Enriching Knowledge Bases with Counting Quantifiers
Table 4: Performance of CINEX-CRF in consolidating counting quantifier mentions wrt. precision (P), coverage (Cov) and MAE. Numt. stands for number-related terms, Art. for articles. Results per type show contribution (Contr) to overall output and precision of individual types.
['Relation', 'Baseline\xa0', 'Baseline\xa0', 'Baseline\xa0', 'CINEX-CRF', 'CINEX-CRF', 'CINEX-CRF', 'CINEX-CRF (per type) Cardinals', 'CINEX-CRF (per type) Cardinals', 'CINEX-CRF (per type) Numt.+Art.', 'CINEX-CRF (per type) Numt.+Art.', 'CINEX-CRF (per type) Ordinals', 'CINEX-CRF (per type) Ordinals']
We report the results broken down per mention type, as well as in overall. Number-related terms and articles are beneficial in improving coverage particularly for containsWork and hasMember, yet produce low precision results for hasChild, possibly due to spurious indefinite articles frequently identified as counting quantifiers. Overall, taking compositionality as well as mention types other than cardinals into account improve both accuracy and coverage of the system, with MAE of not more than 2.6 across relations. We obtain a high accuracy of 92.3% for hasChild and 71.9% for hasSpouse. A main reason is that ordinals only reliably express lower bounds (see e.g. fourth incorrect example). If one considers ordinals as correct whenever they are not higher than the true count, the reported precision scores increase from 14.3-63.2% to 85.7-89.5%.