---
title: "Coreference in Spoken vs. Written Texts — Amoia 2012"
description: "The paper asks why automatic coreference resolution struggles with spoken language."
authors: ["Marilisa Amoia", "Kerstin Kunz", "Ekaterina Lapshinova-Koltunski"]
year: 2012
venue: "Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)"
source: "https://aclanthology.org/L12-1362/"
licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)"
licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/papers/amoia-2012-coreference-spoken-vs-written-texts/"
---
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# Coreference in Spoken vs. Written Texts: a Corpus-based Analysis

**Authors:** Marilisa Amoia, [Kerstin Kunz](https://md.research.elamur.ai/free/authors/kerstin-kunz/), [Ekaterina Lapshinova-Koltunski](https://md.research.elamur.ai/free/authors/ekaterina-lapshinova-koltunski/) · **Year:** 2012 · **Venue:** Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12) · **Licence:** [CC BY-NC-SA 3.0 (ACL Anthology, before 2016)](https://creativecommons.org/licenses/by-nc-sa/3.0/)

**Links:** [Publisher's page](https://aclanthology.org/L12-1362/) · [PDF](https://aclanthology.org/L12-1362.pdf) · [OpenAlex](https://openalex.org/W2251258739)

**Citations:** 10 citations (openalex-snapshot · 2026-10-14) · [6](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
The paper asks why automatic coreference resolution struggles with spoken language. The authors suggest that spoken and written genres organise reference differently, so resolvers built mainly for written input lose accuracy on speech. They compiled an English corpus of 22 texts (about 140,000 tokens): 11 popular-science articles (POPSCI) and 11 manually transcribed and normalised interviews taken from the Backbone Corpus (INTERVIEW). The corpus was annotated with the deterministic, sieve-based Stanford CoreNLP coreference system. The authors then compared the genres on precision, mention length, sentence distance within chains, chain size, syntactic parallelism, grammatical roles, mention types and pronoun morphology. CoreNLP performed noticeably worse on the interviews. The analysis links this drop to speaker-centred first-person pronouns that are split across speaker turns, vague or non-referential *it* and *they*, and repeated general nouns that get merged wrongly. Written texts instead show long, heavily embedded mentions. The authors conclude that coreference algorithms need genre-specific parameters: speaker-turn and pronoun-type information for speech, and embedding and lexical-semantic analysis for writing.

## Key points
- Corpus: POPSCI (11 texts) vs. INTERVIEW (11 interviews with American English speakers, from the Backbone Corpus), each text about 6,000 tokens. Spoken features such as fillers and corrections are marked with XML tags.
- Precision (MUC metric) was estimated by manually correcting 4 of the 22 CoreNLP-annotated texts: 64% for POPSCI vs. 51% for INTERVIEW. The authors report all genre differences as significant (Student's t-test, p \< 0.05).
- Averages for POPSCI vs. INTERVIEW:
  - tokens per sentence: 25.75 vs. 20.73
  - chain size: 3.60 vs. 4.45
  - mention token length (T-length): 3.42 vs. 2.58
- Pronoun morphology differs strongly between the genres:
  - 1st person singular: 26.9% of pronouns in interviews vs. 5.8% in popular science
  - 3rd person plural: 16.9% in interviews vs. 33.1% in popular science
- CoreNLP does not handle discourse-deictic reference to clauses or VPs, which adds to errors with vague *it* in speech.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/shelves/linguistics/)

Topics: [Coreference resolution](https://md.research.elamur.ai/free/topics/coreference-resolution/)

## Cited by

Cited by

6 papers of our library cite it (of the 140673 whose bibliographies we read); 1 of them have a page here, 5 not yet.

- [Variation in Coreference Strategies across Genres and Production Media](https://md.research.elamur.ai/free/papers/akta-2020-variation-coreference-strategies-across-genres/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{amoia2012coreference,
  title = {{Coreference in Spoken vs. Written Texts: a Corpus-based Analysis}},
  author = {Amoia, Marilisa and Kunz, Kerstin and Lapshinova-Koltunski, Ekaterina},
  booktitle = {Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)},
  year = {2012},
  doi = {10.63317/5gb36ny2kine},
  url = {https://doi.org/10.63317/5gb36ny2kine}
}
```

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## References

12 references, 5 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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