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# Automatic Interpretation of Noun Compounds Using WordNet Similarity

**Authors:** Su Nam Kim, [Timothy Baldwin](https://md.mova.elamur.ai/free/en/docs/papers/authors/timothy-baldwin/) · **Year:** 2005 · **Venue:** Second International Joint Conference on Natural Language Processing (IJCNLP 2005) **Link:** [aclanthology.org/I05-1082](https://aclanthology.org/I05-1082/) · **Licence:** [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/) — summary only

[**102** citations](https://openalex.org/works?filter=cites:W1540103129) openalex · 2026-10-18[**32** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/aclbulk-2005-kim-automatic-interpretation-noun-compounds-wordnet/) of the 140673 papers whose bibliographies we read[Second International Joint Conference on Natural Language Processing: Full Papers](https://aclanthology.org/volumes/I05-1/)[licence](https://creativecommons.org/licenses/by-nc-sa/3.0/)

- [Publisher's page](https://aclanthology.org/I05-1082/)
- [PDF](https://aclanthology.org/I05-1082.pdf)
- [DOI 10.1007/11562214_82](https://doi.org/10.1007/11562214_82)
- [OpenAlex](https://openalex.org/W1540103129)

**Authors (2)**

1. [Su Nam Kim](https://openalex.org/A5110283311) h 18 · 2192 citations · 51 works
2. [Timothy Baldwin](https://openalex.org/A5103085805) [ORCID](https://orcid.org/0000-0002-4445-1386) · h 64 · 18251 citations · 880 works

Citations and author metrics from openalex (2026-10-18); authors with the same name are not always told apart.

## What it is about

The paper interprets a novel binary noun compound, such as "paper submission", by finding the most similar training compound and copying its semantic relation. Similarity between the two nouns comes from WordNet::Similarity, with four measures compared. The relation set has 20 members, and the data is noun compounds from the Penn Treebank Wall Street Journal: 1,088 training and 1,081 test instances. The best measure, WUP, classifies 53.3% of the test compounds correctly. That is above the 43.0% majority baseline and at the level of the 52.3% inter-annotator agreement. The authors also expect the modifier and the head noun to carry different weight for different relations (for TIME, the modifier), and they test that with a weight parameter.

## Ideas

- I1. Nearest-compound classifier in `onto/src/wsd.rs` with WordNet path similarity between modifier and head synsets, copying the relation of the nearest training compound. Measure: accuracy on EWT noun compounds against the majority-class baseline, two identical runs.
- I2. Relation-specific weight between modifier and head (the alpha of the paper) in `en/src/explain.rs`, fitted on a split. Measure: accuracy on the held-out split, with one weight versus a weight per relation.
- I3. Before measuring, count the share of binary compounds in the EWT test set. The paper reports 88.4% binary in the WSJ, and that share sets the scope. Measure: the share, reported with the first result.

## For Mova

The chapter is [18. Compounding, conversion and the minor processes](https://md.mova.elamur.ai/free/en/docs/languages/en/18-compounding-conversion-minor/). The code is `onto/src/wsd.rs` and `en/src/explain.rs`. The WSJ material is licensed Penn Treebank data, so it is a test only, and the paper is NC; the 20-relation set is a design source, not something to copy into a release.

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.1007/11562214_82](https://doi.org/10.1007/11562214_82)

```
@inproceedings{kim2005automatic,
  title = {{Automatic Interpretation of Noun Compounds Using WordNet Similarity}},
  author = {Kim, Su Nam and Baldwin, Timothy},
  booktitle = {Second International Joint Conference on Natural Language Processing: Full Papers},
  year = {2005},
  doi = {10.1007/11562214\_82},
  url = {https://doi.org/10.1007/11562214_82}
}
```

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