Learning to Distinguish PP Arguments from Adjuncts
Aline Villavicencio
Computer Laboratory, University of Cambridge
J.J Thomson Avenue, Cambridge, CB3 OFD, UK
Phone: +44-1223-763642
Fax: +44-1223-334678
Aline.Villavicencio@cl.cam.ac.uk
Abstract
Words differ in the subcategorisation frames in
which they occur, and there is a strong cor-
relation between the semantic arguments of a
given word and its subcategorisation frame, so
that all its arguments should be included in its
subcategorisation frame. One problem is posed
by the ambiguity between locative prepositional
phrases as arguments of a verb or adjuncts.
As the semantics for the verb is the same in
both cases, it is difficult to differentiate them,
and to learn the appropriate subcategorisation
frame. We propose an approach that uses se-
mantically motivated preposition selection and
frequency information to determine if a locative
PP is an argument or an adjunct. In order to
test this approach, we perform an experiment
using a computational learning system that re-
ceives as input utterances annotated with log-
ical forms. The results obtained indicate that
the learner successfully distinguishes between
arguments (obligatory and optional) and ad-
juncts.
1 Introduction
Words differ in the subcategorisation frames
that realise their semantic arguments, and a
given word may have several different subcate-
gorisation frames. The subcategorisation frame
includes all the complements of a given word.
For instance, the sentences:
†(1)John ate
†(2)John ate the apple
represent the intransitive and transitive frames,
respectively, and both are valid frames associ-
ated with the word eat. Given that the subcat-
egorisation frame of a given word should only
include a given constituent if it is an argument,
one problem is caused by the ambiguous nature
of some constituents, that can be either argu-
ments or adjuncts.
The ability to distinguish between subcate-
gorised arguments and non-subcategorised ad-
juncts is of great importance for several applica-
tions, such as automatic acquisition of subcat-
egorisation lexicons from data, and this prob-
lem has been widely investigated. For instance,
Buchholz (1998) investigates this task using a
memory-based learning approach, where the use
of syntactic and contextual features results in
a 91.6% accuracy in distinguishing arguments
from adjuncts. Brent (1994) looks at the prob-
lem from a more psychologically oriented per-
spective, trying to simulate the environment
available to a human language learner, and us-
ing binomial error estimation to derive subcat-
egorisation frames for verbs, based on imper-
fectly reliable local syntactic cues. This tech-
nique is able to capture the fact that the rel-
ative frequency of a verb-argument sequence is
likely to be higher than that of a verb-adjunct
sequence. However, the cues used in the sim-
ulations are too simple to achieve high accu-
racy. Steedman (1994) suggests the use of se-
mantic information to deal with this ambigu-
ity, given that syntax should be as close as
possible to semantics. Then, given that for
a particular language there is a strong cor-
relation between the subcategorisation frames
and predicate-argument structure of a given
word, from the predicate-argument structure of
a word it is possible to infer its subcategorisa-
tion frame.
In terms of the difficulty of this task, Buch-
holz (1998) found that in the experiments con-
ducted the ambiguity presented by Preposi-
tional Phrases (PPs) was the most difficult case
to classify, accounting for 23% of the errors.
Moreover, Brent (1994) also found in his sim-
ulations that locative adjuncts were sometimes
mistaken for arguments. In this paper we focus
on the problem of distinguishing between loca-
tive PPs as arguments or adjuncts, where only
if a given locative PP is an argument is that
it should be included in the subcategorisation
frame of the verb. The approach proposed here
is to use semantically motivated preposition se-
lection and frequency information to determine
if a locative PP is an argument of the verb or if
it is an adjunct. In order to test this approach,
we use a computational learning system, and
the results obtained indicate the effectiveness
of the approach.
The wider goal of this project is to inves-
tigate the process of grammatical acquisition
from data. Thus, in section 2 we start by giving
some background in language acquisition em-
ployed in the learning model, which is described
in section 3. Characteristics of the ambiguity
between arguments and adjuncts are discussed
in section 4 together with the approach used to
distinguish them. In section 5 we describe an
experiment conducted to test the approach. We
finish with some conclusions and a discussion of
future work.
2 Language Acquisition
In trying to solve the question of how to get a
machine to automatically learn language from
data, we can look at the way people do it. When
we acquire our mother language we are exposed
to an environment that includes noisy and un-
grammatical sentences, the potential influence
of other languages, and many other linguistic
phenomena. In spite of that, most children are
successful in the acquisition of a grammar in a
relatively short time, acquiring a sophisticated
mechanism for expressing their ideas, based on
data that is said to be too impoverished to gen-
erate such a complex capacity. One approach
to explain the acquisition of languages proposes
that children must have some innate knowledge
about language, a Universal Grammar (UG), to
help them overcome the problem of the poverty
of the stimulus and acquire a grammar on the
basis of positive evidence only (Chomsky 1965).
According to Chomsky’s Principles and Param-
eters Theory (Chomsky 1981), the UG is com-
posed of principles and parameters, and the pro-
cessoflearningalanguageisregardedastheset-
ting of values of a number of parameters, given
exposure to this particular language. Another
likely source of information that is available to
children when learning a language is the seman-
tic interpretation or related conceptual repre-
sentation. Indeed, as Steedman (1994) puts it:
“Since the main thing that syntax is for is
passing concepts around, the belief that syntac-
tic structure keeps as close as possible to seman-
tics, and that in both evolutionary and child lan-
guage acquisition terms, the early development
of syntax amounts to little more than hanging
words onto the preexisting armatures of concep-
tual structure is so simple and probable as to
amount to the null hypothesis”.
A third source of information can be found
in the statistical properties of the input data to
which children seem to be sensitive, as observed
in recent work in psycholinguistics.
3 The Learning System
These ideas about human language acquisition
are employed, in this work, in the construc-
tion of a computational learning system that
can learn from its linguistic environment, which
may contain noise and ambiguities (Villavicen-
cio 2002).
Studies like this can not only be used to pro-
vide clues about possible directions to follow in
the automatic acquisition of information from
data, but also to help us understand better the
process of human language learning. However,
if that is to be achieved, we need to concentrate
only on algorithms and resources that a human
learner could employ. Thus, there are signif-
icant constraints on the assumptions that can
be made in the learning system implemented.
In this way, the learner cannot have access to
negative information; it also cannot start with
information specific to a particular language,
and can only assume information that is gen-
eral among human languages. Another aspect
is that learning has to be on-line and incremen-
tal, with the system only processing one sen-
tence at a time, without the possibility of stor-
ing sentences and reprocessing previously seen
sentences, or doing multiple passes through the
corpus. Moreover, the kind of data given to the
learner must be compatible with the linguistic
environment of a child.
In this work the linguistic environment of the
learner is simulated to a certain extent by us-
ing spontaneous child-directed sentences in En-
glish, which were extracted from the Sachs cor-
pus (MacWhinney 1995) (Sachs 1983). Some of
the semantic and contextual information avail-
able to children is introduced in the corpus by
annotating the sentences with logical forms. At
the moment around 1,500 parents’ sentences are
annotated with the corresponding logical forms.
The computational learning system employed
in this investigation is composed of a UG and
associated parameters, and a learning algorithm
(Villavicencio 2002). The UG is represented
as a Unification-Based Generalised Categorial
Grammar, and it provides the core knowledge
about grammars that the learner has. A learn-
ing algorithm fixes the parameters of the UG to
the target language based on exposure to it. In
this work, this is in the form of the annotated
parents’ sentences to simulates some of the char-
acteristics of the environment in which a child
acquires her language. Finally, children’s sensi-
tivity to statistical properties of the data is also
simulated to some extent in the learning system.
4 Learning from Ambiguous Triggers
The learning environment to which the learner
is exposed contains noise and ambiguity and the
learner has to be able to deal with these prob-
lems if it is to set its parameters correctly and
converge to the target grammar. In this work
we concentrate on the ambiguity in the form of
locative PP that can occur either as arguments
to a verb or as adjuncts.
When processing a sentence the learner needs
to determine appropriate syntactic categories
for the semantic predicates used as input in or-
der to correctly set its parameters. In most
cases, the learner is able to find the required
syntactic categories, using the Categorial Prin-
ciples (Steedman 2000). According to these
principles from the semantic interpretation of
a word and some directional information for a
language, it is possible to determine the syntac-
tic form of the corresponding category. 1 These
principles help the learner to determine the sub-
categorisation frame for a given word based on
1These principles are closely related to the Projection
Principle(Chomsky 1981)thatstatesthattheselectional
requirements of a word are projected onto every level of
syntactic representation.
its semantic predicate. Then, for instance, in
the sentence:
†(3)John talks to Mary
with logical form
†(4) talk-communicative-act(e,x,y), john(x),
comm-to(y), mary(y)
the verb talks has two arguments, the NP sub-
ject John, and the PP to Mary, as represented
in the logical form associated with the verb,
where the PP is the second argument and as
suchshouldbeincludedinthesubcategorisation
frame of the verb: (SnNP)/PP. On the other
hand, in the sentence:
†(5)Bob eats with a fork
with logical form
†(6) eat-ingest-act(e,x), bob(x), instr-
with(e,y), a(y), fork(y)
the PP with a fork is not an argument of the
verb eat as reflected in its logical form and
should not be included in its subcategorisation
frame, which is SnNP.
It means that from the logical form associ-
ated with a verb, the learner can decide whether
a given constituent is an argument of the verb,
and should be included as its complement in
the subcategorisation frame or not. However,
one exception to this case is that of verbs oc-
curring with locative PPs, which can be either
arguments or adjuncts. The ambiguity between
these cases arises because in this logical form
representation the logical form describing the
verb with an argument locative PP is similar to
that describing the verb with an adjunct loca-
tive PP. For example, the sentence:
† (7) Bill kisses Mary in the park,
with logical form:
†(8) kiss-contact-act(e,x,y), bill(x), mary(y),
loc-in(e,z), the(z), park(z)
exemplifies a case where the locative PP is an
adjunct. Thus it should not be included in the
subcategorisation frame of the transitive verb
kiss, which is (SnNP)/NP. On the other hand,
the sentence:
†(9)Bill swims across the river
with logical form:
†(10) swim-motion-act(e,x), bill(x), motion-
across(e,y), the(y), river(y)
shows a case where the PP is an (optional) argu-
ment of the verb swim, and where the appropri-
ate subcategorisation frame for the verb should
include it ((SnNP)/PP), even though the PP is
not included in the logical form of the verb.
Forbothsentences, thelogicalformhasasim-
ilar structure, with both a verbal and a loca-
tive predicate, with the PP not being included
in the logical form of the verb. As a con-
sequence, the logical form cannot be used to
help the learner resolve the ambiguity: given
the logical forms fkiss-contact-act(e,x,y), loc-
in(e,z)g and fswim-motion-act(e,x), motion-
across(e,y)g, which syntactic category should
the learner choose for each of these verbs? This
ambiguity constitutes a significant problem for
the learner, since it has to decide whether a
given PP is functioning as a complement of a
verb or if it is working as an adjunct. Three
different cases to which the learner is exposed
are identified, based on Pustejovsky (1995) and
Wechsler (1995), with the PP occurring as an
obligatory argument, as an optional argument,
or as an adjunct2:
1. The PP is an obligatory argument of
the verb. For certain verbs the PP is an
obligatory argument of the verb and should
be included in its subcategorisation frame.
An instance of this case is the verb put, in
sentence 11:
† (11) Mary put the book on the shelf,
where the verb occurs with a locative PP.
Also, as the ungrammaticality of sentence
12 suggests, this verb requires a locative
PP:
† (12)* Mary put the book
The appropriate syntactic category for the
verb3 is ((SnNP)/PP)/NP.
2. The PP is an optional semantic argu-
ment of the verb. For example, a verb
such as swim can occur as in sentence 9,
where it is modified by a directional PP
which is an optional argument of the verb,
but this verb may also occur without the
PP, as in sentence 13:
† (13) Bill swims.
2In this work we classify PPs in terms of these three
cases, even though more fine-grained classifications can
be used as by Pustejovsky (1995).
3This work does not include, in its investigation,
elliptical or noisy constructions. Therefore, the sen-
tences analysed and the frequencies reported exclude
these cases.
This is a case of a verb that can occur in
both constructions with the PP being a se-
mantic argument, which, when occurring,
must be included in the subcategorisation
frame of the verb. Consequently, the ap-
propriate category for the verbswim in sen-
tence 9 is (SnNP)/PP, and in 13 is SnNP.
3. The PP is an adjunct. Adjuncts modify
the logical form of the sentence, but are not
part of the subcategorisation frame of the
verb. The PPin the park insentence7isan
example of an adjunct that is neither part
of the semantic argument structure of the
verb kiss nor part of its subcategorisation
frame. This verb can also occur without
the PP, as in sentence 14:
† (14) Bill kisses Mary.
The appropriate syntactic category for the
verb in both sentences is (SnNP)/NP.
When faced with a locative PP, the learner
has to identify which of these cases is appro-
priate. The required subcategorisation frame is
determined independently for each verb sense,
depending on the semantic type of the verb,
and on its frequency of occurrence with a par-
ticular subcategorisation frame and predicate
argument-structure combination.
In order to determine if a locative PP is an
obligatory argument of the verb, the learner
uses frequency information about the occur-
rence of each verb with locative PPs. If the fre-
quency with which they occur together is above
a certain threshold, the PP is considered to be
an obligatory argument of the verb and included
in its subcategorisation frame. In this case, the
threshold is set to 80% of the total occurrences
of a verb. This is high enough for discarding
adjuncts and optional arguments that occur oc-
casionally, and at the same time is not high
enough to be affected by the occurrence of noise.
In an analysis of all the mother’s sentences in
the entire Sachs corpus, only two occurrences
of put without the locative PP were found: one
seems to be an instance of an elliptical construc-
tion, and the other a derived sense. The fre-
quency with which put occurs with a locative
PP correctly indicates that the PP is an argu-
ment of the verb, and it needs to be included
in the subcategorisation frame of the verb. On
the other hand, for verbs like kiss and swim in
sentences like 7 and 9, the locative PP is an oc-
casional constituent, with the semantics of the
sentence including the location predicate only in
these cases. The occasional occurrence of PPs
with these verbs correctly indicates that they
are not obligatory arguments of the verbs.
If the frequency of occurrence is not above the
threshold, then the PP can be either an optional
argument or an adjunct. To determine if a PP is
an optional argument, the learner uses informa-
tion about the kind of semantic event denoted
by the verb. As Steedman (1994) notes
“... if we are asking ourselves why children
do not classify meet as subcategorising for NP
PP on the basis of sentences like (1b), we met
Harry on the bus, then we are simply asking
the wrong question. A child who learns this in-
stance of the verb from this sentence must start
from the knowledge that the denoted event is a
meeting, and that this involves a transitive event
concept”.
Thus, when the learner receives an input sen-
tence, it uses semantic information about the
kind of event denoted by the verb and prepo-
sition given in the logical form associated with
the sentence to check if the preposition can be
selected by the verb. This approach to iden-
tify non-obligatory argument PPs is based on
Wechsler’s proposal of semantically motivated
preposition selection (Wechsler 1995), where a
PP is an argument of a verb if it can be selected
by the verb on pragmatic grounds. The learner
represents pragmatic knowledge in terms of a
hierarchy of types and words are classified ac-
cording to these types, based on the seman-
tics associated with them.4 A verb can select
a preposition as an argument if the latter is of
the same type as the verb, or of one of its sub-
types in the hierarchy. A fragment of such a
hierarchy is shown in figure 1. Then, a verb
such as talk (in John talks to Mary), which as
specified in the logical form (in 4) denotes a
communicative event and is an instance of type
communicative-act, can select as its optional
argument a preposition such as to, which is of
type comm-to, because the latter type is a
subtype of the former on the world knowledge
4In this work we do not address the issue of how such
a pragmatic hierarchy would be constructed and we as-
sume that it is already in place. However, for a related
task, see Green (1997).
a0a2a1a4a3a5a3a7a6a4a8a4a9a0a11a10a13a12 a9a14a2a15a4a16 a10a4a0a17a12
a18a19a21a20a23a22a25a24a13a26
a9a8a13a27a4a15a2a28a2a29
a0a11a1a4a3a5a3a7a16 a12 a1
a18a19a30a20a31a22a30a32a33a26
a9a8a34a27a34a15a2a28a2a29
a0a2a1a4a3a5a3a35a16 a10a4a36a34a1a4a6a2a12
a18a19a21a20a23a22a30a37a33a26
a9a8a13a27a34a15a13a28a2a29
a0a11a1a38a3a7a3a7a16 a12 a1a34a16 a10a4a36a13a1a38a6a2a12
a39 a40 a41a38a41a4a42 a43 a44a39 a45 a46a44a47 a48 a49a45 a39 a46
a39 a40 a41a4a41a33a49a46a40 a39 a40 a41a4a41a33a49a45 a50 a40 a42 a46
a41a38a40 a46a44a40 a43 a49a45 a39 a46
a51
a44a52 a48 a39 a46a48
a51
a49a41a38a40 a46a44a40 a43
a41a33a40 a46a44a40 a43 a49a45 a39 a52 a40 a53 a53
a54a4a40 a52 a55
a51
a49a56 a43 a40 a54a4a55a48
a51 a57
a48
a46a52 a45 a43 a53 a58a48 a52 a49a45 a39 a46
a46a52 a45 a43 a53 a58a48 a52 a49a46a40
Figure 1: Fragment of Hierarchy of World
Knowledge
hierarchy. On the other hand, this verb does
not select a preposition such as across of type
motion-across as its argument, in a sentence
such as Bill talked about his treatment across
the country, because their types do not unify.
In this case, the PP is an adjunct to the verb.
However, this preposition can be selected as the
argument of the verb swim in sentence 9, which
denotes a motion event and is an instance of
typemotion-act. As words are associated with
types in the hierarchy, the lower in the hierarchy
a given word is, the more constrained its selec-
tional possibilities are (as discussed by Wech-
sler (1995)). In this way, the pragmatic knowl-
edge confirms certain PPs as arguments of some
verbs, while rejecting others.
If a locative PP is rejected as argument of
a verb on pragmatic grounds, then the PP is
treated as an adjunct and is not included in the
subcategorisation frame of the verb. Once the
learner decides which is the case for a particu-
lar verb PP combination, it uses the triggering
information, including the appropriate subcat-
egorisation frame of the verb, for further pro-
cessing.
5 Argument or Adjunct?
To test this approach we conducted an exper-
iment where the learner is evaluated in terms
of three different verbs: put where the PP is
an obligatory argument, come, where the loca-
tive PP is an optional argument, and draw (in
the sense of drawing a picture) where the PP is
an adjunct. These verbs are representative of
each case and the sentences in which they occur
are taken from the mother’s section of the com-
plete Sachs corpus, which is the largest of the
parents’ sections. The status of the locative PPs
occurring with these verbs is determined follow-
ing syntactic tests for argument structure. The
specific test used in this case is the “do so” test,
which is a standard test for argument structure,
as discussed by Verspoor (1997). In this test,
the claim is that a complete complement can
be replaced by “do so”. In the case of obliga-
tory arguments, only the full constituent verb
PP or verb NP PP can be replaced by do so,
while in the case of adjuncts, the verb or verb
NP constituent can also be replaced by do so.
Sentences 15 to 23 indicate that the PPs are
arguments of the verbs put and come, and ad-
juncts of the verb draw.
† (15) You put Goldie through the chimney
† (16) You put Goldie through the chimney
and Bob also did so
† (17) * You put Goldie through the chimney
and Bob did so through the window
† (18) You came from the garden
† (19) You came from the garden and John
also did so
† (20) * You came from the garden and John
did so from the kitchen
† (21) You drew in the park
† (22) You drew in the park and John also did
so
† (23) You drew in the park and John did so
in the garden
In these experiments, the learner is given as
input sentences from the annotated Sachs cor-
pus (all previously unseen), among which are
the ambiguous cases, as shown in table 1, col-
lected from the mother’s corpus. The learner
processes each sentence, having to determine
valid syntactic category assignments for each
word in the sentence (Villavicencio 2002) (Wal-
dron 2000), and based on these, setting the
parameters of the UG. For each sentence the
learner collects information about the words,
their corresponding logical forms, syntactic cat-
egories, and frequency of occurrence. When the
learner is faced with an ambiguous sentence, it
needs to disambiguate the PP as argument or
adjunct of the verb. It first checks if the fre-
quency of occurrence of the verb with locative
PPs as seen so far is above the threshold of 80%,
in which case the PP is considered to be an
obligatory argument of the verb. Otherwise, the
learner checks if the verb can select the PP on
pragmatic grounds, based on the pragmatic hi-
erarchy the learner has, and on the logical form
associated with the words. If so, the PP is an
optional complement of the verb. On the other
hand, if this is not the case, then the PP is con-
sidered to be an adjunct. After deciding, the
learner proceeds with the setting of parameters
and collects the new frequencies, as described
above, and goes on to process the next sentence.
Table 1: Disambiguation of Locative PPs
Verb Sentences Total
with PPs Sentences
put 137 137
come 24 32
draw 9 21
The results obtained for each of these three
verbs are that the learner correctly selects the
appropriate subcategorisation frame in all of
these cases, which confirms the effectiveness of
the proposed approach to disambiguate locative
PPs. In terms of frequency of occurrence of the
verbs with the locative PPs, other verbs in the
mother’s sentences from the entire Sachs corpus
also have a similar pattern, with the locative PP
being frequent for obligatory arguments of the
verb, and less frequent for the other cases:
† stay, which according to the “do-so” test
has an obligatory locative PP argument,
occurs in 100% of the cases with locative
PPs,
† come, which has optional locative PP ar-
guments, occurs in 69.6% of the cases with
locative PPs, and all of these can be seman-
tically selected by the verb,
† eat, as a transitive verb which does not
have a locative PP argument, occurs in
only 1.23% of the cases with locative PPs,
and
† play, as an intransitive verb also does not
have a PP argument, is in 40% of the cases
with a locative PP.
These results indicate that the proposed ap-
proach indeed helps the learner to disambiguate
between locative PPs as arguments or adjunct
based on frequency information and semanti-
cally motivated selection. Such an approach
provides a possible way forward in which to deal
with this problem for the research in the area.
It follow Steedman’s suggestion about the use
of semantic information, and similarly to Brent
and Buchholz it uses local information to deal
with this ambiguity, in a setting that is compat-
ible with some studies on language acquisition.
6 Conclusions and Future Work
In this paper we described one possible ap-
proach to deal with the problem of disambiguat-
ing between arguments or adjuncts. This ap-
proach is tested by a learning system used to in-
vestigate the automatic acquisition of language
from data. The learning system is equipped
with a plausible model of the Universal Gram-
marandithastosetitsparameterstothetarget
language based on the input data. The ambi-
guity between arguments and adjuncts is one of
several difficulties encountered by the learning
system during the acquisition process and the
approach proposed to overcome this problem,
proved to be effective and helped the learner
decide the appropriate case for the ambiguities
found in the data available. The implemented
learning system can successfully learn from a
corpus of real child-directed data, containing
noise and ambiguity, in a more realistic account
of parameter setting (Villavicencio 2002).
Disambiguation based on frequency informa-
tion and semantically motivated selection pro-
vides a plausible strategy, compatible with some
research on language acquisition. Although this
is primarily a cognitive computational model,
it is potentially relevant to the development of
more adaptive NLP technology, by indicating
possible paths for future developments in the
area. However, larger scale tests still need to be
conducted to see how the approach would gener-
alise, and for that we need more annotated data.
These two tasks of annotating more data and
undertaking this larger scale investigation are
included in the future directions of this work.
Acknowledgments
Thanks to Francis Bond for comments on this
paper. This research was supported in part by
the NTT/Stanford Research Collaboration, re-
search project on multiword expressions, and by
CAPES/Brazil.

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