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Table 8: Sample Results of Noun Guessing for Classes 7 and 8
Class 7 cands. Class 8 cands. N? Equivalent(s)
selo dilo + thing, things
setšhaba ditšhaba + nation, nations
sello dillo + (out)cry, outcries
sepetše *dipetše — walked
sekelela dikelela — recommend, disappear
The checking tool is robust towards inexistent forms (cf. *dipetše) and towards
forms that are not nominal (due to the context constraint on agreement-bearing
items, (cf. sekelela versus dikelela).
A first qualitative evaluation of the noun guessing routines on all candidates from
the 43,000 word corpus sample seems to suggest that the tool only fails on lexicalized
irregular forms (e.g. mong - beng, ‘owner(s)’, instead of the hypothetical mong -
*bang), and on nouns that, mostly due to semantic reasons, do not have both a singular
and a plural form (such as Sepedi ‘Pedi language and culture’, or leboa ‘North’). As
for the verb guesser, the noun guesser can be and has to be applied (for quantitative
reasons) to any new corpus to be annotated.
3.6 Rules for the Disambiguation of Closed Class Items
Given the high degree of ambiguity in closed class items (see section 2.3), there is
a major need for disambiguation strategies for these items. Even though a statistical
tagger is designed for this type of disambiguation, a rule-based preprocessing, leading
at least to a partial reduction of ambiguity, seems necessary.
We use context-based disambiguation rules, in the spirit of Gross and Silberztein’s
local grammars (Silberztein 1993) and of rule-based tagging. As with the noun guessing
queries, disambiguation rules are implemented as queries in the format of the CQP
language. Some extraction rules exclusively rely on lexical contexts (cf. the topmost
part of Table 9), while others involve lexemes and word class tagged items (middle
row), or a combination of lexical, categorical and morphological constraints (including,
for example, the presence of certain affixes [cf. lower part of Table 9]). The examples
in Table 9 all relate to the disambiguation of the form a, the most frequent and most
ambiguous item in our sample (cf. Table 5).
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