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Table 1. The most frequently occurring species in a set of 36082 cytogenetic MEDLINE abstracts mentioning cytogenetic bands

in
by Steven Van Vooren, Bernard Thienpont, Björn Menten, Frank Speleman, Bart De Moor, Joris Vermeesch, Yves Moreau 2006
"... In PAGE 3: ... The most frequently occurring species are shown in Table 1. Note that the results from Table1 do not imply that 14 865 documents discuss human cases and 3664 docu- ments discuss mouse: on the one hand, the term human does not necessarily occur in all abstracts on human. On the other hand, the terms human and mouse can co-occur, since some abstracts discuss patients as well as model organisms.... ..."

Table 3. Results of large-scale application of RelEx on a comprehensive set of MEDLINE abstracts (C241 million abstracts) and comparison

in RelEx–Relation extraction using dependency parse trees
by Katrin Fundel, Robert Küffner, Ralf Zimmer, Satoru Miyano 2007
"... In PAGE 6: ... These relations can be compared against HPRD, which contains interactions that were manually extracted from MEDLINE full-text articles. The comparison provides information with respect to dif- ferences and overlaps of the two approaches ( Table3 ). A large fraction of the HPRD interactions cannot be retrieved from the abstracts.... In PAGE 6: ...2.1 Comparing RelEx relations with HPRD interactions The hprd50 dataset allows us to estimate the performance based on the abstracts referenced by HPRD ( Table3 ) and thus to examine the differences between RelEx relations and HPRD interactions. The performance on this data set is slightly lower than on the LLL-challenge dataset.... ..."
Cited by 1

Table 1: Marks and delimiters used in cocab. Marks include transcription of Greek alphabets that often appear in MEDLINE abstracts. Delimiter Mark

in Protein Name Tagging for Biomedical Annotation in Text
by Kaoru Yamamoto, Taku Kudo, Akihiko Konagaya, Yuji Matsumoto 2003
Cited by 5

Table 1: Marks and delimiters used in cocab. Marks include transcription of Greek alphabets that often appear in MEDLINE abstracts. Delimiter Mark

in Protein Name Tagging for Biomedical Annotation in Text
by Kaoru Yamamoto Taku, Kaoru Yamamoto, Taku Kudo, Akihiko Konagaya, Yuji Matsumoto 2003
Cited by 5

Table 1: Marks and delimiters used in cocab. Marks include transcription of Greek alphabets that often appear in MEDLINE abstracts. Delimiter Mark

in Protein Name Tagging for Biomedical Annotation in Text
by Kaoru Yamamotoy , Taku Kudo, Akihiko Konagaya, Yuji Matsumoto

Table 3. Recall, precision and F-measure INSPEC MEDLINE CAB Abstracts

in Address for correspondence:
by Carmen Galvez, Carmen Galvez, Félix Moya-anegón 2006

Table 3: Sample relations extracted from a search for dyslexia. These relations are extracted from glossary de nitions for dyslexia and search results from MEDLINE abstracts

in Towards Ontologies On Demand
by Youngja Park Roy, Roy J. Byrd, Branimir K. Boguraev
"... In PAGE 4: ... We nd the dependency relations in which the tar- get terms or the selected glossary items appear and generate relations named with the verbs. Table 2 and Table3 show examples of the selected glos- sary items and some relations respectively. These exam- ples are extracted from glossary de nitions and search results from MEDLINE abstracts database for dyslexia.... ..."

Table 4: Relations between text- and data-based scores. The correlation coe cients, the Spearman rank correlations, and the normalized trace of the text-rank{data-rank matrices are reported for the respective settings (free text, optionally expanded with dictionary entries or MEDLINE abstracts, set size is 1).

in On the potential of domain literature for clustering and Bayesian network learning
by Peter Antal, Patrick Glenisson, Geert Fannes 2002
"... In PAGE 7: ... We report the normalized trace of R0, that is the correspondence between the text and data-based ranking using this 4-graded granularity for all the variables and only for Pathology also. Table4 presents the results for the most interesting set- tings while Table 5 contains a more structured and detailed reports for a larger number of settings. Table 4: Relations between text- and data-based scores.... ..."
Cited by 2

Table 4: Relations between text- and data-based scores. The correlation coe cients, the Spearman rank correlations, and the normalized trace of the text-rank{data-rank matrices are reported for the respective settings (free text, optionally expanded with dictionary entries or MEDLINE abstracts, set size is 1).

in On the Potential of Domain Literature
by For Clustering And, Peter Antal 2002
"... In PAGE 7: ... We report the normalized trace of R0, that is the correspondence between the text and data-based ranking using this 4-graded granularity for all the variables and only for Pathology also. Table4 presents the results for the most interesting set- tings while Table 5 contains a more structured and detailed reports for a larger number of settings. Table 4: Relations between text- and data-based scores.... ..."
Cited by 2

Table 1. Queries and the recall, precision, and effectiveness for each, given abstracts (Ab), sentences (Se), and phrases (Ph) as text units from which to extract interactions between the query terms or their synonyms, in MEDLINE abstracts containing both query terms. (The last query, an outlier, is discussed further in Appendix A.) Recall Precision Effectiveness Query terms

in Pacific Symposium on Biocomputing 7:326-337 (2002). MINING MEDLINE: ABSTRACTS, SENTENCES, OR PHRASES?
by J. Ding A, D. Berleant A, D. Nettleton B, E. Wurtele C
"... In PAGE 3: ...3 from MEDLINE using ten queries ( Table1 ) to its PUBMED interface.8 Each query was the AND of two biochemical nouns.... In PAGE 6: ... Information retrieval measures for different types of text units. Recall and precision figures are means over the relevant figures for each query (shown in Table1 for all text unit types except sentence pairs). Each figure was appropriately weighted, by the number of abstracts in the set associated with that query (in the case of precision of abstracts), the number of co-occurrences for that query within the text unit under consideration (in the case of precision of sentence pairs, sentences, and phrases), or by the number of interactions described for that query within the associated set of abstracts (for recall).... In PAGE 8: ... Effectiveness of sophisticated text processing techniques is higher than the baseline figures in Table 2 above for both the sentence and phrase text units. For phrases, sophisticated techniques led to an effectiveness higher than that of any entry in Table1 above. (However comparisons across reports should be interpreted with caution.... In PAGE 9: ...9 Appendix A: An Outlier Query It is interesting to consider an outlier from among our ten queries. For the query cholesterol AND flavonoid, smaller text units fared more poorly than for other queries ( Table1 ). Closer inspection of these abstracts showed that flavonoid is a large family of chemicals, and the name of a specific flavonoid is usually stated in the first sentence of an abstract.... ..."
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