Pith. sign in

REVIEW 3 cited by

Learning Dense Representations for Entity Retrieval

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1909.10506 v1 pith:OEIYEBIP submitted 2019-09-23 cs.CL cs.IRcs.LG

Daniel Gillick , Sayali Kulkarni , Larry Lansing , Alessandro Presta , Jason Baldridge , Eugene Ie , Diego Garcia-Olano This is my paper · ORCID
classification cs.CLcs.IRcs.LG
keywords entityaliasdatasetdensedualencoderentitiesmodel
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

We show that it is feasible to perform entity linking by training a dual encoder (two-tower) model that encodes mentions and entities in the same dense vector space, where candidate entities are retrieved by approximate nearest neighbor search. Unlike prior work, this setup does not rely on an alias table followed by a re-ranker, and is thus the first fully learned entity retrieval model. We show that our dual encoder, trained using only anchor-text links in Wikipedia, outperforms discrete alias table and BM25 baselines, and is competitive with the best comparable results on the standard TACKBP-2010 dataset. In addition, it can retrieve candidates extremely fast, and generalizes well to a new dataset derived from Wikinews. On the modeling side, we demonstrate the dramatic value of an unsupervised negative mining algorithm for this task.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Real-Time Hard Negative Sampling via LLM-based Clustering for Large-Scale Two-Tower Retrieval

    cs.IR 2026-07 unverdicted novelty 6.0 of 10

    Cluster-based real-time out-of-batch negatives drawn from LLM media embeddings outperform industry-standard negative sampling for two-tower retrieval and cut popularity bias.

  2. Locality-Sensitive Hashing for Efficient Hard Negative Sampling in Contrastive Learning

    cs.CV 2025-05 conditional novelty 6.0 of 10

    Binary LSH codes found by Hamming distance provide hard negatives for supervised contrastive learning at a fraction of the compute cost of exact pre-epoch sampling, with comparable or better accuracy on six benchmarks.

  3. A Hybrid Cross-Stage Coordination Pre-ranking Model for Online Recommendation Systems

    cs.IR 2025-02 conditional novelty 6.0 of 10

    A hybrid pre-ranking model that combines ranking-sequence consistency training with margin-based contrastive learning on unexposed items improves recommendation accuracy, especially for long-tail items.

Pith tools