---
title: "Semantic Specialization of Distributional Word Vector Spaces"
description: "The authors introduce ATTRACT-REPEL, a post-processing method that adjusts existing vector… 77 references, cited by 66 papers here."
authors: ["Nikola Mrkšić", "Ivan Vulić", "Diarmuid Ó Séaghdha", "Ira Leviant", "Roi Reichart", "Milica Gašić", "Anna Korhonen", "Steve Young"]
year: 2017
venue: "Transactions of the Association for Computational Linguistics"
source: "https://aclanthology.org/Q17-1022/"
doi: "10.1162/tacl_a_00063"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/mrksic-2017-semantic-specialization-distributional-word-vector/"
---
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# Semantic Specialization of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints

**Authors:** [Nikola Mrkšić](https://md.research.elamur.ai/free/en/authors/nikola-mrksic/), [Ivan Vulić](https://md.research.elamur.ai/free/en/authors/ivan-vulic/), [Diarmuid Ó Séaghdha](https://md.research.elamur.ai/free/en/authors/diarmuid-o-seaghdha/), Ira Leviant, [Roi Reichart](https://md.research.elamur.ai/free/en/authors/roi-reichart/), [Milica Gašić](https://md.research.elamur.ai/free/en/authors/milica-gasic/), [Anna Korhonen](https://md.research.elamur.ai/free/en/authors/anna-korhonen/) and [Steve Young](https://md.research.elamur.ai/free/en/authors/steve-young/) · **Year:** 2017 · **Venue:** [Transactions of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/tacl/) ([Transactions of the Association for Computational Linguistics (TACL) 2017](https://md.research.elamur.ai/free/en/venues/tacl/2017/)) · **Publisher:** [The MIT Press](https://md.research.elamur.ai/free/en/publishers/the-mit-press/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/Q17-1022/) · [PDF](https://aclanthology.org/Q17-1022.pdf) · [DOI 10.1162/tacl\_a\_00063](https://doi.org/10.1162/tacl_a_00063) · [OpenAlex](https://openalex.org/W2620558438)

**Citations:** 170 citations (OpenAlex, 2026-10-14) · [66](https://md.research.elamur.ai/free/en/papers/mrksic-2017-semantic-specialization-distributional-word-vector/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
Word embeddings trained only on co-occurrence statistics tend to mix true similarity with mere relatedness. Lower-resource languages also often lack enough text to learn good vectors. The authors introduce ATTRACT-REPEL, a post-processing method that adjusts existing vector collections. It pulls synonym pairs together and pushes antonym pairs apart, using constraints taken from lexical resources. Because BabelNet supplies constraints that link words across languages, the method can merge several monolingual spaces into one shared cross-lingual space. This lets high-resource languages improve the vectors of weaker ones.

The specialized vectors achieve state-of-the-art scores on SimLex-999, SimVerb-3500 and the languages of Multilingual SimLex-999. They also bring clear gains on newly collected Hebrew and Croatian test sets. In a downstream test, the vectors improve the NBT-CNN dialogue state tracker on English and on new Italian and German WOZ 2.0 data. Mapping all languages to the English ontology makes it possible to train a single multilingual DST model. The work offers a lightweight route to transfer lexical knowledge into any language covered by cross-lingual lexicons.

## Key points
- Released resources (github.com/nmrksic/attract-repel):
  - ATTRACT-REPEL code
  - bilingual vector spaces pairing English with 51 other languages
  - Hebrew and Croatian similarity datasets
  - Italian and German DST datasets
- Cross-lingual constraints come from BabelNet, whose version 3.7 aligns WordNet with Wikipedia across 271 languages. Intrinsic evaluation covers six languages.
- On DST, the multilingual model with English ontology grounding scored EN 82.8, IT 77.1 and DE 57.7. Plain distributional vectors scored 77.6, 71.2 and 46.6.
- Large SimLex improvements did not always produce matching DST gains. For English DST, the bilingual vectors of Vulić and Korhonen (2016a) scored 81.6, beating the specialized spaces despite lower SimLex scores.
- German DST remained the weakest of the three languages, which the authors attribute to case marking and compounding. They highlight morphologically rich languages as future work.

*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: Nikola Mrkšić, Ivan Vulić, Diarmuid Ó Séaghdha, Ira Leviant, Roi Reichart, Milica Gašić, Anna Korhonen and Steve Young. 2017. Semantic Specialization of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints. Transactions of the Association for Computational Linguistics.

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

```bibtex
@article{mrksic2017semantic,
  title = {{Semantic Specialization of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints}},
  author = {Nikola Mrkšić, Ivan Vulić, Diarmuid Ó Séaghdha, Ira Leviant, Roi Reichart, Milica Gašić, Anna Korhonen and Steve Young},
  journal = {Transactions of the Association for Computational Linguistics},
  year = {2017},
  doi = {10.1162/tacl\_a\_00063},
  url = {https://doi.org/10.1162/tacl_a_00063}
}
```

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*Updated 24 October 2026*