Relevance-based Word Embedding
Table 5. Evaluating embedding algorithms via query classification. The superscripts 1/2 denote that the improvements over word2vec/GloVe are significant. The highest value in each column is marked in bold.
['Method', 'Precision', 'F1-measure']
Results and Discussion We compare our models against the word2vec and GloVe methods trained on the external collections that are described in the query expansion experiments. An interesting observation here is that contrary to the query expansion experiments, RPE performs better than RLM in query classification. The reason is that in query expansion the weight of each term is considered in order to generate the expanded query language model. Therefore, in addition to the order of terms, their weights should be also effective for improving the retrieval performance with query expansion. In query classification, we only assign a few categories to each query, and thus as long as the order of categories is correct, the similarity values between the queries and the categories do not matter.