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Icon 2007 Pedersen

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University of Minnesota, Duluth
University of Minnesota, Duluth

The document summarizes research on using different semantic techniques like contexts, co-occurrences, and ontologies to build a "semantic quilt" that can be used for natural language processing tasks. It discusses using n-gram statistics to identify associated words, sense clusters to identify similar contexts, and WordNet to measure conceptual similarity. The goal is to integrate these different semantic resources and methods to solve problems with less reliance on manually built resources.

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The Semantic Quilt: Contexts, Co-occurrences, Kernels, and Ontologies Ted Pedersen University of Minnesota, Duluth http://www.umn.edu/home/tpederse
Create by stitching together
Sew together different materials
Semantics in NLP ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
What do we mean by semantics ? …it depends on our resources… ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
What level of granularity? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
The Terrible Tension : ambiguity versus granularity ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
The Current State of Affairs ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Things we can do now… ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Things we want to do… ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Semantic Patches to Sew Together ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Co-occurrences Ngram Statistics Package http://ngram.sourceforge.net
Things we can do now… ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Co-occurrences and semantics? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Why pairs of words? ,[object Object],[object Object],[object Object],[object Object],[object Object]
Bigrams ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
“ occur together more often than expected by chance…” ,[object Object],[object Object],[object Object],[object Object]
2x2 Contingency Table 100,000 300 not Artificial 400 100 Artificial not Intelligence Intelligence
2x2 Contingency Table 100,000 99,700 300 99,600 99,400 200 not Artificial 400 300 100 Artificial not Intelligence Intelligence
2x2 Contingency Table 100,000 99,700 300 99,600 99,400.0 99,301.2 200.0 298.8 not Artificial 400 300.0 398.8 100.0 000.12 Artificial not Intelligence Intelligence
Measures of Association
Measures of Association
Interpreting the Scores… ,[object Object],[object Object],[object Object]
Measures of Association all supported in NSP http://ngram.sourceforge.net ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
What do we get at the end? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Contexts SenseClusters http://senseclusters.sourceforge.net
Things we can do now… ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Similar Contexts may have the same meaning… ,[object Object],[object Object],[object Object],[object Object],[object Object]
Clustering Similar Contexts ,[object Object],[object Object],[object Object],[object Object],[object Object]
Contexts (input) ,[object Object],[object Object],[object Object],[object Object],[object Object]
Contexts (output) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
General Methodology ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Second Order Features ,[object Object],[object Object],[object Object]
Second Order Context Representation ,[object Object],[object Object],[object Object],[object Object],[object Object]
2 nd Order Context Vectors ,[object Object],0 6272.85 2.9133 62.6084 20.032 1176.84 51.021 O2 context 0 18818.55 0 0 0 205.5469 134.5102 guy 0 0 0 136.0441 29.576 0 0 Oscar 0 0 8.7399 51.7812 30.520 3324.98 18.5533 won needle family war movie actor football baseball
Second Order Co-Occurrences ,[object Object],[object Object],[object Object],[object Object],1.0 .72 memory .00 .00 organ .13 1.1 .03 2.7 1.7 .16 .00 .96 linux .00 .91 .00 2.1 1.3 .01 .00 .76 disk Plasma graphics tissue data ibm cells blood apple
After context representation… ,[object Object],[object Object],[object Object],[object Object],[object Object]
What do we get at the end? ,[object Object],[object Object],[object Object]
Finding Similar Contexts ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Semantic Similarity WordNet-Similarity http://wn-similarity.sourceforge.net
Things we can do now… ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Similarity and Relatedness ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Similarity as Organizing Principle ,[object Object],[object Object],[object Object],[object Object]
Why measure conceptual similarity? ,[object Object],[object Object],[object Object],[object Object]
Word Sense Disambiguation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
WordNet-Similarity http://wn-similarity.sourceforge.net ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Path Finding ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
watercraft instrumentality object artifact conveyance vehicle motor-vehicle car boat ark article ware table-ware cutlery fork from Jiang and Conrath [1997]
Information Content ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Observed “car”... motor vehicle (327 +1) *root* (32783 + 1) minicab (6) cab (23) car (73 +1) bus (17) stock car (12)
Observed “stock car”... motor vehicle (328+1) *root* (32784+1) minicab (6) cab (23) car (74+1) bus (17) stock car (12+1)
After Counting Concepts... motor vehicle (329) IC = 1.9 *root* (32785) minicab (6) cab (23) car (75) bus (17) IC = 3.5 stock car (13) IC = 3.1
Similarity and Information Content ,[object Object],[object Object],[object Object]
Why doesn’t this solve problem? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Using Dictionary Glosses to Measure Relatedness ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Context/Gloss Vectors ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Gloss/Context Vectors
Word Sense Disambiguation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
WordNet-SenseRelate http://senserelate.sourceforge.net ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
WSD Experiment ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Human Relatedness Experiment ,[object Object],[object Object],[object Object],[object Object]
Coverage… ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Supervised WSD http://www.d.umn.edu/~tpederse/supervised.html
Things we can do now… ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Machine Learning Approach ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Clever Ways to Get Training Data ,[object Object],[object Object],[object Object],[object Object]
Where does this leave us? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Integration that already exists… ,[object Object],[object Object],[object Object],[object Object]
Kernels are similarity matrices ,[object Object],[object Object],[object Object],[object Object],[object Object]
Conclusion ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Many Thanks … ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
URLs ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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