---
title: "Current Trends and Approaches in — Naser-Karajah 2022"
description: "This survey reviews ways to obtain synonyms automatically rather than by hand."
authors: ["Eman Naser-Karajah", "Nabil Arman", "Mustafa Jarrar"]
year: 2022
venue: "arXiv:2205.10412 · In Proceedings of the 11th Workshop on Semantic Evaluation (SemEval2021), 2021 International Conference on Information Technology (ICIT), 2021, pp. 428-434, IEEE"
source: "https://arxiv.org/abs/2205.10412"
doi: "10.1109/icit52682.2021.9491713"
arxiv: "2205.10412"
licence: "CC BY 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/naser-2022-karajah-current-trends-approaches-synonyms-extraction/"
---
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# Current Trends and Approaches in Synonyms Extraction: Potential Adaptation to Arabic

**Authors:** Eman Naser-Karajah, Nabil Arman, [Mustafa Jarrar](https://md.research.elamur.ai/free/en/authors/mustafa-jarrar/) · **Institutions:** [Birzeit University](https://md.research.elamur.ai/free/en/universities/birzeit-university/) · Palestine Polytechnic University · **Year:** 2022 · **Venue:** arXiv:2205.10412 · In Proceedings of the 11th Workshop on Semantic Evaluation (SemEval2021), 2021 International Conference on Information Technology (ICIT), 2021, pp. 428-434, IEEE · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://doi.org/10.1109/icit52682.2021.9491713) · [PDF](https://arxiv.org/pdf/2205.10412) · [DOI 10.1109/icit52682.2021.9491713](https://doi.org/10.1109/icit52682.2021.9491713) · [arXiv 2205.10412](https://arxiv.org/abs/2205.10412) · [OpenAlex](https://openalex.org/W3185435365)

**Citations:** 9 citations (OpenAlex, 2026-10-14) · [5](https://md.research.elamur.ai/free/en/papers/naser-2022-karajah-current-trends-approaches-synonyms-extraction/cited-by/) in our library, of the 140884 papers whose bibliographies we read

## Summary
This survey reviews ways to obtain synonyms automatically rather than by hand. Hand-built resources such as Princeton WordNet are slow and costly to produce, which is why most WordNets for other languages, Arabic included, remain small. The authors sort prior work into four families. The first infers synonyms from translation graphs built out of bilingual dictionaries. The second derives new translation pairs by pivoting through a third language. The third assembles WordNet synsets by exploring synonymy and translation graphs. The fourth mines similar words from corpora using word embeddings and, more recently, BERT-based lexical substitution. For each family the paper describes the method, the data and the evaluation used, and then compares the families. A recurring finding is that graph-based methods can be precise, but their output is only as good as the input dictionaries. Polysemy produces spurious links, and no standard benchmark exists, so most work is evaluated against existing WordNets or by manual checking. The survey closes by pointing to these techniques as ways to grow Arabic synonym resources.

## Key points
- **Arabic translation-graph method [6].** Synonym sets are found from cycles in an undirected Arabic–English translation graph, then merged by taking the union of sets that share translations. To test it, the authors flattened the Arabic WordNet (AWN) into a bilingual dictionary and rebuilt it. Agreement with the original, measured by cosine similarity, was 82%.
- **English–Italian thesaurus [8].** Missing synonyms were found using Cyclic and Quasi-Cyclic (CQC) paths, explored with depth-first search and scored so that shorter paths count more. It reached about 80% accuracy at the sixth level and was evaluated on the TOEFL dataset.
- **Translation-pair discovery over Apertium dictionaries [9].** Cycle-based, path-based and multi-way NMT methods were combined. Recall was very low, with precision between 25% and 75%.
- **Apertium RDF graph with 22 dictionaries [10].** Translations were scored by dense cycles, with exploration capped at level 7. After the English–Spanish link was deleted, the method recovered 72% of it. It also matched 73% of a converted Wiktionary English–French set.
- **Synset construction from multiple Wiktionaries [11].** Clustering, greedy expansion and supervised classification were applied to build Slovenian, Persian, German and Russian WordNets. Accuracy ranged from 20% to 88%, and exceeded 90% when strict thresholds were used.

## Interesting ideas
- Testing an induction method by flattening a trusted resource (here AWN) and checking how much the algorithm rebuilds gives a gold standard at no annotation cost. — For Mova (learn): we could remove part of our compiled verb classes or ontology links and check whether our induction rules regenerate them, which acts as a built-in positive control.
- A translation round trip that returns to the starting word is treated as evidence that every word on the loop shares a meaning. Overlapping candidate sets are then merged by union instead of being ranked. — For Mova (world): within the WordNet/Wikidata knowledge graph, closed equivalence loops could license merging entries into one concept, and each merge would stay explainable as the loop that justified it.
- Path scoring that prefers short paths, combined with a hard depth limit, keeps graph walks cheap and limits drift caused by polysemous hops. — For Mova (world): when inferring relations through the knowledge graph, we can cap hop count and down-weight long chains, and show the winning path as the explanation.
- The threshold trade-off is made explicit: a strict setting yields small, accurate synsets, while a loose setting yields larger ones that need a follow-up corpus filter. The threshold must be tuned on validation data kept apart from the test set. — For Mova (learn): silver-data agreement thresholds could be tuned the same way on a held-out split, with a stricter tier for training and a looser tier sent to filtering.
- Errors caused by polysemy can be reduced by requiring agreement through several pivot languages rather than a single one. — For Mova (back): when translating through the meta-world, a lexical choice should be accepted only if more than one independent route supports it, so that one ambiguous bridge word cannot decide it.

*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/en/shelves/linguistics/)

Topics: [Semitic languages](https://md.research.elamur.ai/free/en/topics/semitic-languages/)

## Cite

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

```bibtex
@misc{naserkarajah2022current,
  title = {{Current Trends and Approaches in Synonyms Extraction: Potential Adaptation to Arabic}},
  author = {Naser-Karajah, Eman and Arman, Nabil and Jarrar, Mustafa},
  year = {2022},
  doi = {10.1109/icit52682.2021.9491713},
  eprint = {2205.10412},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.1109/icit52682.2021.9491713}
}
```

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