"... In PAGE 27: ...8. J. Zayer, Faktorisieren mit dem Number Field Sieve, Ph. D. thesis, Universitat des Saarlandes, 1995. Appendix: { RSA140 and RSA155 Table1 gives some statistics on the RSA140 and RSA155 factorisations. Table 1.... ..."

"... In PAGE 3: ... The aim is that the formal speci cations secure sound interfaces to process engineers, control engineers as well as safety analysts. 3 Partners and Activities Table1 gives a list of ProCoS-WG partners with the name of the main contact person(s) at each site. ProCoS II project sites (Oxford University,Technical University of Denmark, Universitat Oldenburg and Universitat Kiel) have each been allocated special responsibilityforanumber of ProCoS-WG sites mainly allocated by geographical proximity where possible.... ..."

"... In PAGE 3: ... The aim is that the formal speci cations secure sound interfaces to process engineers, control engineers as well as safety analysts. 3 Partners and Activities Table1 gives a list of ProCoS-WG partners with the name of the main contact person(s) at each site. ProCoS II project sites (Oxford University, Technical University of Denmark, Universitat Oldenburg and Universitat Kiel) have each been allocated special responsibility for a number of ProCoS-WG sites mainly allocated by geographical proximity where possible.... ..."

"... In PAGE 13: ...n sections 3.3 and 3.2, we will report on their use to lter multiword term candidates according to their likely usefulness for a glossary. Table1 summarizes the steps and the pertaining tool components: 16Not much information is lost because of lemmatization: only in rare cases, forms distinguish term candidates from non-terminological material: technische Unterlagen (plural) may be considered as a term, whereas (standfeste, harte, weiche) Unterlage may not, or may be seen as a di erent term. Ulrich Heid, Stuttgart 13... ..."

"... In PAGE 18: ... 2005 Institut f ur Neuroinformatik, Ruhr-Universit at Bochum 17 determined by hf, see Table7 . The value ymax can be chosen between 1 and 1= maxj(jo1jj), and in the latter case the Pareto optimal solution y1 = ymax lies on the search space boundary.... ..."

"... In PAGE 4: ... To internationalize PEDE, a pilot project, named Virtual International Design (VID) was also developed in collaboration with the Universite de Provence and Hon Industries. The goals of this PEDE/VID project were to gain an understanding and appreciation of engineering design practices in other countries, to develop personal communication skills necessary to work on a team with students from another countries on a common design project, to understand and master the difficulties of communicating clearly and concisely through electronic media, and to establish an ongoing collaborative design program with universities throughout the world Table1 shows the titles of design projects, student affiliations, and names of industrial firms sponsoring projects in 1998-99. JDDW provided five design projects, Hon industries provided two, Monsanto provided two, ALCOA provided one, and Rockwell provided one.... In PAGE 10: ... The original surveys are given in Tables 4 and 5. The survey forms were sent to all twenty-seven students of seven mechanical engineering design projects 1, 3, 4, 5, 6, 8, and 10 (see Table1 ) that were supervised by the authors. For design projects 2, 9, and 11, other faculty from departments other than mechanical engineering also conducted surveys, but their formats were different from Tables 4 and 5.... ..."

"... In PAGE 14: ...200; this means there are 1654 people, 200 of whom are female. Data are provided for the top 50 departments in the following disciplines: Table 1 Chemistry, 2001 and 2003 Table 2 Physics Table 3 Mathematics Table 4 Computer Science Table 5 Chemical Engineering Table 6 Civil Engineering Table 7 Electrical Engineering Table 8 Mechanical Engineering Table 9 Economics Table 10 Political Science Table 11 Sociology Table 12 Psychology Table 13 Biological Sciences Table14... In PAGE 29: ...Table14 . Tenured/Tenure-Track Faculty at PhD-Granting Astronomy/Astrophysics Departments by Race/Ethnicity, by Gender, and b y Rank (FY 200 3 ) * Total Universit y Full Asso c Ass t Tot Full Asso c Ass t Tot Full Asso c Ass t Tot Full Asso c Ass t Tot Full A sso c Ass t Tot Cal Inst of Tec h 17.... In PAGE 30: ... Data are disaggregated by race/ethnicity and by gender. Data are provided for PhD attainment in the following disciplines: Table 1 Chemistry Table 2 Physics Table 3 Mathematics Table 4 Computer Science Table 5 Chemical Engineering Table 6 Civil Engineering Table 7 Electrical Engineering Table 8 Mechanical Engineering Table 9 Economics Table 10 Political Science Table 11 Sociology Table 12 Psychology Table 13 Biological Sciences Table14... ..."

"... In PAGE 22: ... D espite the fact that KIM prov ides more spec ific type informa tion, it is still possible to test it against the huma n annotated corp us (because s omething that is a Mountain is also a Location). In Table1 we p resent the Pre cision , Recall and F- Mea sure of the au tomatically annotated corpus versus t he human annotated one. These metrics are about the correctness of the KIM nam ed entity rec ognition pr ocess in ter ms of ge neral NE types, on t he flat le vel of abstraction in standard NER system s.... In PAGE 22: ... These metrics are about the correctness of the KIM nam ed entity rec ognition pr ocess in ter ms of ge neral NE types, on t he flat le vel of abstraction in standard NER system s. Table1 . Evaluatio n of KIM NER w rt general NE types.... In PAGE 73: ... Domains# glosses# words # disamb. words # of which okRecallPrecision Baseline Precision Tourism305134563659147,28%92,92%82,55% Generic10042117316641,09%95,95%67,05% Domainsnoun recall noun precision adj recall adj precision verb recall verb precision # tot nouns # tot adj # tot verbs Tourism64,52%92,86%28,72%89,29%9,18%77,78%868195294 Generic58,27%95,95%28,38%95,24%5,32%80%2547494 Table1 a) performance of the gloss disambiguation algorithm b) performance by morphological category. Table 1 gives an overview of the results.... In PAGE 73: ... words # of which okRecallPrecision Baseline Precision Tourism305134563659147,28%92,92%82,55% Generic10042117316641,09%95,95%67,05% Domainsnoun recall noun precision adj recall adj precision verb recall verb precision # tot nouns # tot adj # tot verbs Tourism64,52%92,86%28,72%89,29%9,18%77,78%868195294 Generic58,27%95,95%28,38%95,24%5,32%80%2547494 Table 1a) performance of the gloss disambiguation algorithm b) performance by morphological category. Table1 gives an overview of the results. Table 1a provides an overall evaluation of the algorithm, while table 1b computes precision and recall grouped by morphological category.... In PAGE 73: ... This means also that the disambiguation task is far more complex in the case of general glosses, where our algorithm shows particularly good performance. An analysis of performance by morphological category ( Table1 b) shows that noun disambiguation has much higher recall and precision. This is motivated by the fact that, in WordNet, noun definitions are richer than for verbs and adjectives.... In PAGE 88: ... 7 K now ledge Extraction Evaluation We u sed t he system to popu late the KB with information abou t five artists, extracted from arou nd 50 w eb pag es. Preci sion and recal l were cal culated for a s et of 10 artist relatio ns (listed in Table1 ). T he ex periment results g iven in Table 1 shows that precis ion scored h igher that recall w ith averag e values of 85 an d 42 res pectiv ely Workshop on Human Language Technology for the Semantic Web and Web Services... In PAGE 89: ...g. a date of marriag e is bef ore th e date of birth , or tw o unrelated places of birth for the same pers on! Table1 . Precision/R ecall of extracted relations from around 50 doc uments for 5 artists The pref eren ce of precis ion versus recall cou ld be depen dent on the relation in questio n.... In PAGE 89: ... On e possib le approach is to automatically adjust the risk lev el of extractio n rules w ith respect to car dinality, easin g the rules if car dinality is h igh while restricting them further when the cardinality is lo w. In Table1 , Goy a is an example w here few, short docu ments where found. The amount of knowledge extracted per artis t cou ld be used as an automatic tr igger to star t gathering a nd analyzing m ore documents.... In PAGE 130: ... The OntoGenie has succ essfully dis- covered Knowledge Instances from the Web. Table1 shows one of the RDF instance being disc overed for the Universit y Ontology. The excerpts says that Libr arian is an instance of the concept Person and is the member of an Orga- nization whose instance is Librar y.... In PAGE 130: ...NIVUR I = http://ww w.cs.umd. edu/pro jects/plus/DAML/on ts/cs1.0. daml Table1 . Experimental Result s 4 OntoGe nie Concl usi ons: Ge tting bac k into Lamp! We presented a simp le, practical and implem ented framew ork, OntoGenie that solves the high ly critical and imp ortan t problem of disc overin g Knowledge in- stance s from Web.... ..."