# False Positive and Cross-relation Signals in Distant Supervision Data

arXiv: https://arxiv.org/abs/1711.05186 · HTML: https://mlsanity.com/paper/1711.05186/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2017.

## Built on by (most cited first, 1 of 1)

- [Balancing Quality and Variation: Spam Filtering Distorts Data Label Distributions](https://mlsanity.com/paper/2509.08217/) (2025)

## Abstract (arXiv)

Distant supervision (DS) is a well-established method for relation extraction from text, based on the assumption that when a knowledge-base contains a relation between a term pair, then sentences that contain that pair are likely to express the relation. In this paper, we use the results of a crowdsourcing relation extraction task to identify two problems with DS data quality: the widely varying degree of false positives across different relations, and the observed causal connection between relations that are not considered by the DS method. The crowdsourcing data aggregation is performed using ambiguity-aware CrowdTruth metrics, that are used to capture and interpret inter-annotator disagreement. We also present preliminary results of using the crowd to enhance DS training data for a relation classification model, without requiring the crowd to annotate the entire set.