Document Retrieval Using Entity-Based Language Models

By Hadas Raviv, Oren Kurland, David Carmel. 2016

100 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Chenyan Xiong
2. Jamie Callan
3. Zhiyuan Liu
4. Maosong Sun
5. Zhenghao Liu
6. Tie-Yan Liu
7. Zhuyun Dai
8. Jimmy J. Lin

In Information Systems

1. Jamie Callan
2. Chenyan Xiong
3. Tie-Yan Liu
4. Zhenghao Liu
5. Zhiyuan Liu
6. Maosong Sun
7. Zhuyun Dai
8. Edgar Meij

In Natural Language Processing

1. Chenyan Xiong
2. Zhiyuan Liu
3. Maosong Sun
4. Jamie Callan
5. Zhenghao Liu
6. Tie-Yan Liu
7. Laura Dietz
8. Aixin Sun

Citations in the corpus

• Word-Entity Duet Representations for Document Ranking (2017, methodology): “Query Entities to Document Entities ( Qe-De ): There are two ways the interactions in the entity space can be useful. exact match signal addresses the vocabulary mismatch of surface forms [17, 21].”
• Multi-step Entity-centric Information Retrieval for Multi-Hop Question Answering (2019, neutral): “Several IR approaches (Xiong et al., 2016; Raviv et al., 2016) use an off-the-shelf entity linker.”
• Utilizing Knowledge Graphs in Text-centric Information Retrieval (2017, background): “Such approaches match information in KGs with information in text and obtain state-of-the-art performance for ad hoc document retrieval [3, 9, 12, 17].”
• Explicit Semantic Ranking for Academic Search via Knowledge Graph Embedding (2017, background): “This section describes the ranking benchmark dataset of S2 and the experimental settings.”
• A Survey on Application of Knowledge Graph (2020, background): “In the research work [32], bag-of-entities vectors are generated from entity annotations of queries and documents to represent them.”
• A Survey on Deep Learning for Named Entity Recognition (2018, methodology): “To incorporate named entities in search, entity-based language models [34], which consider individual terms as well as term sequences that have been annotated as entities (both in documents and in queries), have been proposed by Raviv et al. [35].”
• Entity-Duet Neural Ranking: Understanding the Role of Knowledge Graph Semantics in Neural Information Retrieval (2018, background): “The recent progresses in entity-oriented search include better text representations with entity annotations (Xiong et al., 2016; Raviv et al., 2016), richer ranking features (Dalton et al.”