Latent entity space: a novel retrieval approach for entity-bearing queries

Liu, Xitong; Fang, Hui

doi:10.1007/s10791-015-9267-x

Cited by 60 publications

(35 citation statements)

References 37 publications

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“…One way to improve the text understanding is to use more meaningful language units than words in text representations. ese approaches include the rst generation of search engines that were based on controlled vocabularies [5] and also the recent entity-oriented search systems which utilize knowledge graphs in search [7,15,20,24,29]. In these approaches, texts are o en represented by entities, which introduce information from knowledge graphs to search systems.…”

Section: Related Workmentioning

confidence: 99%

Towards Better Text Understanding and Retrieval through Kernel Entity Salience Modeling

Xiong

Liu

Callan

et al. 2018

The 41st International ACM SIGIR Conference on Research &Amp; Development in Information Retrieval

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is paper presents a Kernel Entity Salience Model (KESM) that improves text understanding and retrieval by be er estimating entity salience (importance) in documents. KESM represents entities by knowledge enriched distributed representations, models the interactions between entities and words by kernels, and combines the kernel scores to estimate entity salience. e whole model is learned end-to-end using entity salience labels. e salience model also improves ad hoc search accuracy, providing e ective ranking features by modeling the salience of query entities in candidate documents. Our experiments on two entity salience corpora and two TREC ad hoc search datasets demonstrate the e ectiveness of KESM over frequency-based and feature-based methods. We also provide examples showing how KESM conveys its text understanding ability learned from entity salience to search.

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Section: Related Workmentioning

confidence: 99%

Towards Better Text Understanding and Retrieval through Kernel Entity Salience Modeling

Xiong

Liu

Callan

et al. 2018

The 41st International ACM SIGIR Conference on Research &Amp; Development in Information Retrieval

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“…Another way to utilize knowledge graphs in information retrieval is to build the additional connections from query to documents through related entities. Latent Entity Space (LES) builds an unsupervised model using latent entities' descriptions (Liu and Fang, 2015). EsdRank uses related entities as a latent space, and performs learning to rank with various information retrieval features (Xiong and Callan, 2015).…”

Section: Related Workmentioning

confidence: 99%

Entity-Duet Neural Ranking: Understanding the Role of Knowledge Graph Semantics in Neural Information Retrieval

Liu

Xiong

Sun

et al. 2018

Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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This paper presents the Entity-Duet Neural Ranking Model (EDRM), which introduces knowledge graphs to neural search systems. EDRM represents queries and documents by their words and entity annotations. The semantics from knowledge graphs are integrated in the distributed representations of their entities, while the ranking is conducted by interaction-based neural ranking networks. The two components are learned end-to-end, making EDRM a natural combination of entityoriented search and neural information retrieval. Our experiments on a commercial search log demonstrate the effectiveness of EDRM. Our analyses reveal that knowledge graph semantics significantly improve the generalization ability of neural ranking models.

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“…Combinations of knowledge base retrieval and entity linking methods have been studied for web search queries both for entity ranking tasks [38,42] as well as document ranking tasks [12,29,48]. Our work builds on these ideas for the purpose of creating event collections.…”

Section: Current Limitationsmentioning

confidence: 99%

“…Entities from W are collected following all Wikipedia outlinks. is approach is inspired by work on entity query feature expansion [12,29].…”

Section: System's Overviewmentioning

confidence: 99%

Building Entity-Centric Event Collections

Nanni¹,

Ponzetto²,

Dietz³

2017

2017 ACM/IEEE Joint Conference on Digital Libraries (JCDL)

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Web archives preserve an unprecedented abundance of materials regarding major events and transformations in our society. In this paper, we present an approach for building event-centric sub-collections from such large archives, which includes not only the core documents related to the event itself but, even more importantly, documents describing related aspects (e.g., premises and consequences). is is achieved by 1) identifying relevant concepts and entities from a knowledge base, and 2) detecting their mentions in documents, which are interpreted as indicators for relevance. We

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Latent entity space: a novel retrieval approach for entity-bearing queries

Cited by 60 publications

References 37 publications

Towards Better Text Understanding and Retrieval through Kernel Entity Salience Modeling

Towards Better Text Understanding and Retrieval through Kernel Entity Salience Modeling

Entity-Duet Neural Ranking: Understanding the Role of Knowledge Graph Semantics in Neural Information Retrieval

Building Entity-Centric Event Collections

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