# Low-cost Relevance Generation and Evaluation Metrics for Entity Resolution in AI

arXiv: https://arxiv.org/abs/2205.10298 · HTML: https://mlsanity.com/paper/2205.10298/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 17 references that are in the map.
- First posted on arXiv 20 May 2022.

## Builds on (most cited first, 4 of 4)

- [Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond](https://mlsanity.com/paper/1602.06023/) (2016)
- [Linguistic Input Features Improve Neural Machine Translation](https://mlsanity.com/paper/1606.02892/) (2016)
- [Exploring the Use of Text Classification in the Legal Domain](https://mlsanity.com/paper/1710.09306/) (2017)
- [A Practioner's Guide to Evaluating Entity Resolution Results](https://mlsanity.com/paper/1509.04238/) (2015)

## Abstract (arXiv)

Entity Resolution (ER) in voice assistants is a prime component during run time that resolves entities in users request to real world entities. ER involves two major functionalities 1. Relevance generation and 2. Ranking. In this paper we propose a low cost relevance generation framework by generating features using customer implicit and explicit feedback signals. The generated relevance datasets can serve as test sets to measure ER performance. We also introduce a set of metrics that accurately measures the performance of ER systems in various dimensions. They provide great interpretability to deep dive and identifying root cause of ER issues, whether the problem is in relevance generation or ranking.