Using the Web as an Implicit Training Set: Application to Structural Ambiguity Resolution

Recent work has shown that very large corpora can act as training data for NLP algorithms even without explicit labels. In this paper we show how the use of surface features and paraphrases in queries against search engines can be used to infer labels for structural ambiguity resolution tasks. Using unsupervised algorithms, we achieve 84% precision on PP-attachment and 80% on noun compound coordination.

• Publication year

  2005

• Venue

  Human Language Technology - The Baltic Perspectiv

• Publication date

  2005-10-06

• Fields of study

  Linguistics, Computer Science

• Identifiers
  DOI 10.3115/1220575.1220680
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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