[Mova](https://md.mova.elamur.ai/free/en/) · [this page in HTML](https://mova.elamur.ai/free/en/docs/papers/boguslavsky-2000-creating-universal-networking-language-module/) [home](https://md.mova.elamur.ai/free/en/) / [docs](https://md.mova.elamur.ai/free/en/docs/) / [papers](https://md.mova.elamur.ai/free/en/docs/papers/) / [Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/mtrans/) / Creating a Universal Networking Language Module within an Advanced NLP System # Creating a Universal Networking Language Module within an Advanced NLP System **Authors:** Igor Boguslavsky, Nadezhda Frid, Leonid Iomdin, Leonid Kreidlin, Irina Sagalova, Victor Sizov · **Year:** 2000 · **Venue:** COLING 2000, Volume 1: The 18th International Conference on Computational Linguistics **Link:** [aclanthology.org/C00-1013](https://aclanthology.org/C00-1013/) · **Licence:** [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/) — summary only [**25** citations](https://openalex.org/works?filter=cites:W2046998411) openalex · 2026-10-19[**3** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/boguslavsky-2000-creating-universal-networking-language-module/) of the 140673 papers whose bibliographies we read[COLING 2000 Volume 1: The 18th International Conference on Computational Linguistics](https://aclanthology.org/volumes/C00-1/)[licence](https://creativecommons.org/licenses/by-nc-sa/3.0/) - [Publisher's page](https://aclanthology.org/C00-1013/) - [PDF](https://aclanthology.org/C00-1013.pdf) - [DOI 10.3115/990820.990833](https://doi.org/10.3115/990820.990833) - [OpenAlex](https://openalex.org/W2046998411) **Authors (6)** 1. [Igor Boguslavsky](https://openalex.org/A5004657777) [ORCID](https://orcid.org/0000-0003-3390-1449) · [Semantic Scholar](https://www.semanticscholar.org/author/145605664) · h 11 · 490 citations · 75 works 2. [Nadezhda Frid](https://openalex.org/A5017092088) [Semantic Scholar](https://www.semanticscholar.org/author/2384113) · h 3 · 116 citations · 4 works 3. Leonid Iomdin [Semantic Scholar](https://www.semanticscholar.org/author/1716696) 4. [Leonid Kreidlin](https://openalex.org/A5077819303) [Semantic Scholar](https://www.semanticscholar.org/author/1745245) · h 3 · 116 citations · 4 works 5. [Irina Sagalova](https://openalex.org/A5056350231) [Semantic Scholar](https://www.semanticscholar.org/author/2190768) · h 1 · 25 citations · 2 works 6. [Victor Sizov](https://openalex.org/A5054795523) [Semantic Scholar](https://www.semanticscholar.org/author/1733972) · h 4 · 93 citations · 20 works Citations and author metrics from openalex (2026-10-19); authors with the same name are not always told apart. ## What it is about The paper describes a deconverter that turns Universal Networking Language (UNL) expressions into Russian, built inside the ETAP-3 system. A UNL expression is a directed hypergraph: nodes are Universal Words (English-based concept labels, optionally restricted), arcs are semantic relations such as agent, object, place and possession, and attributes such as present tense, definiteness and modal meaning sit on the nodes. ETAP-3 is transfer-based and works on a normalised syntactic structure, which is a tree with word order. The authors list where the two structures agree (both sit between surface and semantics, both have lexical nodes and attributes) and where they differ: a UNL node can be a subgraph, a UNL graph can contain loops, and UNL has no word order. The paper reports the module and its construction, not accuracy figures. ## Ideas - I1. Allow a meta-world node to be a subgraph for a multi-word concept. Keep the inner relations of the phrase, as the UNL hypernode does, and attach the outer relation to the phrase head. Test it on the phrase concepts already listed in `meta.rs`. - I2. Count the modal and aspect attributes in the residual. UNL keeps modal meaning as attributes, and Mova's meta tokens need the same information for the translator to choose a target word. Measure how many residual tokens are modal. - I3. Keep word order out of the meta-world. The paper's split is clear: the interlingua has no order, and order lives in the target syntax. In Mova that is the job of `en/src/lin.rs`. Measure the order residual separately, as the exact mode already does. ## For Mova The most useful point is the comparison of a graph interlingua with a tree-shaped target structure, which is the same problem as the meta-world's handling of heads and relations. The code is `tools/translate` (`meta.rs`) and `en/src/lin.rs`, with `tools/roundtrip` for the trees. The paper gives no accuracy measures, so the counts in I1 to I3 come first. Topics: [Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/topics/machine-translation/) ## Cite The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.3115/990820.990833](https://doi.org/10.3115/990820.990833) ``` @inproceedings{boguslavsky2000creating, title = {{Creating a Universal Networking Language Module within an Advanced NLP System}}, author = {Boguslavsky, Igor and Frid, Nadezhda and Iomdin, Leonid and Kreidlin, Leonid and Sagalova, Irina and Sizov, Victor}, booktitle = {COLING 2000 Volume 1: The 18th International Conference on Computational Linguistics}, year = {2000}, doi = {10.3115/990820.990833}, url = {https://doi.org/10.3115/990820.990833} } ``` - [← previous HUME: Human UCCA-Based Evaluation of Machine Translation](https://md.mova.elamur.ai/free/en/docs/papers/birch-2016-hume-human-ucca-based-evaluation/) - [next → Broad Coverage Multilingual Deep Sentence Generation with a Stochastic Multi-Level Realizer](https://md.mova.elamur.ai/free/en/docs/papers/bohnet-2010-broad-coverage-multilingual-deep-sentence/) - [↑ back to Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/mtrans/) ## References 4 references, 0 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. Apresjan Ju.D., I.M.Boguslavsky, L.L.Iomdin et al. (1992). ETAP-2: The Linguistics of a Machine Translation System. // META, Vol. 37, No 1, pp. 97-112. [find](https://scholar.google.com/scholar?q=Apresjan+Ju%2ED%2E%2C+I%2EM%2EBoguslavsky%2C+L%2EL%2EIomdin+et+al%2E+%281992%29%2E+ETAP%2D2%3A+The+Linguistics+of+a+Machine+Translation+System%2E+%2F%2F+META%2C+Vol%2E+37%2C+No+1%2C+pp%2E+97%2D112%2E) 2. Boguslavsky I.(1995). A bi-directional Russian-to-English machine translation system (ETAP-3). // Proceedings of the Machine Translation Summit V. Luxembourg. [find](https://scholar.google.com/scholar?q=Boguslavsky+I%2E%281995%29%2E+A+bi%2Ddirectional+Russian%2Dto%2DEnglish+machine+translation+system+%28ETAP%2D3%29%2E+%2F%2F+Proceedings+of+the+Machine+Translation+Summit+V%2E+Luxembourg%2E) 3. Iomdin L.& O. Streiter. (1999). Learning from Parallel Corpora: Experiments in Machine Translation. // Dialogue'99: Computational Linguistics and its Applications International Workshop. [find](https://scholar.google.com/scholar?q=Iomdin+L%2E%26+O%2E+Streiter%2E+%281999%29%2E+Learning+from+Parallel+Corpora%3A+Experiments+in+Machine+Translation%2E+%2F%2F+Dialogue%2799%3A+Computational+Linguistics+and+its+Applications+International+Workshop%2E) 4. Tarusa, Russia, June 1999. Vol.2, pp. 79-88. [find](https://scholar.google.com/scholar?q=Tarusa%2C+Russia%2C+June+1999%2E+Vol%2E2%2C+pp%2E+79%2D88%2E)