| C96-1013 | them , we are developing a set of | graph matching | rules . Figure 2 exemplifies |
| C96-1013 | relations is essential to the | graph matching | operations we use for the integration |
| E09-1097 | modifiers to head words as a bipartite | graph matching | problem . This is similar to |
| C96-1013 | CCKG . If matching surpasses the | graph matching | threshold , perform integration |
| C96-1013 | this rea - son , we establish a | graph matching | threshold to decide whether we |
| D14-1085 | as a maximum-weight bipartite | graph matching | problem ( Figure 4 ) . Formally |
| C96-1013 | Again , if matching is over the | graph matching | thresh - old , perform integration |
| E09-1097 | heads as a weighted bipartite | graph matching | ( or assignment ) problem , a |
| C96-1013 | hierarchy , relation hierarchy and | graph matching | operations , we now describe |
| D13-1038 | representation , REG can be formulated as a | graph matching | algorithm similar to that described |
| H05-1049 | Linguistics Robust Textual Inference via | Graph Matching | </title> Aria D Haghighi Andrew |
| D10-1042 | abstracting entity translation as a | graph matching | problem of two graphs Ge and |
| C04-1021 | of a valid interpretation to a | graph matching | problem ( Popescu et al. , 2003 |
| J12-1007 | , and textual entailment using | graph matching | . Methods for word sense and |
| E09-1097 | spanning tree as a weighted bipartite | graph matching | problem ( or the assignment problem |
| C96-1013 | our definitions , we can use the | graph matching | operations defined in ( Sowa |
| D14-1085 | sentence compression . The resulting | graph matching | problem is solved using the NetworkX |
| E09-1097 | selection as a weighted bipartite | graph matching | ( or assignment ) problem . This |
| C04-1021 | reducing semantic interpretation to a | graph matching | problem that is solved by MaxFlow |
| D13-1038 | the corresponding hyperarc . 3.4 | Graph Matching | for REG Now the hypergraph representing |
