Expansion de requêtes à base de motifs et de Word Embeddings pour améliorer la recherche de microblogs

International audienceSocial microblogging services have an especially significant role in our society. Twitter is one of the most popular microblogging sites used by people to find relevant information (e.g., breaking news, popular trends, information about people of interest, etc). In this context, retrieving information from such data has recently gained growing attention and opening new challenges. However, the size of such data and queries is usually short and may impact the search result. Query Expansion (QE) has the main task in this issue. In fact, words can have different meanings where only one is used for a given context. In this paper, we propose a QE method by considering the meaning of the context. Thus, we use patterns and Word Embeddings to expand users' queries. We experiment and evaluate the proposed method on the TREC dataset. Results show the effectiveness of the proposed approach and signify the combination of patterns and word embedding for enhanced microblog retrieval.Les services sociaux de microblogging jouent un rôle important dans notre société. Twitter est l'une des plateformes de microblogging les plus populaires, utilisées par les internautes pour trouver des informations pertinentes (sujets d'actualité, tendances populaires, informations sur certains internautes, etc.). Dans ce contexte, la recherche d'information provenant de telles données a récemment gagné un intérêt majeur et ouvert de nouveaux défis. Cependant, la taille de ces données ainsi que des requêtes est généralement courte et peut avoir un impact sur le résultat de la recherche. Cette dernière peut être améliorée à l'aide de l'expansion de requêtes. En effet, les mots peuvent avoir plusieurs sens dont un seul est utilisé pour un contexte donné. Dans cet article, nous proposons une méthode d'expansion de requêtes prenant en compte le sens du contexte. Nous utilisons les motifs et les plongements de mots pour étendre les requêtes des utilisateurs. L'évaluation expérimentale de la méthode proposée est menée sur la collection TREC. Les résultats montrent l'efficacité de l'approche en combinant des motifs avec des plongements de mots pour améliorer significativement la recherche de microblog

Terms interrelationship query expansion to improve accuracy of Quran search

Quran retrieval system is becoming an instrument for users to search for needed information. The search engine is one of the most popular search engines that successfully implemented for searching relevant verses queries. However, a major challenge to the Quran search engine is word ambiguities, specifically lexical ambiguities. With the advent of query expansion techniques for Quran retrieval systems, the performance of the Quran retrieval system has problem and issue in terms of retrieving users needed information. The results of the current semantic techniques still lack precision values without considering several semantic dictionaries. Therefore, this study proposes a stemmed terms interrelationship query expansion approach to improve Quran search results. More specifically, related terms were collected from different semantic dictionaries and then utilize to get roots of words using a stemming algorithm. To assess the performance of the stemmed terms interrelationship query expansion, experiments were conducted using eight Quran datasets from the Tanzil website. Overall, the results indicate that the stemmed terms interrelationship query expansion is superior to unstemmed terms interrelationship query expansion in Mean Average Precision with Yusuf Ali 68%, Sarawar 67%, Arberry 72%, Malay 65%, Hausa 62%, Urdu 62%, Modern Arabic 60% and Classical Arabic 59%

Hybrid Query Expansion Model for Text and Microblog Information Retrieval

International audienc