1. Unsupervised Word Sense Disambiguation REU, Summer, 2009

2. Word Sense Disambiguation E.g., “The soldiers drove the tank.” armored combat vehicle large vessel for holding gases or liquids

3. hire Context Knowledge Base company programmer many computer “Many companies hire computer programmers” write programmer software “Computer programmers write software” + computer

4. Context Knowledge Base hire company programmer many computer write software 11 1 11 2 Result of merging dependency trees Weights are number of dependency relation instances found

5. WSD Algorithm Parse original sentence using Minipar, get weighted dependency tree. hire company programmer software computer “A large software company hires computer programmers.” 1 0.5 0.33 To-be-disambiguated word large 11 Weights are distances from to-be-disambiguated word

6. Parse each gloss of to-be-disambiguated word, get weighted dependency trees. WSD Algorithm Gloss 1: an institution created to conduct business create institution business unit smallmilitary Gloss 2: a small military unit conduct

7. For each word in a gloss tree, find that word’s dependent words in the context knowledge base. We are looking for words in the knowledge base that match words in the original sentence. In other words, we are looking for context clues to disambiguate a word. A score is generated based on the weights of those dependency relations in the knowledge base, and the dependent words of the to-be-disambiguated word in the original sentence. The more matches we find, the higher the generated score will be. The gloss with the highest generated score will be selected as the correct sense of the word. WSD Algorithm

8. Synonym Matching If no direct matches are found between a gloss word and dependency relations in context knowledge base, we can replace the gloss word with one of its synonyms, since synonyms are semantically equivalent words.

9. Hypernym/hyponym Matching E.g., animal mammal dog poodle Extract hypernyms and hyponyms of words from WordNet database. Store these in a data structure. Strategies:use all “levels” use only levels close to the original word apply the above strategies to synonym matching, as well

10. Word Similarity Use WordNet::Similarity Perl module to calculate “similarity score” between gloss word and dependent words in knowledge base. The most similar word found will be considered the closest to an actual match. doganimal 0.780 dogdesk 0.162 WordNet::Similarity similarity scores