---
title: "Learning to Rank Answers to Non-Factoid Questions from Web"
description: "The authors argue that community QA sites can serve as free supervision. 46 references, cited by 34 papers here."
authors: ["Mihai Surdeanu", "Massimiliano Ciaramita", "Hugo Zaragoza"]
year: 2011
venue: "Computational Linguistics"
source: "https://aclanthology.org/J11-2003/"
doi: "10.1162/coli_a_00051"
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/en/papers/surdeanu-2011-learning-rank-answers-non-factoid/"
---
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# Learning to Rank Answers to Non-Factoid Questions from Web Collections

**Authors:** [Mihai Surdeanu](https://md.research.elamur.ai/free/en/authors/mihai-surdeanu/), [Massimiliano Ciaramita](https://md.research.elamur.ai/free/en/authors/massimiliano-ciaramita/) and [Hugo Zaragoza](https://md.research.elamur.ai/free/en/authors/hugo-zaragoza/) · **Year:** 2011 · **Venue:** [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/) ([Computational Linguistics 2011](https://md.research.elamur.ai/free/en/venues/cl/2011/)) · **Publisher:** [The MIT Press](https://md.research.elamur.ai/free/en/publishers/the-mit-press/) · **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/J11-2003/) · [PDF](https://aclanthology.org/J11-2003.pdf) · [DOI 10.1162/coli\_a\_00051](https://doi.org/10.1162/coli_a_00051) · [OpenAlex](https://openalex.org/W2155712036)

**Citations:** 159 citations (OpenAlex, 2026-10-14) · [34](https://md.research.elamur.ai/free/en/papers/surdeanu-2011-learning-rank-answers-non-factoid/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Most question answering research targets factoid questions with short entity-like answers. This paper instead tackles ranking answers to non-factoid "How to" (manner) questions, where annotated training data is scarce. The authors argue that community QA sites can serve as free supervision. Yahoo! Answers threads come with user-chosen best answers, which they treat as positives and all other answers as negatives. Their pipeline first retrieves candidates with a standard IR model. A re-ranker called FMIX then scores them as a linear combination of four feature groups:
- similarity,
- question-to-answer translation,
- content frequency/density,
- correlation with external collections.

These features are computed over several text views, from bags of words and n-grams to syntactic dependencies and semantic role dependencies, with WordNet-based semantic tags used as back-off. Combining the features raises MRR and Precision@1 by 14–21% relative to the baseline. This is strong large-scale evidence that rich linguistic analysis can improve retrieval on noisy web data, without any manual annotation.

## Key points
- The evaluation covers over 25,000 "How to" questions from Yahoo! Answers, each with up to 100 candidate answers. All text was processed with unadapted, off-the-shelf NL tools.
- Translation-style features linking question structure to answer structure contribute the most. Still, the best results require combining all feature families.
- Semantic role dependencies and WordNet-sense labels give a statistically significant gain over bag-of-words and n-gram representations.
- Treating syntactic and semantic structures as simple "bags" of dependencies performs at least as well as heavier models such as tree kernels.
- Suggested future work includes graded relevance from non-best but upvoted answers and adapting the ranker to other domains (news, blogs).

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

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- [Yahoo](https://md.research.elamur.ai/free/en/companies/yahoo/) — Company, Sunnyvale, United States · 8 works · 11,973 citations

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## Cite

Cite as: Mihai Surdeanu, Massimiliano Ciaramita and Hugo Zaragoza. 2011. Learning to Rank Answers to Non-Factoid Questions from Web Collections. Computational Linguistics.

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

```bibtex
@article{surdeanu2011learning,
  title = {{Learning to Rank Answers to Non-Factoid Questions from Web Collections}},
  author = {Mihai Surdeanu, Massimiliano Ciaramita and Hugo Zaragoza},
  journal = {Computational Linguistics},
  year = {2011},
  doi = {10.1162/coli\_a\_00051},
  url = {https://doi.org/10.1162/coli_a_00051}
}
```

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*Updated 31 October 2026*