Estimating Embedding Vectors for Queries

By Hamed Zamani, W. Bruce Croft. 2016

111 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Relevance-based Word Embedding (2017, methodology): “changed during the optimization process, we cannot simply omit the normalization term as is done in [41] for estimating query embedding vectors based on pre-trained word embedding vectors.”
Neural Ranking Models with Weak Supervision (2017, methodology): “Word embedding vectors have been applied to term re-weighting in IR models [32, 47], query expansion [10, 33, 43], query classication [25, 44], etc.”
To Clean or Not to Clean (2018, background): “These techniques are popular among researchers as baseline retrieval methods [37, 54, 73, 74, 78].”
EXS: Explainable Search Using Local Model Agnostic Interpretability (2018, background): “Recently, the IR community has also seen successful application of deep learning models in document representation [3, 9], query modeling [4, 19, 20] and design of ranking models [6, 10, 11, 16].”
Neural information retrieval: at the end of the early years (2017, methodology): “Zamani and Croft [212, 213] apply a sigmoid function on the cosine similarity scores in order to in-”
Pseudo-Relevance Feedback Based on Matrix Factorization (2016, background): “Using other information, such as term proximity [21, 24], term topics [38], term dependency [23], and semantic similarity [25, 40, 41] has been shown to be effective in PRF and query expansion.”
Explainable Product Search with a Dynamic Relation Embedding Model (2019, methodology): “Previous studies have proposed several methods to model search intents with queries in latent space [6, 57, 59, 66].”
Situational Context for Ranking in Personal Search (2017, methodology): “Word embedding has been also employed in other IR tasks, such as query classiﬁcation [49], document classiﬁcation [32], etc.”
Seeping Semantics: Linking Datasets Using Word Embeddings for Data Discovery (2018, background): “Of those, averaging word embeddings (AWE) is quite popular [13], [14], [15].”
Query Expansion Using Word Embeddings (2016, methodology): “Our RM-Cent method which integrates the expanded query with a relevance model is diﬀerent than that which uses an embedding of a relevance model to expand the query [18].”