---
title: "Bootstrapping Distributional Feature Vector Quality (2009)"
description: "A conventional measure first builds a rough similarity space, and then features a word shares with its… 54 references, cited by 29 papers here."
authors: ["Maayan Zhitomirsky-Geffet", "Ido Dagan"]
year: 2009
venue: "Computational Linguistics"
source: "https://aclanthology.org/J09-3004/"
doi: "10.1162/coli.08-032-r1-06-96"
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/geffet-2009-bootstrapping-distributional-feature-vector-quality/"
---
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# Bootstrapping Distributional Feature Vector Quality

**Authors:** [Maayan Zhitomirsky-Geffet](https://md.research.elamur.ai/free/en/authors/maayan-zhitomirsky-geffet/) and [Ido Dagan](https://md.research.elamur.ai/free/en/authors/ido-dagan/) · **Year:** 2009 · **Venue:** [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/) ([Computational Linguistics 2009](https://md.research.elamur.ai/free/en/venues/cl/2009/)) · **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/J09-3004/) · [PDF](https://aclanthology.org/J09-3004.pdf) · [DOI 10.1162/coli.08-032-r1-06-96](https://doi.org/10.1162/coli.08-032-r1-06-96) · [OpenAlex](https://openalex.org/W2164359605)

**Citations:** 68 citations (OpenAlex, 2026-10-14) · [29](https://md.research.elamur.ai/free/en/papers/geffet-2009-bootstrapping-distributional-feature-vector-quality/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Distributional similarity methods often group words that are only loosely related. For example, distant co-hyponyms such as *party* and *company* come out close to *country*. This makes such methods a weak source of substitutable lexical entailment for QA, IR and textual entailment systems. The authors trace much of this looseness to poorly weighted feature vectors: common weightings such as PMI fail to rank the most meaning-bearing contexts highest. Their fix is a bootstrapping reweighting step. A conventional measure first builds a rough similarity space, and then features a word shares with its nearest neighbours are boosted, since shared contexts are assumed to be the most characteristic. Features are syntactic dependency triples produced by the Minipar parser. Evaluation uses two settings: human lexical-entailment judgments and a pseudo-word sense disambiguation task. A new diagnostic metric supports the analysis. Bootstrapping helps across several similarity measures and weighting functions and allows strong feature pruning. It therefore offers a measure-independent way to obtain cleaner distributional vectors.

## Key points
- Features are triples of context word, Minipar dependency relation, and head/modifier role. Bootstrapping was applied over three similarity measures (LIN, WJ, COS) and two weighting functions, with consistent gains.
- The gold standard for the refined substitutable lexical entailment relation was judged by three assessors, with high inter-annotator agreement.
- In pseudo-WSD, error rates fell from 0.157 to 0.133 (LIN), 0.150 to 0.132 (WJ) and 0.155 to 0.145 (COS). The LIN reduction exceeds 15%, compared with about 6% reported by Weeds and Weir (2005).
- The authors introduce the average common-feature rank ratio (acfr-ratio). They present it as the first metric that judges feature weighting directly, without plugging it into a similarity measure.
- Characteristic features concentrate at the top of bootstrapped vectors, enabling massive feature reduction. The method also underlies the feature inclusion scheme of Geffet and Dagan (2005).

*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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## Cite

Cite as: Maayan Zhitomirsky-Geffet and Ido Dagan. 2009. Bootstrapping Distributional Feature Vector Quality. Computational Linguistics.

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

```bibtex
@article{zhitomirskygeffet2009bootstrapping,
  title = {{Bootstrapping Distributional Feature Vector Quality}},
  author = {Maayan Zhitomirsky-Geffet and Ido Dagan},
  journal = {Computational Linguistics},
  year = {2009},
  doi = {10.1162/coli.08-032-r1-06-96},
  url = {https://doi.org/10.1162/coli.08-032-r1-06-96}
}
```

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*Updated 29 October 2026*