1.7IRNov 17, 2019
Quels corpus d'entraînement pour l'expansion de requêtes par plongement de mots : application à la recherche de microblogs culturelsPhilippe Mulhem, Lorraine Goeuriot, Massih-Reza Amini et al.
We describe here an experimental framework and the results obtained on microblogs retrieval. We study the contribution one popular approach, i.e., words embeddings, and investigate the impact of the training set on the learned embedding. We focus on query expansion for the retrieval of tweets on the CLEF CMC 2016 corpus. Our results show that using embeddings trained on a corpus in the same domain as the indexed documents did not necessarily lead to better retrieval results.
12.9IRJun 22, 2016
Toward Word Embedding for Personalized Information RetrievalNawal Ould-Amer, Philippe Mulhem, Mathias Gery
This paper presents preliminary works on using Word Embedding (word2vec) for query expansion in the context of Personalized Information Retrieval. Traditionally, word embeddings are learned on a general corpus, like Wikipedia. In this work we try to personalize the word embeddings learning, by achieving the learning on the user's profile. The word embeddings are then in the same context than the user interests. Our proposal is evaluated on the CLEF Social Book Search 2016 collection. The results obtained show that some efforts should be made in the way to apply Word Embedding in the context of Personalized Information Retrieval.