---
title: "An IR Approach for Translating New Words from — Fung 1998"
description: "The paper addresses how to find translations for new words, such as recent proper names or freshly coined terms, that a machine translation lexicon lacks…"
authors: ["Pascale Fung", "Lo Yuen Yee"]
year: 1998
venue: "36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 1"
source: "https://aclanthology.org/P98-1069/"
doi: "10.3115/980845.980916"
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/fung-1998-ir-approach-translating-new-words/"
---
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# An IR Approach for Translating New Words from Nonparallel, Comparable Texts

**Authors:** [Pascale Fung](https://md.research.elamur.ai/free/en/authors/pascale-fung/), Lo Yuen Yee · **Institutions:** [Hong Kong University of Science and Technology](https://md.research.elamur.ai/free/en/universities/hong-kong-university-of-science-and-technology/) · **Year:** 1998 · **Venue:** 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 1 · **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/P98-1069/) · [PDF](https://aclanthology.org/P98-1069.pdf) · [DOI 10.3115/980845.980916](https://doi.org/10.3115/980845.980916) · [OpenAlex](https://openalex.org/W2041232209)

**Citations:** 316 citations (OpenAlex, 2026-10-14) · [126](https://md.research.elamur.ai/free/en/papers/fung-1998-ir-approach-translating-new-words/cited-by/) in our library, of the 140884 papers whose bibliographies we read

## Summary
The paper addresses how to find translations for new words, such as recent proper names or freshly coined terms, that a machine translation lexicon lacks and that no parallel corpus yet contains. Fung and Yee use nonparallel but comparable English–Chinese newspaper text downloaded from the Web: Hong Kong Standard and Mingpao issues from 12–31 December 1997. Their method borrows from information retrieval. Each word is represented as a vector over the "seed words" of an existing bilingual MT lexicon that co-occur with it in sentences. Dimensions are weighted with TF/IDF and down-weighted when a seed translation is ambiguous. Candidate translations are then ranked with cosine- and Dice-style similarity scores and their products. Applied to unknown Chinese words, mostly names, almost every unambiguous one has its correct English translation within the top 100 candidates, and six are ranked first. The work shows that comparable, continuously updated monolingual sources can support lexicon expansion without costly parallel data.

## Key points
- Corpus: about 3 Mb of English (Hong Kong Standard) and 8.8 Mb of Chinese (Mingpao) from the same three-week period. The authors treat the two papers as comparable in size and topic.
- The seed bridges come from the 20,000-entry lexicon of a broad-coverage English–Chinese MT system. The motivating unknown word was the Chinese term for flu (liougan).
- The context of liougan shares 233 seed words with the context of *flu* but only 121 with *Africa*. IDF lowers the weight of the ubiquitous *Hong Kong* relative to *virus*.
- The paper defines similarity measures S0–S7. These include plain cosine, cosine with IDF, a Dice variant, versions with 1/k confidence weighting for the k-th lexicon candidate, and products of measures. The measures with IDF and confidence weighting place *flu* first for liougan, while plain S0 does not.
- Errors include *Lunar* (not actually unknown in English) and *Yeltsin* (correct answer ranked 4th). Precision is described as good, but recall is limited by segmentation and word ambiguity.

## Interesting ideas
- **Translate words the way IR matches documents.** Treating a word's sentence-level context as a "document" makes standard IR machinery, such as TF/IDF weighting and cosine or Dice similarity, directly reusable for cross-lingual lexicon induction. This is handy for any language pair with only comparable corpora, for example Ukrainian–English news.
- **Discount ambiguous bridges.** Dividing a seed pair's weight by the rank of that translation in the lexicon is a cheap way to reduce noise from one-to-many dictionary entries. It is relevant wherever the bilingual dictionary is rich but ambiguous.
- **Combine complementary similarity scores.** The authors reason that comparing context segments sits between query-to-document and document-to-document matching. They therefore multiply measures designed for each case, a simple ensemble trick worth trying in other matching tasks.
- **Use time-aligned web news as a corpus.** Downloading same-period newspapers in both languages makes the corpus timely and topically aligned, which is exactly what new names and terms require. Collection can also run automatically in the background.
- **Bootstrap the seed lexicon.** The authors propose adding high-confidence new pairs back into the seed list so that each iteration can uncover more translations. This is a natural self-training loop for growing low-resource dictionaries.

*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: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

## Cite

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

```bibtex
@inproceedings{fung1998ir,
  title = {{An IR Approach for Translating New Words from Nonparallel, Comparable Texts}},
  author = {Fung, Pascale and Yee, Lo Yuen},
  booktitle = {36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 1},
  year = {1998},
  doi = {10.3115/980845.980916},
  url = {https://doi.org/10.3115/980845.980916}
}
```

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## References

31 references, 7 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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3. Table 6: English words most similar to ~,~/liougan SO 0.181114 0.088879 0.085886 0.081411 Lei flu Tang b'-~,~ ~,l~ Ap ~'~ $4 flu Lei Beijing poultry ~,~ ~,~ ~r~ ~,r~, 0.120879 0.097577 0.068657 0.065833 $5 0.086287 0.040090 0.028157 0.024500 $6 0.010430 0.001854 0.001840 0.001682 $7 0.007669 0.00195… [find](https://scholar.google.com/scholar?q=Table+6%3A+English+words+most+similar+to+%7E%2C%7E%2Fliougan+SO+0%2E181114+0%2E088879+0%2E085886+0%2E081411+Lei+flu+Tang+b%27%2D%7E%2C%7E+%7E%2Cl%7E+Ap+%7E%27%7E+%244+flu+Lei+Beijing+poultry+%7E%2C%7E+%7E%2C%7E+%7Er%7E+%7E%2Cr%7E%2C+0%2E120879+0%2E097577+0%2E068657+0%2E0658)
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