Cross-lingual relevance models

By Victor Lavrenko, Martin Choquette, W. Bruce Croft. 2002

247 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): “…top retrieved documents in response to the given query are relevant to the query and uses these documents in order to estimate better query models. effectiveness of PRF in various retrieval scenarios indicates that useful information can be captured from the top retrieved documents [19, 20, 44].”
Finding similar questions in large question and answer archives (2005, methodology): “[11] proposed a cross-lingual information retrieval model based on relevance models [12].”
Estimation of statistical translation models based on mutual information for ad hoc information retrieval (2010, methodology): “As a result, translation language models have been successfully applied to diﬀerent tasks such as cross-lingual information retrieval [12, 20, 39], question answering [40], sentence retrieval [19], and tracking information ﬂow [18].”
Inducing Crosslingual Distributed Representations of Words (2012, methodology): “In the last decade crosslingual methods have attracted a lot of attention both in NLP and closely related communities such as information retrieval (Lavrenko et al., 2002) and information management (Frederking et al., 2001).”
Cross-Language Text Classification Using Structural Correspondence Learning (2010, background): “allel corpora to induce correspondences between two languages (Lavrenko et al., 2002; Olsson et al., 2005).”
Automatic image annotation and retrieval using cross-media relevance models (2003, methodology): “We refer to this distribution as the relevance model of I (see [13, 14]).”
Monolingual and Cross-Lingual Information Retrieval Models Based on (Bilingual) Word Embeddings (2015, methodology): “As a standard practice [17, 44, 41], both English and Dutch queries have been extracted from the title and description ﬁelds of the CLEF themes for the years 2001-2003.”
Improving Spoken Dialogue Understanding Using Phonetic Mixture Models (2011, methodology): “NPCEditor employs a statistical language modeling approach called relevance models (Lavrenko, Choquette, and Croft 2002).”
A Discriminative Kernel-Based Approach to Rank Images from Text Queries (2008, methodology): “CMRM [22] is inspired by the Cross-Lingual Relevance Model [28], considering the caption of an image as the translation of its visual properties into words.”
Statistical Language Models for Information Retrieval: A Critical Review (2008, methodology): “Please apply to now Publishers, PO Box 179, 2600 AD Delft, The Netherlands, www.nowpublishers.com; e-mail: sales@nowpublishers.com Foundations and Trends R© in Information Retrieval Volume 2 Issue 3, 2008 Editorial Board”