learn.ifs.name

Unsupervised Word Sense Disambiguation Rivaling Supervised Methods

Yarowsky, 1995, Unsupervised Word Sense Disambiguation Rivaling Supervised Methods (Proceedings of the 33rd Annual Meeting of ACL)

Subscriber deck — free during early access

4 cards in “4 The algorithm” · whole deck

4 The algorithm

  1. Outline Yarowsky’s bootstrapping algorithm.
    (1) Collect all contexts of the target word from a large untagged corpus. (2) Tag a few examples per sense via seed collocations (e.g. life vs manufacturing for plant), leaving a large untagged residual. (3) Train a decision list on the seed sets, apply it to the residual, and add examples classified above a threshold (optionally using one sense per discourse to filter and extend). (4) Repeat until the residual is stable. (5) Use the final classifier on new data.
    labelled setsseeds + newdecision listcollocationslabel residualkeep if confidenttrainapplyadd
    One sense per discourse can extend and correct labels at each round
  2. What does the decision list learned for plant look like?
    Collocations ranked by log-likelihood ratio, each pointing to a sense, e.g. initially manufacturing plant, life (within ±10 words) and animal → living, equipment and employee → factory. In the final list the seeds are displaced by broader collocations such as car, union, flower, job, fruit.
  3. How does one sense per discourse help bootstrapping find new collocations?
    Extending a sense label to other occurrences of the word in the same discourse can form a bridge to collocations (like cell for living plants) that never co-occur with the original seeds nearby; it can also correct mislabelled examples.
  4. Why can an original seed end up indicating the other sense?
    Seeds are only a starting point: if a seed collocate turns out more compatible with the other class, the final list reflects that, so a few misleading seeds aren’t fatal as long as most seeds form a coherent collocation space.

Source

  • David Yarowsky. 1995. Unsupervised Word Sense Disambiguation Rivaling Supervised Methods. In Proceedings of the 33rd Annual Meeting of the Association for Computational Linguistics, pp. 189–196. https://aclanthology.org/P95-1026/