1. Jiuling Zhang 2009-03-04

2.  Why perform query expansion?  WordNet based Word Sense Disambiguation WordNet Word Sense Disambiguation  Conceptual Query Expansion  Experiments  Conclusion  Future work  References

3. queries are baffled with:  Incompleteness lack of enough knowledge  Vagueness uncertainties inherent to natural languages: synonymous & polysemous  Spelling errors

4. How to handle this problem?  Query expansion/reformulation with a thesaurus  Query expansion by automatic thesaurus generation  Spelling correction

5. Examples & recent researches:  Qiu introduced query expansion based on concept[1]  Smeaton tried to expand weighting and word sense disambiguation techniques[2]  Hoeber manually constructed a concept network to expand[3]

6.  A large manually constructed comprehensive thesaurus developed at Princeton[4]  WordNet is organized into a network of synonyms(synsets)  A synset is basic element in WordNet and words of the same synset are exchangeable in some context

7.  Example: word vs. synset wn good -synsn Sense 1 good => advantage, vantage Sense 2 good, goodness => morality Sense 3 good, goodness => quality Sense 4 commodity, trade good, good => artifact, artifact

8.  Word Sense Disambiguation supervised Word Sense Disambiguation: relies on a sense-tagged corpus unsupervised Word Sense Disambiguation: relies on a machine readable thesaurus instead of sense-tagged corpus[5]  Our method belongs to the latter one

9. Components:  WordNet  WordNet modules: WordNet::SenseRelate::AllWords[6] WordNet::QueryData[7] Lingua::WordNet[8]

10.  The adapted Lesk measure is employed.  Adapted Lesk algorithm is higher version of the Lesk method by counting the number of overlaps not only of glosses of synonymy but also of glosses of other related synsets, hyponymy, meronymy, troponymy e.g. [5]

11. Procedure: I. Preprocess II. Perform WSD to query III. Combine new terms to obtain new queries IV. Perform WSD to obtained queries V. Compare synset array & select expanded queries

12. The flow chart of query expansion procedure

13.  Short queries are avoided  Expanded queries are to the Google and evaluated by 10 persons  Precision@10 are recorded

14.  Results: Query_numOriginal queryExpanded queryIncrement 10.60.66250.0625 20.6750.6125-0.0625 30.650.700.05 40.51250.53750.025 50.6250.76250.1365 Avg@100.61250.6550.0425

15.  Propose a new concept based query expansion using WordNet and WSD  Experimental results show it can improve effectiveness

16.  Apply the similar idea to sentences in documents to perform documents expansion  Employing Markov language model to modify newly generated queries[9]  Experiments on TREC Web Track collections

17. [1] Qiu, Y., Frei, H.-P.: Concept based query expansion. In Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval. ACM Press, Pittsburgh, Pennsylvania, USA (1993) 160-169 [2] R. Richardson, AF Smeaton.: Using WordNet in a Knowledge-Based Approach to Information Retrieval. Proceedings of the BCS-IRSG Colloquium, Crewe(1995) [3] Hoeber, X.-D. Yang, and Y. Yao.: Conceptual query expansion. In Proceedings of the Atlantic Web Intelligence Conference (2005) [4] Miller, G., R. Beckwith, C. Fellbaum, D. Gross, and K. Miller.: Five papers on WordNet. CSL Report 43, Cognitive Science Laboratory, Princeton University(1990) [5] Patwardhan, S., Banerjee, S., Pedersen, T.: UMND1: Unsupervised Word Sense Disambiguation Using Contextual Semantic Relatedness. In the Proceedings of SemEval-2007: 4th International Workshop on Semantic Evaluations(2007) 390-393 [6] http://search.cpan.org/~jrennie/WordNet-SenseRelate-AllWords/ [7] http://search.cpan.org/~jrennie/WordNet-QueryData-1.47/ [8] http://search.cpan.org/~dbrian/Lingua-Wordnet-0.74/ [9] Shuang Liu, Fang Liu, Clement Yu Weiyi Meng.: An Effective Approach to Document Retrieval via Utilizing WordNet and Recognizing Phrases. In Proceedings of the 27th Annual International ACM/SIGIR Conference on Research and development in information retrieval, Sheffield, Yorkshire, UK(2004)

18.  Thanks!