---
title: "Automatic Word Sense Discrimination — Schütze 1998"
description: "The paper addresses word sense discrimination: deciding which occurrences of an ambiguous word share a meaning, without naming that meaning."
authors: ["Hinrich Schütze"]
year: 1998
venue: "Computational Linguistics"
source: "https://aclanthology.org/J98-1004/"
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/schutze-1998-automatic-word-sense-discrimination/"
---
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# Automatic Word Sense Discrimination

**Authors:** [Hinrich Schütze](https://md.research.elamur.ai/free/en/authors/hinrich-schutze/) · **Year:** 1998 · **Venue:** Computational Linguistics · **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/J98-1004/) · [PDF](https://aclanthology.org/J98-1004.pdf) · [OpenAlex](https://openalex.org/W2130337399)

**Citations:** 1263 citations (OpenAlex, 2026-10-14) · [223](https://md.research.elamur.ai/free/en/papers/schutze-1998-automatic-word-sense-discrimination/cited-by/) in our library, of the 140884 papers whose bibliographies we read

## Summary
The paper addresses word sense discrimination: deciding which occurrences of an ambiguous word share a meaning, without naming that meaning. Treating this separately from sense labeling removes the need for dictionaries, thesauri, bilingual corpora or hand-tagged examples. Schütze proposes context-group discrimination. Each word gets a vector in Word Space, a high-dimensional real-valued space, built from the words it co-occurs with in a training corpus. A context of the ambiguous word is represented by summing the vectors of its surrounding words. Similarity therefore rests on second-order co-occurrence, which is less sparse and more robust than direct co-occurrence. Training contexts are clustered, and each cluster's centroid becomes a sense vector. A new occurrence is assigned to the nearest centroid. Several representations are compared, some reduced with SVD. The method is fully unsupervised and was tested on natural and artificial ambiguous words from the New York Times News Wire, where it performed well. This matters for information access, such as sense-based retrieval and search interfaces that account for ambiguity. The supplied extract omits the exact accuracy figures.

## Key points
- Disambiguation is split into discrimination and labeling. Only discrimination is tackled, so no external knowledge source is needed in training or application.
- The method uses three kinds of vectors:
  - word vectors, from neighbors within a sentence or larger window;
  - context vectors, the sum of the word vectors in a context;
  - sense vectors, the cluster centroids.
  
  Variants with SVD dimensionality reduction are also tested.
- Cluster granularity is adjustable. With few clusters, *space* splits into "outer space" and "spatial extent". With more clusters (e.g., n = 10), finer uses such as office vs. exhibition space appear.
- The evaluation data comes from the New York Times News Wire and covers both natural and artificial ambiguous words.
- Background and motivation:
  - The idea was first described in Schütze (1992b).
  - Schütze and Pedersen (1995) found that sense-based document–query similarity improved relevance ranking.
  - Miller and Charles (1991) support the link between contextual and semantic similarity.

*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/)

Topics: [Word sense disambiguation](https://md.research.elamur.ai/free/en/topics/word-sense-disambiguation/)

## Institutions

- [Palo Alto Research Center](https://md.research.elamur.ai/free/en/labs/palo-alto-research-center/) — Research institute, Palo Alto, United States · 9 works · 4 797 citations

## Cite

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

```bibtex
@article{schutze1998automatic,
  title = {{Automatic Word Sense Discrimination}},
  author = {Schütze, Hinrich},
  journal = {Computational Linguistics},
  year = {1998},
  url = {https://aclanthology.org/J98-1004/}
}
```

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## References

15 references, 2 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Sanderson, Mark. 1994. Word sense disambiguation and information retrieval. In Proceedings of SIGIR "94, pages 142-151. [find](https://scholar.google.com/scholar?q=Sanderson%2C+Mark%2E+1994%2E+Word+sense+disambiguation+and+information+retrieval%2E+In+Proceedings+of+SIGIR+%2294%2C+pages+142%2D151%2E)
2. Schiitze, Hinrich. 1992a. Context space. In Robert Goldman, Peter Norvig, Eugene Charniak, and Bill Gale, editors, Working Notes of the AAAI Fall Symposium on Probabilistic Approaches to Natural Language, pages 113-120, AAAI Press, Menlo Park, CA. [find](https://scholar.google.com/scholar?q=Schiitze%2C+Hinrich%2E+1992a%2E+Context+space%2E+In+Robert+Goldman%2C+Peter+Norvig%2C+Eugene+Charniak%2C+and+Bill+Gale%2C+editors%2C+Working+Notes+of+the+AAAI+Fall+Symposium+on+Probabilistic+Approaches+to+Natural+Langu)
3. Schiitze, Hinrich. 1992b. Dimensions of meaning. In Proceedings of Supercomputing "92, pages 787-796, Minneapolis, MN. [find](https://scholar.google.com/scholar?q=Schiitze%2C+Hinrich%2E+1992b%2E+Dimensions+of+meaning%2E+In+Proceedings+of+Supercomputing+%2292%2C+pages+787%2D796%2C+Minneapolis%2C+MN%2E)
4. Schtitze, Hinrich. 1997. Ambiguity Resolution in Language Learning. CSLI Publications, Stanford, CA. [find](https://scholar.google.com/scholar?q=Schtitze%2C+Hinrich%2E+1997%2E+Ambiguity+Resolution+in+Language+Learning%2E+CSLI+Publications%2C+Stanford%2C+CA%2E)
5. Schiitze, Hinrich and Jan O. Pedersen. 1995. Information retrieval based on word senses. In Proceedings of the Fourth Annual Symposium on Document Analysis and Information Retrieval, pages 161-175, Las Vegas, NV. [find](https://scholar.google.com/scholar?q=Schiitze%2C+Hinrich+and+Jan+O%2E+Pedersen%2E+1995%2E+Information+retrieval+based+on+word+senses%2E+In+Proceedings+of+the+Fourth+Annual+Symposium+on+Document+Analysis+and+Information+Retrieval%2C+pages+161%2D175%2C+La)
6. Schiitze, Hinrich and Jan O. Pedersen. 1997. A cooccurrence-based thesaurus and two applications to information retrieval. [find](https://scholar.google.com/scholar?q=Schiitze%2C+Hinrich+and+Jan+O%2E+Pedersen%2E+1997%2E+A+cooccurrence%2Dbased+thesaurus+and+two+applications+to+information+retrieval%2E)
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8. Sparck-Jones, Karen. 1991. Notes and references on early classification work. ACM SIGIR Forum, 25(1):10-17. van Rijsbergen, C. J. 1979. Information Retrieval. Second edition. Butterworths, London. [find](https://scholar.google.com/scholar?q=Sparck%2DJones%2C+Karen%2E+1991%2E+Notes+and+references+on+early+classification+work%2E+ACM+SIGIR+Forum%2C+25%281%29%3A10%2D17%2E+van+Rijsbergen%2C+C%2E+J%2E+1979%2E+Information+Retrieval%2E+Second+edition%2E+Butterworths%2C+London%2E)
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