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TABLE2BilingualExperiments

in Using EuroWordNet in a Concept-based Approach to Cross-Language Text Retrieval
by Julio Gonzalo, Felisa Verdejo 1999
Cited by 14

Table 1: Results of key-phrase spotting

in Hierarchical Tag-Graph Search For Spontaneous Speech Understanding In Spoken Dialog Systems
by Bor-shen Lin, Berlin Chen, Hsin-min Wang, Lin-Shan Lee
"... In PAGE 3: ... A total of 102 voice memos and 100 simple speech queries recorded by four male speakers were used in the following experiments. Table1 shows the results of key-phrase spotting. Note that, here the date-time expression part of each utterance (voice memo and speech query) may contain several phrases, such as DATE , TIME-RANGE and TIME , and each phrase may contain several semantic slots, e.... ..."

Table 6: Results of the bilingual runs

in Different approaches to Cross Language Information Retrieval
by Wessel Kraaij, Renee Pohlmann 2001
"... In PAGE 11: ...Table 6: Results of the bilingual runs Table6 lists the results of the bilingual experiments. The base run for Dutch to... ..."
Cited by 5

Table 2. Summary of bilingual runs of the Experiment.

in Bengali and Hindi to English CLIR Evaluation
by Debasis M, Mayank Gupta, Ipan D, Pratyush Banerjee, Sudeshna Sarkar
"... In PAGE 5: ...Sl.# Run ID Topic Lang Topic Field(s) 1 BENGALITITLE Bengali title 2 BENGALITITLEDESC Bengali title+desc 3 BENGALITITLEDESCNARR Bengali title+desc+narr 4 HINDITITLE Hindi title 5 HINDITITLEDESC Hindi title+desc 6 HINDITITLEDESCNARR Hindi title+desc+narr The results of our cross-language task are summarized in Table2 and Table 3. Table 2 shows that the recall gradually improved with the addition of more relevant terms from the topic fields for Bengali, as expected, but the same did not repeat for Hindi.... In PAGE 5: ...# Run ID Topic Lang Topic Field(s) 1 BENGALITITLE Bengali title 2 BENGALITITLEDESC Bengali title+desc 3 BENGALITITLEDESCNARR Bengali title+desc+narr 4 HINDITITLE Hindi title 5 HINDITITLEDESC Hindi title+desc 6 HINDITITLEDESCNARR Hindi title+desc+narr The results of our cross-language task are summarized in Table 2 and Table 3. Table2 shows that the recall gradually improved with the addition of more relevant terms from the topic fields for Bengali, as expected, but the same did not repeat for Hindi. This result was a surprise to us as we had used a bilingual lexicon of superior performance for Hindi.... ..."

Table 4. Bilingual experiments by topic language

in WITH
by Fredric Gey, Ray Larson, Mark S, Kerstin Bischoff, Thomas M, Christa Womser-hacker, Diana Santos, Paulo Rocha, Giorgio M. Di Nunzio, Nicola Ferro

Table 9. Bilingual experiments by topic language

in Xie: GeoCLEF 2007: the CLEF 2007 Cross-Language Geographic Infor¬ma¬tion Retrie¬val Track Overview
by Thomas M, Fredric Gey, Giorgio Di Nunzio, Nicola Ferro, Ray Larson, Mark S, Diana Santos, Christa Womser-hacker, Xing Xie

Table 4. Bilingual experiments by topic language

in WITH
by Fredric Gey, Ray Larson, Mark S, Kerstin Bischoff, Thomas M, Christa Womser-hacker, Diana Santos, Paulo Rocha, Giorgio M. Di Nunzio, Nicola Ferro

Table 9. Bilingual experiments by topic language

in Xie: GeoCLEF 2007: the CLEF 2007 Cross-Language Geographic Infor¬ma¬tion Retrie¬val Track Overview
by Thomas M, Fredric Gey, Giorgio Di Nunzio, Nicola Ferro, Ray Larson, Mark S, Diana Santos, Christa Womser-hacker, Xing Xie

Table 7: Number of words per keyphrase for the Journal Article corpus.

in Extraction of Keyphrases from Text: Evaluation of Four Algorithms
by Peter Turney, Council Canada 1997
"... In PAGE 15: ... Our experiments indicate that the chemistry journal is particularly challenging for automatic keyphrase extraction. In Table7 , we show the distribution of words per keyphrase for the 75 journal articles. Most authors use one or two words in a keyphrase.... ..."
Cited by 25

Table 7: Number of words per keyphrase for the Journal Article corpus.

in Extraction of Keyphrases from Text: Evaluation of Four Algorithms
by Peter Turney
"... In PAGE 15: ... Our experiments indicate that the chemistry journal is particularly challenging for automatic keyphrase extraction. In Table7 , we show the distribution of words per keyphrase for the 75 journal articles. Most authors use one or two words in a keyphrase.... ..."
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