Analysis of the Paragraph Vector Model for Information Retrieval

By Qingyao Ai, Liu Yang, Jiafeng Guo, W. Bruce Croft. 2016

84 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

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Citations in the corpus

Relevance-based Word Embedding (2017, background): “…models have been demonstrated to be effective in a number of IR tasks, including query expansion [11, 17, 40], query classification [23, 41], short text similarity [15], and document model estimation [2, 31]. aforementioned embedding models are typically trained basedontermproximityinalarge corpus.”
Learning over Knowledge-Base Embeddings for Recommendation (2018, methodology): “rize multiple relation matrix on a product knowledge graph. As shown in Equation (8), the final objective of the proposed model is controlled by the noisy distribution Pt. Similar to previous studies [1, 14, 19], we notice that the relationships with tail entities that have high frequency in the col”
Neural information retrieval: at the end of the early years (2017, methodology): “2 Document ranking Learn Learn to match [79, 89, 114, 152, 156, 157, 175, 176, 207] Learn to predict [1, 58] Learn to generate [116]”
Towards Conversational Search and Recommendation: System Ask, User Respond (2018, background): “Negative sampling was first proposed by Mikolov et al. [23] and has now been extensively used for machine learning and information retrieval [1, 2, 18].”
Convolutional Neural Networks for Soft-Matching N-Grams in Ad-hoc Search (2018, unknown): “e rst one is the exact match kernel μ = 1,σ = 10−3, or bin [1, 1].”
Learning to Spot and Refactor Inconsistent Method Names (2019, background): “Recent literature has provided evidence that paragraph vector outperforms other state-of-the-art techniques [35], [36] for text representations [32], [37], and can effectively capture semantic similarities among words and sentences [38]–[42].”