Query Expansion Using Word Embeddings

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Relevance-based Word Embedding (2017, methodology): “Query expansion using word embedding has been also studied in [11, 17, 35].”
Task-Oriented Query Reformulation with Reinforcement Learning (2017, methodology): “[9], the top-N terms are selected based on the cosine similarity of their embeddings against the original query embedding.”
Riker: Mining Rich Keyword Representations for Interpretable Product Question Answering (2019, methodology): “Question expansion strategies in our work (especially QE1) are most related to recent word embedding based techniques such as [24].”
Query Expansion Techniques for Information Retrieval: a Survey (2017, unknown): “2016 [149] Correlation coefficient cq,s2 = 1 |q| ∑ s1∈q cs1,s2 = 1 |q| ∑ s1∈q ∑ dj ws1,j .”
Humpty Dumpty: Controlling Word Meanings via Corpus Poisoning (2020, background): “Furthermore, pre-trained embeddings can directly solve subtasks in information retrieval systems, such as expanding search queries to include related terms [21, 39, 61], predicting question-answer relatedness [14, 35], deriving the word’s kmeans cluster [54], and more.”
Large Language Models for Information Retrieval: A Survey (2023, unknown): “Mainly-used methods include relevance feedback [87–92], word-embedding based methods [93, 94] etc.”