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Table 1: Famous persons and their corresponding homo- graphic place names.
2006
"... In PAGE 2: ... 3.1 Person Name Ambiguity Table1 lists known persons where the last name and the first name are also cities somewhere in the world. apos;Javier apos; being a city in Spain and apos;Solana apos; a city in the Philippines, a place name lookup in a sentence containing apos;EU-general secretary Javier Solana apos; would recognise these two cities.... ..."
Cited by 2
Table 3. Readers/writers-examples. 6 Conclusion We have introduced a deadlock detection method based on net unfoldings us- ing linear algebraic techniques. Moreover, we have presented an implementation of McMillan apos;s deadlock algorithm and we pointed out the performance gap be- tween McMillan apos;s LISP implementation and our optimized C version. By means of several examples we have pointed out the strong and weak aspects of both approaches. The results show that the larger the percentage of cut-o events is, the more likely the new method will yield better performance than McMillan apos;s. Our future work is to exploit some more CPLEX heuristic in order to speed up our implementation. Acknowledgements. We thank Javier Esparza for drawing our attention to this problem and Ken McMillan for sending us his LISP sources of the DME generator.
1997
"... In PAGE 10: ...resented. We modelled a 4-bit implementationbased on busy waiting semaphors. We used our methods to check deadlock freeness for a setting with one writer and two or three readers (SYNC). The results are depicted in Table3 . In con- trast to the DME example we see that the application of the linear algebraic approach turns out to yield better results if the percentage of cut-o events is... ..."
Cited by 44
Table 2 shows the benchmarks for each dataset, using the three measures just defined. The new algorithm when only using VSM-based similarity (VSMOnly) outperforms the existing algorithm (Baseline) by 5%. The new algorithm using the full context similarity measures including IE features (Full) significantly outperforms the existing algorithm (Baseline) in every test: the overall F-
"... In PAGE 7: ... Constructed Testing Corpus I # of Mentions Name Set 1a Set 1b Mikhail S. Gorbachev 20 50 Dick Cheney 20 10 Dalai Lama 20 10 Bill Clinton 20 10 Set 2a Set 2b Bob Dole 20 50 Hun Sen 20 10 Javier Perez de Cuellar 20 10 Kim Young Sam 20 10 Set 3a Set 3b Jiang Qing 20 10 Ingrid Bergman 20 10 Margaret Thatcher 20 50 Aung San Suu Kyi 20 10 Set 4a Set 4b Bill Gates 20 10 Jiang Zemin 20 10 Boris Yeltsin 20 50 Kim Il Sung 20 10 Table2 . Testing Corpus I Benchmarking ... ..."
