# Quootstrap: Scalable Unsupervised Extraction of Quotation-Speaker Pairs from Large News Corpora via Bootstrapping

arXiv: https://arxiv.org/abs/1804.02525 · HTML: https://mlsanity.com/paper/1804.02525/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2018.

## Built on by (most cited first, 6 of 6)

- [SciClops: Detecting and Contextualizing Scientific Claims for Assisting Manual Fact-Checking](https://mlsanity.com/paper/2110.13090/) (2021)
- [Quote Erat Demonstrandum: A Web Interface for Exploring the Quotebank Corpus](https://mlsanity.com/paper/2207.03592/) (2022)
- [Identifying Informational Sources in News Articles](https://mlsanity.com/paper/2305.14904/) (2023)
- [Strong Heuristics for Named Entity Linking](https://mlsanity.com/paper/2207.02824/) (2022)
- [Detecting Contextomized Quotes in News Headlines by Contrastive Learning](https://mlsanity.com/paper/2302.04465/) (2023)
- [Cross-Lingual Speaker Identification Using Distant Supervision](https://mlsanity.com/paper/2210.05780/) (2022)

## Abstract (arXiv)

We propose Quootstrap, a method for extracting quotations, as well as the names of the speakers who uttered them, from large news corpora. Whereas prior work has addressed this problem primarily with supervised machine learning, our approach follows a fully unsupervised bootstrapping paradigm. It leverages the redundancy present in large news corpora, more precisely, the fact that the same quotation often appears across multiple news articles in slightly different contexts. Starting from a few seed patterns, such as ["Q", said S.], our method extracts a set of quotation-speaker pairs (Q, S), which are in turn used for discovering new patterns expressing the same quotations; the process is then repeated with the larger pattern set. Our algorithm is highly scalable, which we demonstrate by running it on the large ICWSM 2011 Spinn3r corpus. Validating our results against a crowdsourced ground truth, we obtain 90% precision at 40% recall using a single seed pattern, with significantly higher recall values for more frequently reported (and thus likely more interesting) quotations. Finally, we showcase the usefulness of our algorithm's output for computational social science by analyzing the sentiment expressed in our extracted quotations.