blog.guillaume-pitel.fr

Mostly linguistically computational | Adventure in collaborative filtering, information retrieval, matrix factorization and other stuff

Adventure in collaborative filtering, information retrieval, matrix factorization and other stuff. Does SGNS (Word2Vec) encodes word frequency? I’ve been wondering for a while about the behaviour of SGNS vs SVD/PMI with regards to the difference of performance on analogy questions. (Have a look at Omer Levy’s TACL article. For a deep comparison of different methods). One of the thing that made me think that SGNS could encode frequency is when I look at the neighbours of rare words :. X the query count.

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Mostly linguistically computational | Adventure in collaborative filtering, information retrieval, matrix factorization and other stuff | blog.guillaume-pitel.fr Reviews

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Adventure in collaborative filtering, information retrieval, matrix factorization and other stuff. Does SGNS (Word2Vec) encodes word frequency? I’ve been wondering for a while about the behaviour of SGNS vs SVD/PMI with regards to the difference of performance on analogy questions. (Have a look at Omer Levy’s TACL article. For a deep comparison of different methods). One of the thing that made me think that SGNS could encode frequency is when I look at the neighbours of rare words :. X the query count.

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Mostly linguistically computational | Adventure in collaborative filtering, information retrieval, matrix factorization and other stuff | blog.guillaume-pitel.fr Reviews

https://blog.guillaume-pitel.fr

Adventure in collaborative filtering, information retrieval, matrix factorization and other stuff. Does SGNS (Word2Vec) encodes word frequency? I’ve been wondering for a while about the behaviour of SGNS vs SVD/PMI with regards to the difference of performance on analogy questions. (Have a look at Omer Levy’s TACL article. For a deep comparison of different methods). One of the thing that made me think that SGNS could encode frequency is when I look at the neighbours of rare words :. X the query count.

INTERNAL PAGES

blog.guillaume-pitel.fr

1

octobre 2014

http://blog.guillaume-pitel.fr/2014/10

Adventure in collaborative filtering, information retrieval, matrix factorization and other stuff. Distributed Protocol for Social Networks : never more needed than now. I still have a Firefox t-shirt labelled « Take back the web ». I think this goal hasn’t been more important than today, with the rise of so many social networks. I’ve already written my opinion on this here. And I’ve read this on Slashdot recently, that seems to back it a little. Posted in: social networking. EXenSa / Pivot / eXenGine.

2

Paris Datageeks talk : video available

http://blog.guillaume-pitel.fr/2014/05/paris-datageeks-talk-video-available

Adventure in collaborative filtering, information retrieval, matrix factorization and other stuff. Paris Datageeks talk : video available. I gave a talk at the Paris Datageeks meetup a month ago. Here is the video. Of my presentation of Apache Spark. Amazing results of NCISC on Netflix. Brand new version of Wikinsights.org →. Laisser un commentaire Annuler la réponse. Vous devez être connecté. Pour rédiger un commentaire. Count-Min-Log Sketch for improved Average Relative Error on low frequency events.

3

juin 2014

http://blog.guillaume-pitel.fr/2014/06

Adventure in collaborative filtering, information retrieval, matrix factorization and other stuff. Brand new version of Wikinsights.org. We’ve worked hard to make WikInsights. More appealing and fun to use. Now it has a graphical interface with a graph view of the neighbours of Wikipedia. Of course, the graph is dynamically generated and expose the most relevant neighbours along various dimensions, it’s not a view of the Wikipedia page graph. Have fun with it, and enjoy discovering new and relevant pages.

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Mostly linguistically computational | Adventure in collaborative filtering, information retrieval, matrix factorization and other stuff | Page 2

http://blog.guillaume-pitel.fr/page/2

Adventure in collaborative filtering, information retrieval, matrix factorization and other stuff. Paris Datageeks talk : video available. I gave a talk at the Paris Datageeks meetup a month ago. Here is the video. Of my presentation of Apache Spark. Amazing results of NCISC on Netflix. Last week, I’ve played a little bit with the Netflix prize dataset and NC-ISC. The results are amazingly better than the current state of the art. Over the current state of the art. This is a huge gain. Music, jobs,.

5

Distributed Protocol for Social Networks : never more needed than now

http://blog.guillaume-pitel.fr/2014/10/distributed-protocol-for-social-networks-never-more-needed-than-now

Adventure in collaborative filtering, information retrieval, matrix factorization and other stuff. Distributed Protocol for Social Networks : never more needed than now. I still have a Firefox t-shirt labelled « Take back the web ». I think this goal hasn’t been more important than today, with the rise of so many social networks. I’ve already written my opinion on this here. And I’ve read this on Slashdot recently, that seems to back it a little. Posted in: social networking. And pingbacks are open.

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Apache Spark : a practical feedback after implementing a data analysis workflow

https://www.infoq.com/fr/presentations/apache-spark-feedback-implementing-data-analysis-workflow

À propos de Nous. À propos de Vous. Adéquation à notre Mission. Restez à jour sur :. Diffuser les connaissances et l'innovation dans le développement logiciel d'entreprise. 28,262 juil. visiteurs uniques. A la une dans Développement. HTTP/2 pour le développeur Web. Nous, les développeurs, cherchons encore à comprendre les implications de HTTP/2 dans nos applications et nos infrastructures. Voyons ensemble les bases du protocole lui-même, suivi des questions impo. Tout voir dans Développement. Vous êtes i...

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Mostly linguistically computational | Adventure in collaborative filtering, information retrieval, matrix factorization and other stuff

Adventure in collaborative filtering, information retrieval, matrix factorization and other stuff. Does SGNS (Word2Vec) encodes word frequency? I’ve been wondering for a while about the behaviour of SGNS vs SVD/PMI with regards to the difference of performance on analogy questions. (Have a look at Omer Levy’s TACL article. For a deep comparison of different methods). One of the thing that made me think that SGNS could encode frequency is when I look at the neighbours of rare words :. X the query count.

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