Optimizing disambiguation in Swahili  
 
Arvi HURSKAINEN 
Institute for Asian and African Studies  
Box 59 
FI-00014 University of Helsinki 
Arvi.Hurskainen@helsinki.fi 
 
Abstract 
It is argued in this paper that an optimal 
solution to disambiguation is a combination of 
linguistically motivated rules and resolution 
based on probability or heuristic rules. By 
disambiguation is here meant ambiguity 
resolution on all levels of language analysis, 
including morphology and semantics. The 
discussion is based on Swahili, for which a 
comprehensive analysis system has been 
developed by using two-level description in 
morphology and constraint grammar 
formalism in disambiguation. Particular 
attention is paid to optimising the use of 
different solutions for achieving maximal 
precision with minimal rule writing. 
 
1 Introduction 
In ambiguity resolution of natural language, 
both explicit linguistic information and 
probability calculation have been used as basic 
approaches. In early experiments usually only 
one strategy was applied, so that ambiguity 
resolution was performed either with the help of 
linguistic rules, or through probability 
calculation. Advanced approaches make use of 
both strategies, and they differ mainly in what 
kind of role each of these two methods has in the 
system (Wilks and Stevenson 1998, Stevenson 
and Wilks 2001). Sources of structured data, such 
as the WordNet (Miller 1990; Resnik 1998b; 
Banerjee and Pedersen 2002), have also been 
made use of.  
It is commonly known that the more 
comprehensive the description of language is, the 
more ambiguous is the interpretation of 
individual words
1
. Ambiguity occurs between 
word classes, between variously inflected word-
forms, and above all, between various meanings 
of a word. A fairly large number
2
 of words in 
different word categories have more than one 
clearly distinguished meaning. Semantic 
disambiguation tends to be the hardest part of the 
disambiguation process, largely because of the 
fact that in semantics there are few 
distinguishable categories that could be used for 
generalising disambiguation rules. Below I shall 
describe a method where the use of linguistic 
rules and probability has been optimised with 
minimal loss of linguistic precision.  
Morphological description is carried out in the 
framework of two-level formalism
3
. After having 
been under development for 19 years 
(Hurskainen 1992, 1996), the parser of Swahili 
has now reached a phase where the recall as well 
as the precision
4
 is close to 100% in unrestricted 
standard Swahili text.  
                                                      
1
 By word is here meant any string of characters, 
excluding punctuation marks and diacritics. Also, 
multi-word concepts, if they are handled as single 
entities, are considered words. 
2
 I do not consider it meaningful to present 
statistical details of ambiguity, because, when 
semantic glosses are included, the borderline between 
real ambiguity and such ambiguity as is found 
between synonyms and near-synonyms is vague.  
3
 The development environment for designing the 
morphological parser was provided by Lingsoft and 
Kimmo Koskenniemi (1983). 
4
 The criterion of precision in morphological 
analysis is considered fulfilled if one of the readings 
of a word is correct in the context concerned, and all 
other readings are grammatically correct analyses in 
some other context. 
Disambiguation rules, as well as the rules for 
syntactic mapping (not discussed here) and for 
identifying idioms, were written within the 
framework of constraint grammar by using the 
CG-2 parser
5
. In other words, morphological 
disambiguation and semantic disambiguation 
were implemented within a single rule system. 
This was possible because the CG-2 parser treats 
all strings in the analysis result, including glosses 
in English, as tags that can be made use of in rule 
writing (Tapanainen 1996: 6).
6
 
The properties of the CG-2 parser include the 
following:  
(a) With a rule one may either select or remove 
a reading from a cohort
7
.  
(b) The application of a rule can be constrained 
in several ways by making use of the occurrence 
or absence of features. Reference to the position 
of the constraining feature can be precisely made 
forwards and backwards within the sentence. 
(c) The identification of constraining features 
can be made relational by more than one phase of 
scanning, whereby after finding one feature, 
scanning may be continued again in either 
direction. By default, scanning terminates at a 
sentence boundary, but its termination can also 
be defined elsewhere. 
(d) Rule conditions can be expressed either 
directly with concrete tags or indirectly by using 
set names. The latter facility simplifies rule 
writing, especially of general rules. 
(e) The possibility of concatenating tag sets as 
well as concrete tags decreases considerably the 
need of defining tag sets. 
(f) The application of rule order can be defined 
by placing the rules into sections, so that the 
more general and reliable rules come first and 
other rules later in the order of decreasing 
reliability. This also makes it possible to write 
heuristic rules within the same rule system. 
                                                      
5
 The environment for writing and testing 
disambiguation rules was provided by Connexor and 
Pasi Tapanainen (1996).  
6
 In disambiguation, the precision criterion is 
considered fulfilled if the reading chosen in that 
context is correct. In two independent tests with recent 
news texts of 5,000 words each, the precision was 
99.8% and 99.9%. 
7
 A cohort is a word-form plus all its morphological 
interpretations. 
(g) Mapping rules, which are the standard rules 
for syntactic mapping, also include a possibility 
of adding a new reading as well as of replacing 
the reading of a line. The latter facility is 
demonstrated below when discussing idioms. 
2 Maximal morphological and semantic 
description as precondition 
The basic strategy in processing is that the 
morphological description is as full and detailed 
as possible. Each string in text is interpreted and 
all possible interpretations of each string are 
made explicit. The maximal recall and precision 
are achieved by updating the dictionary from 
time to time with the help of the changing target 
language
8
. As a result of analysis there is a text 
where every string has at least one interpretation 
and no legitimate interpretation is excluded. 
Example (1) illustrates the point.  
(1) 
Kiboko 
"kiboko"  N 7/8-SG  { fat person } HUM  
"kiboko"  N 7/8-SG  { whip , strip of hippo hide }  
 "kiboko"  N 7/8-SG  { hippo , hippopotamus } AN  
 "kiboko"  N 7/8-SG   
{ beautiful/attractive/outstanding thing }  
"kiboko"  N 7/8-SG  { ornamental stitch }   
 "boko"  ADV ADV:ki  9/10-SG  { gourd for 
 drinking water or local brew }  
 "boko"  ADV ADV:ki  9/10-PL  { gourd for 
 drinking water or local brew }  
aishiye 
 "ishi"  V 1/2-SG3-SP VFIN { live , reside , stay } 
 SV AR GEN-REL 1/2-SG  
kwenye 
 "kwenye" PREP { in , at } 
 "enye" PRON 15-SG { which has } 
 "enye" PRON 17-SG { place which has } 
maziwa 
                                                      
8
 By target language I mean the kind of text, for 
which the application is intended. It is hardly possible 
to maintain a dictionary that is optimal for handling all 
types of domain-specific texts. Although the large size 
of the dictionary would not be a problem, it would be 
difficult to handle e.g. such words that in one type of 
text are individual lexemes but in another domain are 
part of multi-word concepts that should be treated as 
one unit. In addition to new words, misspellings also 
cause problems. Some commonly occurring 
misspellings and non-standard spellings can be 
encoded into the dictionary and thus give the word a 
precise interpretation. 
 "ziwa"  N 5a/6-PL  { lake }   
 "ziwa"  N 5a/6-PL  { breast }   
 "maziwa" N 6-PL  { milk }  
amekula 
 "la"  V 1/2-SG3-SP VFIN PERF:me 1/2-SG2-OBJ 
 OBJ { eat } SV SVO MONOSLB  
 "la"  V 1/2-SG3-SP VFIN PERF:me 15-SG-OBJ 
 OBJ { eat } SV SVO MONOSLB  
 "la"  V 1/2-SG3-SP VFIN PERF:me 17-SG-OBJ 
 OBJ { eat } SV SVO MONOSLB  
 "la"  V 1/2-SG3-SP VFIN PERF:me INFMARK  
{ eat } SV SVO MONOSLB  
nyanya 
 "nyanya"  N 5a/6-SG  { tomato }  
 "nyanya"  N 9/10-SG  { tomato }  
 "nyanya"  N 9/10-SG  { grandmother } HUM  
 "nyanya"  N 9/10-PL  { tomato }  
 "nyanya"  N 9/10-PL  { grandmother } HUM  
 "nyanya"  N 9/6-SG  { grandmother }  HUM  
.$
Without disambiguation, the following 
interpretations are possible: 
(a) A fat person, who lives in lakes, has eaten 
tomatoes. (b) A fat person, who lives in lakes, 
has eaten grandmothers. (c) A fat person, who 
lives in breasts, has eaten tomatoes. (d) A fat 
person, who lives in breasts, has eaten 
grandmothers. (e) A fat person, who lives in 
milk, has eaten tomatoes. (f) A fat person, who 
lives in milk, has eaten grandmothers. (g) A 
hippo, which lives in lakes, has eaten tomatoes. 
(h) A hippo, which lives in lakes, has eaten 
grandmothers. (i) A hippo, which lives in breasts, 
has eaten tomatoes. (j) A hippo, which lives in 
breasts, has eaten grandmothers. (k) A hippo, 
which lives in milk, has eaten tomatoes. (l) A 
hippo, which lives in milk, has eaten 
grandmothers. 
The situation would be even worse if "aishiye" 
with relative marker (GEN-REL 1/2-SG) were 
missing. It requires that the preceding referent be 
animate and thus excludes inanimate alternatives. 
The subject prefix in the main verb "amekula" 
also refers to an animate subject. But because it 
can also stand without an overt subject, this clue 
is not reliable. 
When we look for the possible subject in the 
sentence, we seem to have three candidates. 
"Kiboko" certainly is one of them, because it is a 
noun and some of its readings agree
9
 with the 
                                                      
9
 In this case agreement means something other 
than morphological agreement. The noun belongs to 
subject prefix of the main verb. In regard to its 
position, "ziwani" would also suit, but it is ruled 
out because it has a locative suffix. Finally, no 
overt subject would be necessary, whereby the 
phrase preceding the main verb would be an 
object dislocated to the left and the sentence 
would mean, "The grandmother has eaten the 
hippo/fat person who lives in the 
lakes/breasts/milk".  
3 Disambiguation with linguistic rules 
From the analysed sentence we can see that 
part of the ambiguity is easy to resolve with 
rules. For example, "kiboko" cannot be an 
adverbial form (ADV:ki) of "boko" (= in the 
manner of a gourd), because it is the referent of 
the following relative verb "aishiye", which for 
its part requires that the referent has to be 
animate. Therefore, the interpretation "whip" and 
more rare meanings, "beautiful thing" and 
"ornamental stitch", are also ruled out. So we are 
left with two animate meanings, "fat person" and 
"hippo", for which there are no reliable tags 
available for writing disambiguation rules. 
One of the three interpretations of  "kwenye" 
can be removed (15-SG), because no infinitive 
precedes it. The word "maziwa" with three 
interpretations has no grammatical criteria for 
disambiguation.  
The interpretations with object marker (OBJ) 
of "amekula" (has eaten you) can be removed on 
the basis of the following noun (without 
locative), which is reliably the real object. 
For "nyanya" there are no reliable criteria for 
disambiguation. Because it is in object position 
and without qualifications, no clues for 
disambiguation can be found among agreement 
markers.  
After applying linguistic disambiguation 
rules
10
, we have an analysis as in (2). 
 
(2) 
Kiboko 
 "kiboko"  N 7/8-SG  { fat person } HUM  
 "kiboko"  N 7/8-SG  { hippo , hippopotamus }  
                                                                                 
Class 7 (7/8-SG) and the subject prefix of the verb to 
Class 1 (1/2-SG3-SP), but the semantic principle, i.e. 
animacy, overrides the formal criterion. 
10
 Because of space restrictions, those rules are not 
reproduced here. 
AN AR 
aishiye 
 "ishi"  V 1/2-SG3-SP VFIN { live ,  reside , stay } 
 SV GEN-REL 1/2-SG  
kwenye 
 "kwenye" PREP { in , at } 
 "enye" PRON 17-SG { place which has } 
maziwa 
 "ziwa"  N 5a/6-PL  { lake }   
 "ziwa"  N 5a/6-PL  { breast }   
 "maziwa" N 6-PL  { milk }  
amekula 
 "la"  V 1/2-SG3-SP VFIN PERF:me INFMARK  
{ eat } SV SVO MONOSLB  
nyanya 
 "nyanya"  N 5a/6-SG  { tomato }  
 "nyanya"  N 9/10-SG  { tomato }  
 "nyanya"  N 9/10-SG  { grandmother } HUM  
 "nyanya"  N 9/10-PL  { tomato }  
 "nyanya"  N 9/10-PL  { grandmother } HUM  
 "nyanya"  N 9/6-SG  { grandmother }  HUM  
.$ 
4 Disambiguation with context-sensitive 
semantic rules 
Now follows the hard part of disambiguation, 
because no reliable linguistic rules can be 
written. The easiest case is "kwenye", because 
the two interpretations represent different phases 
of the grammaticalization process, and the 
semantic difference between them is marginal. 
The preposition "kwenye" is in fact formally a 
locative (17-SG) form of the relative word "enye" 
(which has). 
For "Kiboko" we can make use of the common 
knowledge that fat persons do not normally live 
in lakes, or in breasts, or in milk. Therefore, a 
rule based on the co-occurrence of "kiboko" and 
"maziwa"
11
 with appropriate meanings can be 
written. 
The word "maziwa" is even more difficult to 
disambiguate. The word "kiboko" in the sense of 
hippo can easily co-occur with all three meanings 
of "maziwa". Here we have to rely on 
probability
12
.  
                                                      
11
 A set of words referring to places where a hippo 
resides can be defined and used in the rule. 
12
 It is possible to write also a context-sensitive 
rule, where use is made of the fact that rhinos can live 
in lakes but not in breasts or milk, but such a rule 
easily becomes too specific. 
The word "nyanya" in object position is almost 
impossible to disambiguate elegantly. The 
subject of eating can be one or more tomatoes, as 
well as one or more grandmothers. It is not rare 
at all that hippos devour people, although there is 
no proof that they would be particularly fond of 
grandmothers. Nobody has heard fat men eating 
grandmothers, but those do not come into 
question in any case, because they do not live in 
lakes. 
If we assume that hippos hardly eat 
grandmothers we can remove the reading, which 
has the tag "grandmother". We are still left with 
singular and plural alternatives of tomato. Here 
plural would be more natural, because tomatoes 
are here treated as a mass rather than as 
individual fruits. 
 When context-sensitive semantic rules and 
heuristic rules are applied, the reading is as 
shown in (3). 
(3) 
Kiboko 
 "kiboko"  N 7/8-SG  { hippo , hippopotamus } AN  
aishiye 
 "ishi"  V 1/2-SG3-SP VFIN { live , reside , stay } 
 SV GEN-REL 1/2-SG  
kwenye 
 "kwenye" PREP { in , at } 
maziwa 
 "ziwa"  N 5a/6-PL  { lake }   
amekula 
 "la"  V 1/2-SG3-SP VFIN PERF:me INFMARK  
{ eat } SV SVO MONOSLB  
nyanya 
 "nyanya"  N 9/10-PL  { tomato }  
.$ 
5 Problem of semantic generalisation 
Although the possibilities for generalisation in 
semantics are limited, in noun class languages 
relevant semantic clusters can be found. Even 
though classes in Swahili are only in exceptional 
cases semantically 'pure', the class membership 
often provides sufficient information for 
disambiguation, either by direct selection or, 
more often, by exclusion of a reading.  
The grades of animacy  (e.g. human, animal, 
vegetation) are an example of useful semantic 
groupings, which can be used in generalising 
disambiguation. Another useful feature, actually 
belonging to syntax, is the division of verbs into 
categories according to their argument structure 
(e.g. SV, SVO, SVOO) 
Neural networks have been used successfully 
for identifying clusters of co-occurrence of words 
and their accompanying tags (Veronis and Ide 
1990; Sussna 1993; Resnik 1998a). Research 
results, carried out with the Self-Organizing Map 
(Kohonen 1995) on semantic clustering of verbs 
and their arguments in Swahili, are very 
promising, and useful generalizations have been 
found (Ng'ang'a 2003).
13
 These findings can be 
encoded into the morphological parser and used 
in writing semantic disambiguation rules. 
6 When means for rule writing fail 
It sometimes happens that linguistic 
disambiguation rules cannot be written. 
Particularly problematic is the noun of the Class 
9/10 in object position without qualifiers, many 
of which would help in disambiguation. In this 
noun class there are no features in nouns for 
determining whether the word is in singular or 
plural
14
. The detailed survey of about 11,000 
occurrences of class 9/10 nouns in object position 
shows, however, that 97% of them are 
unambiguously in singular. Among the remaining 
3%, 2% can be either in singular or plural, and 
only one percent are such cases where the noun is 
clearly in plural. These 2% are typically count 
nouns, which sometimes can be disambiguated, 
if, for example, they are members in a list of 
nouns. Nouns in such lists tend to be either in 
singular or in plural, and often at least one list 
member belongs to one of the other noun classes, 
where singular and plural are distinguished.  
The solution for the nouns of the class 9/10 in 
object position is, therefore, that for the rare 
plural cases, disambiguation rules are written, 
while singular is the default interpretation. 
                                                      
13
 The likelihood of co-occurrence can be 
established between word pairs, or clusters, and also 
between words and tags attached to them. Therefore, 
the full range of information in an analysed corpus can 
be utilized in establishing relationships. 
14
 Singular and plural are identical in this class, and 
it is the biggest class of the language, consisting of 
about 39% of all nouns. 
7 Treatment of multi-word concepts 
and idioms 
In computational description of a language, 
multi-word concepts and idioms can be treated as 
one unit, because in both cases the meaning is 
based on more than one string in text. If a multi-
word concept consists of a collocation or noun 
phrase, it can be encoded in the tokenizer (4) and 
the morphological lexicon (5). Such 
constructions have two forms (SG and PL) at the 
most. 
(4) bwana  shamba > bwana_shamba  
jumba la makumbusho > jumba_la_makumbusho 
majumba ya makumbusho > 
majumba_ya_makumbusho 
(5) bwana_shamba 
 "bwana_shamba" N 9/6-SG { agricultural adviser} 
 HUM 
jumba_la_makumbusho 
 "jumba_la_makumbusho" N 5/6-SG { museum } 
majumba_ya_makumbusho 
 "majumba_ya_makumbusho" N 5/6-PL  
{ museums} 
 
If the concept has a non-finite verb as part of 
the construction, as is often the case in idioms, 
the constructions cannot be handled on the 
surface level. It is possible to handle them with 
disambiguation rules. Example (6), which is an 
idiom, shows how each of its constituent parts is 
interpreted in isolation. 
(6) 
alipiga  
 "piga"  V 1/2-SG3-SP VFIN PAST  { hit , beat } 
 SVO   
konde  
 "konde"  N 5/6-SG { cultivated land , fist} 
la  
 "la"  GEN-CON 5/6-SG { of } 
nyuma  
 "nyuma"  ADV { behind } 
With the help of disambiguation rules, the 
idiom can be identified, although the verb "piga" 
may have several surface forms, including 
extended forms. The solution adopted here is the 
following:  
As a first step we identify the constituent parts 
of the idiom and describe its structure by a tag, as 
is shown in (7). The angle brackets (<>>) show 
that the idiom contains the current word as well 
as the preceding word and two following words. 
Also the meaning of the idiom ("to bribe") is 
attached to this word.  
(7) 
alipiga  
 "piga"  V 1/2-SG3-SP VFIN PAST { hit , beat } 
 SVO   
konde  
 "konde" <>>IDIOM { to bribe } 
la  
 "la"  GEN-CON 5/6-SG { of } 
nyuma  
 "nyuma"  ADV { behind } 
Then we mark each of the other constituent 
parts of the idiom and show their relative location 
in the structure by using angle brackets, as shown 
in (8). For example, "nyuma" is the last 
constituent and all three words before it are part 
of the idiom. Original glosses of other constituent 
parts are removed. The verb retains its 
morphological tags, and a special tag (IDIOM-V) 
is added to show that it is part of the idiom. 
(8) 
alipiga  
 "piga"  V 1/2-SG3-SP VFIN PAST SVO  IDIOM-V 
konde  
 "konde" <>>IDIOM { to bribe } 
la  
 "la" IDIOM<<> 
nyuma  
 "nyuma" IDIOM<<< 
8 Making use of default interpretation 
Although it would be possible to write 
disambiguation rules for practically all such cases 
where sufficient features for rule writing are 
available, it is sometimes impractical, especially 
in selecting the right semantic interpretation. This 
can be implemented in more than one way, for 
example by constructing the morphological 
analyser so that the alternative semantic analyses 
are in frequency order (9). 
(9) 
taa 
 "taa"  N 9/10-SG  { lamp , lantern } AR  
 "taa"  N 9/10-SG  { discipline , obedience }  
 "taa"  N 9/10-SG  { large flat fish , skate } AN  
 "taa"  N 9/10-PL  { lamp , lantern } AR  
 "taa"  N 9/10-PL  { discipline , obedience }  
 "taa"  N 9/10-PL  { large flat fish , skate } AN  
The word "taa" gets three semantic 
interpretations, each in singular and plural. The 
most obvious gloss (lamp, lantern) is the first in 
order, and if no rule has chosen any of the other 
alternatives, this one is chosen as the default 
case. The choice of other alternatives is 
controlled by rules as far as possible. For 
example, the animate reading can often be chosen 
with congruence rules. 
9 Discussion 
The disambiguation of a language is a process 
where the cooperation of linguistic rules and 
probability should be optimised. It was shown 
above briefly that different disambiguation 
operations should be cascaded so that the most 
reliable disambiguation is carried out first and the 
least reliable cases last. Multi-word concepts can 
be handled so that such constructions that do not 
have inflecting constituent parts are treated as 
part of morphology, and those with inflecting 
parts, especially idioms, are handled with 
disambiguation rules. We have also seen that 
linguistic rules should precede rules based on 
probability. It is also possible to simplify the 
writing of semantic rules by constructing the 
morphological parser so that semantic readings 
come in order of frequency, whereby the most 
frequent interpretation is considered a default 
case, and only other interpretations need rules. 
The experiments with the SOM algorithm 
indicate that it is possible to find significant 
relationships between adjacent words on the one 
hand and between words and tags on the other. 
Such information can then be encoded in the 
morphological dictionary and used in 
generalising disambiguation rules. Ambiguity 
resolution can be enhanced further by 
constructing explicit dependencies between 
constituent parts of a sentence  (Järvinen and 
Tapanainen 1997; Tapanainen and Järvinen 
1997; Tapanainen 1999) or by making use of a 
parse tree bank of the type of WordNet (Hirst and 
Onge 1998). 
10 Acknowledgements 
Thanks go to Lingsoft and Kimmo 
Koskenniemi for allowing me to use the Two-
Level Compiler for handling morphological 
analysis and to Connexor and Pasi Tapanainen 
for prividing access to CG-2 for writing 
disambiguation rules. 

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