Anchoring Floating Quantifiers 
in Japanese-to-English Machine Translation 
Francis Bond,t Daniela Kurz* and Satoshi Shirai t 
t NTT Communication Science Laboratories 
2-4 Hikari-dai, Seika-cho, Soraku-gun, Kyoto, Japan, 619-0237 
{bond, shirai}©cslab, kecl. nit. co. jp 
Department of Computational Linguistics, University of the Saarland 
Postfach 1150, D-66041 Saarbrficken, Germany 
kurz©coli, uni-sb, de 
Abstract 
In this paper we present an algorithm to an- 
chor floating quantifiers in Japanese, a language 
in which quantificational nouns and numeral- 
classifier combinations can appear separated 
from the noun phrase they quantify. The algo- 
rithm differentiates degree and event modifiers 
from nouns that quantify noun phrases. It then 
finds a suitable anchor for such floating quan- 
tifiers. To do this, the algorithm considers the 
part of speech of the quantifier and the target, 
the semantic relation between them, the case 
marker of the antecedent and the meaning of the 
verb that governs the two constituents. The al- 
gorithm has been implemented and tested in a 
rule-based Japanese-to-English machine trans- 
lation system, with an accuracy of 76% and a 
recall of 97%. 
1 Introduction 
One interesting phenomenon in Japanese is the 
fact that quantifiers can appear in two main po- 
sitions, as pre-modifier in a noun phrase (1), or 
'floating' as adjuncts to the verb phrase, typi- 
cally in pre-verbal position (2). 1,2 
(1) watashi-wa 3-ko-no kgki-wo tabeta 
I-TOP 3-CL-ADN cake-ACC ate 
I ate three cakes 
(2) watashi-wa kgki-wo 3-ko tabeta 
I-TOP cake-ACC 3-CL ate 
I ate three cakes 
Quantifier 'float' of numeral-classifier combi- 
nations is widely discussed in the linguistic liter- 
1Quantifiers are shown in bold, the noun phrases 
they quantify are underlined. 
2This phenomenon exists in other languages, such as 
Korean. We will, however, restrict our discussion to 
Japanese in this paper. 
ature. 3 Much of the discussion focuses on iden- 
tifying the conditions under which a quantifier 
can appear in the adjunct position. The expla- 
nations range from configurational (Inoue, 1983; 
Miyagawa, 1989) to discourse based (Downing, 
1996; Alam, 1997), we shall discuss these fur- 
ther below. There has been almost no discus- 
sion of other floating quantifiers, such as quan- 
tificational nouns. 
We call the process of identifying the noun 
phrase being quantified by a floating quanti- 
fier 'anchoring' the quantifier. The necessity 
of anchoring floating quantifiers for many nat- 
ural language processing tasks is widely recog- 
nized (Asahioka et al., 1990; Bond et al., 1996), 
and is important not only for machine transla- 
tion but for the interpretation of Japanese in 
general. However, although there are several 
NLP systems that incorporate some solution to 
the problem of floating quantifiers, to the best 
of our knowledge, no algorithm for anchoring 
floating quantifiers has been given. We propose 
such an algorithm in this paper. The algorithm 
uses information about case-marking, sentence 
structure, part-of-speech, noun and verb mean- 
ing. The algorithm has been implemented and 
tested within the Japanese-to-English machine 
translation system ALT-J/E (Ikehara et al., 1991). 
The next section describes the phenomenon 
of quantifier float in more detail. We then pro- 
pose our algorithm to identify and anchor float- 
ing quantifiers in Section 3. The results of im- 
plementing the algorithm in ALT-J/E are dis- 
3The name 'float' comes from early transformational 
accounts, where the quantifier was said to 'float' out of 
the noun phrase. Although this analysis has largely been 
abandoned, and we disagree with it, we shall continue 
with accepted practice and call a quantifier in the ad- 
junct position a floating quantifier. 
152 
cussed in Section 4 and some remaining prob- 
lems identified. The conclusion summarises the 
implementation of the algorithm and highlights 
some of its strengths. 
2 Quantifier float in Japanese 
First we will give a definition of quantifiers. Se- 
mantically, quantifiers are elements that serve 
to quantify, or enumerate, some target. The tar- 
get can be an entity, in which case the number 
of objects is quantified, or an action, in which 
case the number of events (i.e. iterations of the 
action) are quantified. The quantification can 
be by a cardinal number, or by a more vague 
expression, like several or many. 
In Japanese, quantifiers (Q) axe mainly re- 
alised in two ways: numeral-classifier combi- 
nations (XC) and quantificational nouns (N). 
Note that these nouns are often treated as ad- 
verbs, as they typically function as adjuncts 
that modify verbs, a function prototypically car- 
ried out by adverbs. They can however head 
noun phrases, and take some case-markers, so 
we classify them as nouns. 
Numeral classifiers form a closed class, al- 
though a large one. Japanese and Korean both 
have two or three hundred numeral classifiers 
(not counting units), although typically indi- 
vidual speakers use far less, between 30 and 80 
(Downing, 1995, 346). 
Syntactically, numeral classifiers are a sub- 
class of nouns. The main property distinguish- 
ing them from prototypical nouns is that they 
cannot stand alone. Typically they postfix 
to numerals, forming a quantifier phrase, al- 
though they can also combine with the quan- 
tificational prefix s~ "some" or the interrogative 
nani "what": 
(3) 2-hiki "2 animals" (Numeral) 
(4) s~-hiki "some animals" (Quantifier) 
(5) nan-biki "how many animals" (Inter- 
rogative) 
Semantically, classifiers both classify and 
quantify the referent of the noun phrase they 
collocate with. 
Quantificational nouns, such as takusan 
"much/many", subete "all" and ichibu "some", 
only quantify their targets, there is no classifi- 
cation involved. 
Numeral classifier combinations appear in 
seven major patterns of use (following Asahioka 
et al. (1990)) as shown below (T refers to 
the quantified target noun phrase, m is a case- 
marker): 
Type Form XC N 
pre-nominal Q-no T-m + + 
appositive TQ-m + - 
floating T-m Q + + 
Q T-m 
partitive T-no Q-m + + 
attributive QT-m + - 
anaphoric T-m ÷ - 
predicative T-wa Q-da ÷ - 
Table 1: Types of quantifier constructions 
Noun quantifiers cannot appear in the ap- 
positive, attributive, anaphoric and predicative 
complement patterns. 
In the pre-nominal construction the relation 
between the target noun phrase and quantifier 
is explicit. For numeral-classifier combinations 
the quantification can be of the object denoted 
by the noun phrase itself as in (6); or of a sub- 
part of it as in (7) (see Bond and Paik (1997) for 
a fuller discussion). For nouns, only the object 
denoted by the noun itself can be quantified. 
(6) 3-ts~-no tegami 
3-CL-ADN letter 
3 letters 
(7) 3-rnai-no tegami 
3-CL-ADN letter 
a 3 page letter 
In the partitive construction the quantifier re- 
stricts a subset of a known amount: e.g., tegami- 
no 3-ts~ "three of the letters". This is a very 
different construal to the pre-nominal construc- 
tion. Only rational quantificational nouns can 
appear in the partitive construction. 
The floating construction, on the other hand, 
has the same quantificational meaning as the 
pre-nominal. Two studies indicate that there 
are pragmatic differences (Downing, 1996; Kim, 
1995). Pre-nominal constructions typically are 
used to introduce important referents, with non- 
existential predicates, while floating construc- 
tions typically introduce new number informa- 
tion. In addition floating constructions are used 
153 
when the nominal has other modifiers, and are 
more common in spoken text. 
We will restrict the following discussion to the 
difference between the pre-nominal and floating 
uses. 
2.1 Restrictions on quantifier float 
There have been many attempts to describe the 
situations under which the floating construction 
is possible, almost all of which only consider 
numeral-classifier constructions. 
The earliest generative approaches suggested 
that the target in the floating construction 
must be either subject or object. Inoue (1983) 
pointed out that quasi-objects, noun phrases 
marked with the accusative case-marker but 
failing other tests for objecthood, could also be 
targets. 
Miyagawa (1989) gives a comprehensive con- 
figurational explanation, where the target and 
quantifier must mutually c-command each other 
(that is, neither the target nor the quantifier 
dominates the other, and the first branching 
node that dominates either one, dominates the 
other). The restriction to nominative and 
accusative targets is explained by proposing a 
difference in structure. Verb arguments sub- 
categorized for in the lexicon are noun phrases, 
where the case-marker is a clitic and thus can be 
c-commanded, whereas adjuncts are headed by 
their markers, to form post-positional phrases 
which are thus not available as targets. 
The c-command relation is applied to both 
the noun phrases themselves and traces. Quan- 
tifiers can be scrambled (moved from their base 
position after their target) leaving a trace if the 
target is an affected Theme NP, and the target 
and quantifier are governed by the verb that 
assigns this thematic role. Thus quantifiers as- 
sociated with affected themes can move within 
the sentence. Affected themes are things that 
axe "changed, created, converted, extinguished, 
consumed, destroyed or gotten-rid of". 
Miyagawa (1989, 57) proposes a syntactic test 
for affectiveness: affected themes can occure in 
the intransitive resultative construction -te-aru. 
Alam (1997) looks at the problem from a dif- 
ferent angle, and proposes that only quantifiers 
which are interpreted "distributively or as a 
quantified event" can float, as they take wide 
scope beyond the NP. A quantified noun phrase 
will also quantify the event if the noun phrase 
measures-out the event, where "direct internal 
arguments undergoing change in the event de- 
scribed by the verb measure out the event" 
a very similar description to that of affected 
theme. However, Jackendoff (1996) has shown 
that a wide variety of arguments can measure 
out processes, not just subjects and objects, but 
also the complements of prepositional phrases. 
Which case-roles measure out the process can 
be pragmatically determined as well as lexically 
stipulated, so it is not a simple matter to deter- 
mine which arguments are relevent. 
The excellent distributional analysis of Down- 
ing (1996) shows that actual cases of float- 
ing tend to be absolutive, that is quantifiers 
largely float from intransitive subjects (67%) or 
direct objects of transitive verbs (24%) rather 
than from transitive subjects (4%) or indirect 
objects (1%). 
On the question of why quantifiers appear 
outside of the noun phrases they quantify, there 
have been two explanations: Discourse new in- 
formation floats to the pre-verb focus position 
(Downing, 1996; Kim, 1995), quantifiers float 
from noun phrases that 'measure out' an event 
(Alam, 1997). 
We speculate that there may be a perfor- 
mance based reason. Hawkins (1994) has shown 
that many phenomena claimed to be discourse 
related are in fact largely due to performance. 
However we have not yet compiled sufficient em- 
pirical evidence to show this conclusively. 
3 An algorithm to identify and 
anchor floating quantifiers 
The proposed algorithm is outlined in Figure 1. 
In our implementation it is appplied to each of 
one or more candidate outputs of a Japanese 
dependency parser as part of the semantic rank- 
ing. 
3.1 Identify potential floating 
quantifiers 
The first step is to identify potential floating 
quantifiers. 
Every adjunct case element headed by a noun 
is checked. All numeral classifier combinations 
are potential candidates. 
An adjunct must must meet two conditions to 
be considered a floating quantificational nouns, 
one semantic and one syntactic. The semantic 
criterion is that one of the noun's senses must be 
154 
For each unit sentence 
Identify potential floating 
quan~ifiers (QP) 
\[Numeral-classifier 
or Quantificational Noun\] 
Identify potential anchors (NP) 
\[nominative or accusative\] 
Discard bad combinations 
\[semantic anomalies, 
degree modifiers, event modifiers\] 
Rank remaining combinations 
Prefer accusative 
Prefer anchor on the left 
Prefer closest 
Anchor the best candidate pair(s) 
Figure 1: Algorithm to anchor floating quanti- 
tiers 
subsumed by quanta, few/some, all-part. 
The syntactic criterion is that the part of 
speech subcategory must be one of degree or 
quantifier adverbial. 4 We use the Goi-Taikei 
(Ikehara et al., 1997) to test for the senses and 
Miyazaki et al. (1995) for the syntactic classifi- 
cation. 
3.2 Identify potential anchors 
All noun phrases that matched a case-slot 
marked with -ga (nominative) or -o (accusative) 
are accepted as potential anchors. This is the 
traditional criterion given for potential anchors. 
Note even if the surface marker is different, for 
example when the case-marker is overwritten by 
a focus-marker such as -wa "topic", the 'canon- 
ical' case-marker will be found by our parser. 
Noun phrases marked with -hi (dative), have 
been shown to be permissible candidates, but 
we do not allow them. Such sentences are, how- 
ever, rare outside linguistics papers. We found 
no such candidates in the sentences we exam- 
ined, and Downing (1996, 239) found only one 
in ninety six examples. When we tried allow- 
ing dative noun phrases, it significantly reduced 
the performance of our algorithm: every dative 
noun phrase selected was wrong. If we could 
determine which noun phrases measure-out the 
action, then they should also be considered as 
4This part of speech category actually includes both 
true adverbs and adverb-like nouns. 
candidates, but we have no way to identify them 
at present. 
3.3 Discard bad combinations 
Some combinations of anchor and quantifier can 
be ruled out. We have identified three cases: 
semantically anomalous cases; sentences where 
the quantifier modifies the verb as a degree 
modifier; and sentences where the quantifier 
modifies the verb as a frequency modifier. 
3.3.1 Semantically anomalous cases 
Singular noun phrases In Japanese, pro- 
nouns and names are typically marked with a 
collectiviser (such as -tachi) if there are multi- 
ple referents (see e.g. Martin (1988, 143-154)). 
A pronoun or name not so marked characteris- 
tically has a singular interpretation. For names 
this can be overridden by a numeral-classifier 
combination (8), although it is rare, but not by 
an quantificational noun (9). 
(8) Matsuo-san-ga 3-n{n shabetta 
Matsuo-HON-NOM 3-CL spoke 
3 Matsuos spoke 
(9) Matsuo-san-ga takusan shabetta 
Matsuo-HON-NOM many spoke 
Matsuo spoke a lot 
In all the texts we examined, we found no ex- 
amples of names modified by floating numeral- 
classifier combinations. We therefore block all 
pronouns and names not modified by a collec- 
tiviser from serving as anchors to floating quan- 
tifiers. 
In Japanese, there is not a clear division be- 
tween pronouns and common nouns, particu- 
larly kin-terms such as ojisan "grandfather/old 
man". Pronouns can be modified in the same 
way as common nouns, and kin-terms are often 
used to refer to non kin. Pronouns modified by 
quantifiers need to be translated by more gen- 
eral terms as in (10). 
(10) kanojo-tachi-ga 3-nin kita 
she-COL-NOM 3-CL came 
? 3 she came 
The 3 girls came 
Classifier semantic restrictions For nu- 
meral classifiers, the selectional restrictions of 
the classifier can be used to disallow certain 
155 
combinations. For example,-kai "event" can 
only be used to modify event-nouns such as 
shokuji "meal" or fishin "earthquake". How- 
ever, the semantics are very complicated, and 
there is a great deal of variation, as a classifier 
can select not just for the object denoted by its 
target but also a sub-part of it. In addition, 
classifiers can be used to select meanings figu- 
ratively, coercing a new interpretation of their 
head. Bond and Paik (1997) suggest a way of 
dealing with this in the generative lexical frame- 
work of Pustejovsky (1995) but it requires more 
information about the conceptual structure of 
noun phrases than is currently available. 
For the time being, we use a simple table 
of forbidden combinations. For example pointo 
"point" will not be used to quantify nouns de- 
noting agent, place or abstract noun. 
3.3.2 Degree modification 
Noun quantifiers can be used as degree modi- 
fiers as well as quantifying some referent. If the 
predicate is used to state a property of the po- 
tential anchor, then a noun quantifier will char- 
acteristically be a degree modifier. 
We use the verbal semantic attributes given 
in the Goi-Taikei (Ikehara et al., 1997) to 
test for this relationship. Anchoring will 
be blocked either if the potential anchor is 
nominative and the verbal semantic attribute 
is one of attribute transfer, existence, 
attribute or result or if the anchor is ac- 
cusative and the verbal semantic attribute is 
physical/attribute transfer. 
Sentence (ii) shows this constraint in action: 
(11) kodomo-ga sukoshi samui 
child-NOM a little cold 
* A few children are cold 
The child is a little cold 
3.3.3 Event modification 
The final case we need to consider is where the 
noun quantifier can quantify the event or the 
affected theme of the event, such as (12). In 
Japanese, either reading is possible when the 
quantifier is in pre-verbal position. Anchor- 
ing the quantifier is equivalent to choosing the 
theme reading. 
(12) kare-wa k~ki-wo takusan tabeta 
he-TOP cake-NOM much ate 
He ate cake a lot 
He ate a lot of cake 
(event) 
(theme) 
Examining our corpus showed the theme 
reading to be the default. Of course, if the 
event is modified elsewhere, for example by a 
temporal modifier, then different readings are 
possible. The system in which our implementa- 
tion was tested lacks a system for event quan- 
tification, so we were not able to implement 
any constraint for this phenomenon. We there- 
fore implemented the theme reading as our de- 
fault. Note that, for stative verbs with per- 
manent readings such as shiru "know", there is 
almost no difference between the two readings 
(13). 
(13) watashi-wa ratengo-wo sukoshi 
I-TOP Latin-ACC a little 
shitte-iru 
know 
I know a little Latin 
I know Latin a little 
3.4 Rank and select candidates 
If there are more than two combinations, the 
following heuristics are used to choose which one 
or ones to choose. 
Prefer accusative: A combination with an 
accusative anchor gets two points: This is 
to allow for the absolutive bias. 
Prefer left anchor: If the anchor is to the left 
of the quantifier score it with one point: 
Quantifiers tend to float to the right of their 
anchors. 
Prefer closest: Subtract one for each inter- 
vening quantifier: Closer targets are bet- 
ter. 
Finally select the highest scoring combination 
and eliminate any combinations that include the 
chosen quantifier and anchor. If there is still a 
combination left (e.g. there were two quantifiers 
and two targets) then select it as well. 
These heuristics rule out crossing combina- 
tions in the rare instances of two quantifiers and 
two candidates. 
156 
Floating 
Quantifiers: 
Anchored Not anchored 
Good Bad Good Bad 
Nouns (N): 12 2 7 0 
Num-Cls (XC): 16 7 11 1 
Total: 28 9 18 1 
Table 2: Test results 
3.5 Anchoring 
Once the best combinations are chosen, the 
quantifier can be anchored to its target. We 
consider the best way to represent this would 
be by showing the semantic relation in a sepa- 
rate level from the syntax, in a similar way to 
the architecture outlined by Jackendoff (1997). 
Our implementation is in a machine transla- 
tion system and we simply rewrite the sentence 
so that the floating quantifier becomes an pre- 
nominal modifier of its target, marked with the 
adnominal case-marker -no. The resulting mod- 
ifier is labeled as 'anchored', to allow special 
processing during the transfer phase. 
4 Results and Discussion 
The algorithm was tested on a 3700 sentence 
machine translation test set of Japanese sen- 
tences with English translations, produced by 
a professional human translator. A description 
of the test set and its design is given in Ikehara 
et al. (1994). 
Overall, 56 possible combinations were found 
and 37 anchored in 3700 sentences: Table 2. 
Of these, 9 were anchored that should not 
have been, and 1 was not anchored that 
should have been. The accuracy (correctly an- 
chored/anchored) was 76% (28/37), and the 
recall (correctly anchored/should be anchored) 
was 97% (28/29). 
The major source of errors was from parsing 
errors in the system as a whole. All of the badly 
anchored numeral-classifiers combinations were 
caused by this. In this case, the algorithm has 
not degraded the system performance, it would 
have been a bad result anyway. 
There were three problems with the algorithm 
itself. In one case an anaphoric quantifier was 
mistaken as a floating quantifier, in another the 
verbal semantic attribute check for degree mod- 
ification gave a bad result. Finally there was 
one case where the default blocking for seman- 
tic anomalies blocked a good combination. 
Translation of floating quantifiers 
Note that anchoring a floating quantifier is only 
the first step toward translating it. Special han- 
dling is sometimes needed to translate the an- 
chored quantifiers. 
For example, Japanese has some universal 
pronouns that can stand alone as full noun 
phrases (14) or act as floating quantifiers (15): 
e.g., minna "everyone", zen'in "all members". 
When they are anchored, the information about 
the denotation of the head carried by the pro- 
noun is redundant, and should not be trans- 
lated. A special rule is required for this. 
(14) minna-ga sorou 
everyone-NOM gather 
All members gather. 
(15) membd-ga minna sorou 
members-NOM everyone gather 
All the members gather. 
*Everyone's members gather. 
Further work 
The proposed algorithm forms a solid base for 
extensions in various ways. 
1. Combine it with a fuller system of event 
semantics. 
2. Make the treatment of classifier-target se- 
mantics more detailed, so that inbuilt se- 
mantic restrictions can be used instead of 
a table of forbidden combinations. 
3. Use the results of the algorithm to help 
choose between candidate parses and in- 
tegrate it with the resolution of zero pro- 
nouns. 
4. Test the algorithm on other languages, for 
example Korean. 
5 Conclusion 
We have presented an algorithm to anchor float- 
ing quantifiers in Japanese. The algorithm pro- 
ceeds as follows. First identify potential float- 
ing quantifiers: either numeral classifier combi- 
nations or quantificational nouns. Then iden- 
tify potential anchors: all accusative or nom- 
inative noun phrases. Inappropriate combina- 
tions are deleted, either because of a semantic 
157 
mismatch between the target and quantifier, or 
because the quantifier is interpreted as a degree 
or event modifier. Finally, possible combina- 
tions are ranked, with the accusative candidate 
being the best choice, then the closest and left- 
most. The algorithm is robust and uses the full 
power of currently available detailed semantic 
dictionaries. 
Acknowledgments 
The authors thank Tim Baldwin, Yukie Kurib- 
ayashi, Kyonghee Paik and the members of the 
NTT Machine Translation Research Group for 
their discussion and comments on this paper 
and earlier versions. The research was carried 
out while Daniela Kurz visited the NTT Com- 
munication Science Laboratories. Francis Bond 
is currently also enrolled part time as a doc- 
toral candidate at the University of Queens- 
land's Center for Language Teaching & Re- 
search. 

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