Records_Via_Supervised_Models_Reza_Karimi
"Here we present a general supervised framework for record deduplication and author-disambiguation via Spark. This work differentiates itself by - Application of Databricks and AWS makes this a scalable implementation. Compute resources are comparably lower than traditional legacy technology using big boxes 24/7. Scalability is crucial as Elsevier's Scopus data, the biggest scientific abstract repository, covers roughly 250 million authorships from 70 million abstracts covering a few hundred years. - We create a fingerprint for each content by deep learning and/or word2vec algorithms to expedite pairwise similarity calculation. These encoders substantially reduce compute time while maintaining semantic similarity (unlike traditional TFIDF or predefined taxonomies). We will briefly discuss how to optimize word2vec training with high parallelization. Moreover, we show how these encoders can be used to derive a standard representation for all our entities namely such as documents, authors, users, journals, etc. This standard representation can simplify the recommendation problem into a pairwise similarity search and hence it can offer a basic recommender for cross-product applications where we may not have a dedicate recommender engine designed. - Traditional author-disambiguation or record deduplication algorithms are batch-processing with small to no training data. However, we have roughly 25 million authorships that are manually curated or corrected upon user feedback. Hence, it is crucial to maintain historical profiles and hence we have developed a machine learning implementation to deal with data streams and process them in mini batches or one document at a time. We will discuss how to measure the accuracy of such a system, how to tune it and how to process the raw data of pairwise similarity function into final clusters. Lessons learned from this talk can help all sort of companies where they want to integrate their data or deduplicate their user/customer/product databases.
Session hashtag: #EUai2"
About: Databricks provides a unified data analytics platform, powered by Apache Spark™, that accelerates innovation by unifying data science, engineering and business.
Read more here: https://databricks.com/product/unified-data-analytics-platform
Connect with us:
Website: https://databricks.com
Facebook: https://www.facebook.com/databricksinc
Twitter: https://twitter.com/databricks
LinkedIn: https://www.linkedin.com/company/databricks
Instagram: https://www.instagram.com/databricksinc/ Databricks is proud to announce that Gartner has named us a Leader in both the 2021 Magic Quadrant for Cloud Database Management Systems and the 2021 Magic Quadrant for Data Science and Machine Learning Platforms. Download the reports here. https://databricks.com/databricks-named-leader-by-gartner
- published: 30 Oct 2017
- views: 810
49:16
Mod-01 Lec-31 Wordnet; Metonymy and Word Sense Disambiguation
Natural Language Processing by Prof. Pushpak Bhattacharyya, Department of Computer science & Engineering,IIT Bombay.For more details on NPTEL visit http://nptel...
Natural Language Processing by Prof. Pushpak Bhattacharyya, Department of Computer science & Engineering,IIT Bombay.For more details on NPTEL visit http://nptel.iitm.ac.in
https://wn.com/Mod_01_Lec_31_Wordnet_Metonymy_And_Word_Sense_Disambiguation
Natural Language Processing by Prof. Pushpak Bhattacharyya, Department of Computer science & Engineering,IIT Bombay.For more details on NPTEL visit http://nptel.iitm.ac.in
- published: 03 Jul 2012
- views: 3088
5:19
Chatbot Disambiguation Demonstration
Your Chatbot Must Be Able To Disambiguate. Disambiguation Is Part & Parcel Of Human Conversations And Should Be Part Of Your Chatbot Experience.
IBM Watson ha...
Your Chatbot Must Be Able To Disambiguate. Disambiguation Is Part & Parcel Of Human Conversations And Should Be Part Of Your Chatbot Experience.
IBM Watson has a built-in feature which allows for the configuration of disambiguation. In this practical example you can toggle the feature on or off.
Apart from this you can set the message explaining the clarification, the default is, "Did you mean"…this could be changed to "This might help" or, "This is what I could find".
An option is also available for none of the above and the maximum number of suggestions can be limited. The scope and size of the dialog will determine what this number might be.
This also provides a central point where disambiguation can be switched off; this of this as a global toggle switch to enable or disable this feature.
Apart from this, each node can be added or removed individually as the structure of the application changes.
Here the name of the node becomes important as this is what will be displayed to the user.
Hence it is crucial that the name of the node is clear, presentable and
explains the function and intention of the node it names.
Here is a short tutorial on the functionality and setup.
https://wn.com/Chatbot_Disambiguation_Demonstration
Your Chatbot Must Be Able To Disambiguate. Disambiguation Is Part & Parcel Of Human Conversations And Should Be Part Of Your Chatbot Experience.
IBM Watson has a built-in feature which allows for the configuration of disambiguation. In this practical example you can toggle the feature on or off.
Apart from this you can set the message explaining the clarification, the default is, "Did you mean"…this could be changed to "This might help" or, "This is what I could find".
An option is also available for none of the above and the maximum number of suggestions can be limited. The scope and size of the dialog will determine what this number might be.
This also provides a central point where disambiguation can be switched off; this of this as a global toggle switch to enable or disable this feature.
Apart from this, each node can be added or removed individually as the structure of the application changes.
Here the name of the node becomes important as this is what will be displayed to the user.
Hence it is crucial that the name of the node is clear, presentable and
explains the function and intention of the node it names.
Here is a short tutorial on the functionality and setup.
- published: 11 Feb 2020
- views: 906