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A Method for Word Sense Disambiguation on Unrestricted Text

This presentation outlines a comprehensive method for Word Sense Disambiguation (WSD) focused on unrestricted text, presented by Marian Olteanu and based on the work of Rada Mihalcea and Dan Moldovan. The approach integrates multiple WSD techniques, including unsupervised learning and semantic density analysis using WordNet. Key methodologies include tagging content words, utilizing web resources for corpus creation, and examining verb-noun pairs in context to identify correct senses. The evaluation employs SemCor, comparing results from different algorithms and refining the disambiguation process.

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A Method for Word Sense Disambiguation on Unrestricted Text

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  1. A method for WSD on Unrestricted Text Authors: Rada Mihalcea and Dan Moldovan Presenter: Marian Olteanu

  2. Introduction • WSD methods: • Information in MRD (machine readable dictionaries) • Supervised training (info from a disambiguated corpus) • Unsupervised training (info from a raw corpus) • Hybrid methods

  3. Approach • Unsupervised learning • Tag all content words (nouns, verbs, adjectives, adverbs) • Use Web as a corpus (Altavista search engine) • Use semantic density (using WordNet)

  4. Algorithm • Use word pairs (one word in the context of the other) • Verb-noun pairs (syntactically linked) • I.e.: investigate report • {report#1, study}, {report#2, news report, story, account, write up}

  5. Algorithm (cont.) • Search for “investigate report” and “investigate study” – first sense • Search for “investigate report”, “investigate news report”, …, “investigate write up” – second sense • Order sense # by counts

  6. Algorithm (cont.) • Repeat for verbs • Use both phrases and NEAR operator – similar results • Select first 4 senses for N and V, first 2 for J and R

  7. Algorithm – step 2 • Compute conceptual density • Apply only for N-V pair (because WN doesn’t have adequate hierarchies for J and R) • Between senses found at step 1 • Count match between nouns in the sub-glosses of the verb and all the hyponyms (+noun) for the noun

  8. Algorithm – step 2 (cont.) • Formula: • I find it flawed (log part) • revise law:

  9. Evaluation • SemCor • Step 1: • Step 2:

  10. Comparison

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