Vertical intent prediction approach based on Doc2vec and convolutional neural networks for improving vertical selection in aggregated search

Vertical selection is the task of selecting the most relevant verticals to a given query in order to improve the diversity and quality of web search results. This task requires not only predicting relevant verticals but also these verticals must be those the user expects to be relevant for his particular information need. Most existing works focused on using traditional machine learning techniques to combine multiple types of features for selecting several relevant verticals. Although these techniques are very efficient, handling vertical selection with high accuracy is still a challenging research task. In this paper, we propose an approach for improving vertical selection in order to satisfy the user vertical intent and reduce user’s browsing time and efforts. First, it generates query embeddings vectors using the doc2vec algorithm that preserves syntactic and semantic information within each query. Secondly, this vector will be used as input to a convolutional neural network model for increasing the representation of the query with multiple levels of abstraction including rich semantic information and then creating a global summarization of the query features. We demonstrate the effectiveness of our approach through comprehensive experimentation using various datasets. Our experimental findings show that our system achieves significant accuracy. Further, it realizes accurate predictions on new unseen data

Pré-processamento de tweets visando melhorar resultados de NERD

TCC(graduação) - Universidade Federal de Santa Catarina. Centro Tecnológico. Sistemas de Informação.O enriquecimento semântico das postagens em mídias sociais pode trazer diversos benefícios em aplicações. Todavia, as técnicas e ferramentas de extração de informação atualmente presentes na literatura não trabalham adequadamente com dados provenientes dessas fontes, os quais estão sujeitos a ruídos diversos. Este trabalho propõe um método para filtragem de tweets baseado em normalização léxica visando diminuir ruídos e obter melhores resultados nas tarefas de reconhecimento e desambiguação de entidades nomeadas (NERD). Para realizar tal proposta, este trabalho apresenta uma revisão do estado-da-arte sobre o reconhecimento e desambiguação de entidades nomeadas com foco em mídias sociais, bem como revisa propostas para uma etapa preliminar de filtragem de tweets. De modo a verificar a qualidade do método proposto, foram realizados experimentos com a ferramenta FOX e observou-se um aumento de 5% no número de entidades nomeadas reconhecidas após a normalização léxica dos tweets.The semantic enrichment of posts in social media can bring several benefits in applications. However, the information extraction techniques and tools currently available in the literature are not prepared to work with data from these sources, which are very affected by noises. This work proposes a method for filtering tweets based on lexical normalization to reduce noise and obtain better results in the recognition and naming entity disambiguation (NERD) tasks. To accomplish this, this paper presents a state-of-the-art review of the recognition and disambiguation of social media-focused entities, as well as reviews proposals for a preliminary tweeting filtering step. In order to verify the quality of the proposed method, experiments were performed with the FOX tool and a 5% increase in the number of named entities recognized after the lexical normalization of the tweets was observed