| N01-1010 | a clustering technique called | tree-cut | . We compare our lexicon to WordNet |
| J98-2002 | should employ to select the best | tree-cut | model . We adopt the Minimum |
| N01-1010 | Systematic Polysemy Using the | tree-cut | technique described above , our |
| N01-1010 | distribution of the clusters in the | tree-cut | F . It is calculated as k L ( |
| N01-1010 | work . 2.1 Tree-cut Models The | tree-cut | technique is applied to data |
| N01-1010 | abstract semantic classes . A | tree-cut | is a partition of a thesaurus |
| N01-1010 | best model is the one with the | tree-cut | -LSB- AIRCRAFT , ball , kite |
| N01-1010 | shows the MDL lengths for all five | tree-cut | models . The best model is the |
| N01-1010 | as a cluster .3 Clusters in a | tree-cut | exhaustively cover all leaf nodes |
| N01-1010 | In ( Li and Abe , 1998 ) , the | tree-cut | technique was applied to the |
| N01-1010 | of the description length for a | tree-cut | model is as follows . Given a |
| N01-1010 | MDL Principle To select the best | tree-cut | model , ( Li and Abe , 1998 ) |
| N01-1010 | . 2 The Tree-cut Technique The | tree-cut | technique is an unsupervised |
| N01-1010 | problem of selecting the best | tree-cut | model that estimates the true |
| N01-1010 | model M is a pair consisting of a | tree-cut | F and a probability parameter |
| N01-1010 | sample corpus data . Formally , a | tree-cut | model M is a pair consisting |
| N01-1010 | we give a brief summary of this | tree-cut | technique using examples from |
| N01-1010 | thesaurus tree and one possible | tree-cut | -LSB- AIRCRAFT , ball , kite |
| N01-1010 | Figure 2 shows parts of the final | tree-cuts | for the ARTIFACT and MEASURE |
| N01-1010 | third step , clusters in those two | tree-cuts | are matched up , and the pairs |
