2 research outputs found
Enhancing Translation Language Models with Word Embedding for Information Retrieval
In this paper, we explore the usage of Word Embedding semantic resources for
Information Retrieval (IR) task. This embedding, produced by a shallow neural
network, have been shown to catch semantic similarities between words (Mikolov
et al., 2013). Hence, our goal is to enhance IR Language Models by addressing
the term mismatch problem. To do so, we applied the model presented in the
paper Integrating and Evaluating Neural Word Embedding in Information Retrieval
by Zuccon et al. (2015) that proposes to estimate the translation probability
of a Translation Language Model using the cosine similarity between Word
Embedding. The results we obtained so far did not show a statistically
significant improvement compared to classical Language Model
Enhancing Translation Language Models with Word Embedding for Information Retrieval
International audienceIn this paper, we explore the usage of Word Embedding semantic resources for Information Retrieval (IR) task. This embedding, produced by a shallow neural network, have been shown to catch semantic similarities between words (Mikolov et al., 2013). Hence, our goal is to enhance IR Language Models by addressing the term mismatch problem. To do so, we applied the model presented in the paper Integrating and Evaluating Neural Word Embedding in Information Retrieval by Zuccon et al. (2015) that proposes to estimate the translation probability of a Translation Language Model using the cosine similarity between Word Embedding. The results we obtained so far did not show a statistically significant improvement compared to classical Language Model