The Role of Modifier and Head Properties in Predicting the Compositionality of English and German Noun-Noun Compounds: A Vector-Space Perspective
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The Role of Modifier and Head Properties in Predicting the
Compositionality of English and German Noun-Noun Compounds:
A Vector-Space Perspective
Sabine Schulte im Walde and Anna Hätty and Stefan Bott
Institut für Maschinelle Sprachverarbeitung, Universität Stuttgart
Pfaffenwaldring 5B, 70569 Stuttgart, Germany
{schulte,haettyaa,bottsn}@ims.uni-stuttgart.de
Abstract modifier and head transparency (T) vs. opaque-
ness (O) within a compound. Examples for these
In this paper, we explore the role of con- categories were car-wash (TT), strawberry (OT),
stituent properties in English and Ger- jailbird (TO), and hogwash (OO). Libben et al.
man noun-noun compounds (corpus fre- confirmed Zwitserlood’s analyses that both se-
quencies of the compounds and their con- mantically transparent and semantically opaque
stituents; productivity and ambiguity of compounds show morphological constituency; in
the constituents; and semantic relations addition, the semantic transparency of the head
between the constituents), when predict- constituent was found to play a significant role.
ing the degrees of compositionality of the From a computational point of view, address-
compounds within a vector space model. ing the compositionality of noun compounds (and
The results demonstrate that the empirical multi-word expressions in more general) is a cru-
and semantic properties of the compounds cial ingredient for lexicography and NLP appli-
and the head nouns play a significant role. cations, to know whether the expression should
be treated as a whole, or through its constituents,
1 Introduction
and what the expression means. For example,
The past 20+ years have witnessed an enormous studies such as Cholakov and Kordoni (2014),
amount of discussions on whether and how the Weller et al. (2014), Cap et al. (2015), and Salehi
modifiers and the heads of noun-noun compounds et al. (2015b) have integrated the prediction of
such as butterfly, snowball and teaspoon influence multi-word compositionality into statistical ma-
the compositionality of the compounds, i.e., the chine translation.
degree of transparency vs. opaqueness of the com- Computational approaches to automatically
pounds. The discussions took place mostly in psy- predict the compositionality of noun compounds
cholinguistic research, typically relying on read- have mostly been realised as vector space mod-
ing time and priming experiments. For example, els, and can be subdivided into two subfields:
Sandra (1990) demonstrated in three priming ex- (i) approaches that aim to predict the meaning
periments that both modifier and head constituents of a compound by composite functions, relying
were accessed in semantically transparent En- on the vectors of the constituents (e.g., Mitchell
glish noun-noun compounds (such as teaspoon), and Lapata (2010), Coecke et al. (2011), Baroni
but there were no effects for semantically opaque et al. (2014), and Hermann (2014)); and (ii) ap-
compounds (such as buttercup), when primed ei- proaches that aim to predict the degree of compo-
ther on their modifier or head constituent. In con- sitionality of a compound, typically by comparing
trast, Zwitserlood (1994) provided evidence that the compound vectors with the constituent vec-
the lexical processing system is sensitive to mor- tors (e.g., Reddy et al. (2011), Salehi and Cook
phological complexity independent of semantic (2013), Schulte im Walde et al. (2013), Salehi et
transparency. Libben and his colleagues (Libben al. (2014; 2015a)). In line with subfield (ii),
et al. (1997), Libben et al. (2003)) were the first this paper aims to distinguish the contributions
who systematically categorised noun-noun com- of modifier and head properties when predicting
pounds with nominal modifiers and heads into four the compositionality of English and German noun-
groups representing all possible combinations of noun compounds in a vector space model.
148
Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics (*SEM 2016), pages 148–158,
Berlin, Germany, August 11-12, 2016.Up to date, computational research on noun one opaque constituent) produce semantic prim-
compounds has largely ignored the influence of ing of their constituents. For the heads of seman-
constituent properties on the prediction of compo- tically transparent compounds, a larger amount of
sitionality. Individual pieces of research noticed facilitation was found than for the modifiers. Dif-
differences in the contributions of modifier and ferences in the results by Sandra (1990) and Zwit-
head constituents towards the composite functions serlood (1994) were supposedly due to different
predicting compositionality (Reddy et al., 2011; definitions of partial opacity, and different prime–
Schulte im Walde et al., 2013), but so far the target SOAs.
roles of modifiers and heads have not been distin- Libben and his colleagues (Libben et al. (1997),
guished. We use a new gold standard of German Libben (1998), and Libben et al. (2003)) were the
noun-noun compounds annotated with corpus fre- first who systematically categorised noun-noun
quencies of the compounds and their constituents; compounds with nominal modifiers and heads
productivity and ambiguity of the constituents; and into four groups representing all possible com-
semantic relations between the constituents; and binations of a constituent’s transparency (T) vs.
we extend three existing gold standards of German opaqueness (O) within a compound: TT, OT, TO,
and English noun-noun compounds (Ó Séaghdha, OO. Libben’s examples for these categories were
2007; von der Heide and Borgwaldt, 2009; Reddy car-wash (TT), strawberry (OT), jailbird (TO),
et al., 2011) to include approximately the same and hogwash (OO). They confirmed Zwitserlood’s
compound and constituent properties. Relying on analyses that both semantically transparent and se-
a standard vector space model of compositional- mantically opaque compounds show morphologi-
ity, we then predict the degrees of compositional- cal constituency, and also that the semantic trans-
ity of the English and German noun-noun com- parency of the head constituent was found to play
pounds, and explore the influences of the com- a significant role. Studies such as Jarema et al.
pound and constituent properties. Our empirical (1999) and Kehayia et al. (1999) to a large ex-
computational analyses reveal that the empirical tent confirmed the insights by Libben and his col-
and semantic properties of the compounds and the leagues for French, Bulgarian, Greek and Polish.
head nouns play a significant role in determining
Regarding related computational work, promi-
the compositionality of noun compounds.
nent approaches to model the meaning of a com-
pound or a phrase by a composite function include
2 Related Work
Mitchell and Lapata (2010), Coecke et al. (2011),
Regarding relevant psycholinguistic research on Baroni et al. (2014), and Hermann (2014)). In this
the representation and processing of noun com- area, researchers combine the vectors of the com-
pounds, Sandra (1990) hypothesised that an asso- pound/phrase constituents by mathematical func-
ciative prime should facilitate access and recog- tions such that the resulting vector optimally rep-
nition of a noun compound, if a compound con- resents the meaning of the compound/phrase. This
stituent is accessed during processing. His three research is only marginally related to ours, since
priming experiments revealed that in transparent we are interested in the degree of compositional-
noun-noun compounds, both constituents are ac- ity of a compound, rather than its actual meaning.
cessed, but he did not find priming effects for the Most closely related computational work in-
constituents in opaque noun-noun compounds. cludes distributional approaches that predict the
Zwitserlood (1994) performed an immediate degree of compositionality of a compound regard-
partial repetition experiment and a priming exper- ing a specific constituent, by comparing the com-
iment to explore and to distinguish morpholog- pound vector to the respective constituent vector.
ical and semantic structures in noun-noun com- Most importantly, Reddy et al. (2011) used a stan-
pounds. On the one hand, she confirmed San- dard distributional model to predict the compo-
dra’s results that there is no semantic facilitation of sitionality of compound-constituent pairs for 90
any constituent in opaque compounds. In contrast, English compounds. They extended their predic-
she found evidence for morphological complex- tions by applying composite functions (see above).
ity, independent of semantic transparency, and that In a similar vein, Schulte im Walde et al. (2013)
both transparent and also partially opaque com- predicted the compositionality for 244 German
pounds (i.e., compounds with one transparent and compounds. Salehi et al. (2014) defined a cross-
149lingual distributional model that used translations German Noun-Noun Compound Datasets As
into multiple languages and distributional simi- basis for this work, we created a novel gold stan-
larities in the respective languages, to predict the dard of German noun-noun compounds: Gh OST-
compositionality for the two datasets from Reddy NN (Schulte im Walde et al., 2016). The new
et al. (2011) and Schulte im Walde et al. (2013). gold standard was built such that it includes a rep-
resentative choice of compounds and constituents
3 Noun-Noun Compounds from various frequency ranges, various productiv-
Our focus of interest is on noun-noun compounds, ity ranges, with various numbers of senses, and
such as butterfly, snowball and teaspoon as well with various semantic relations. In the follow-
as car park, zebra crossing and couch potato in ing, we describe the creation process in some de-
English, and Ahornblatt ‘maple leaf’, Feuerwerk tail, because the properties of the gold standard are
‘fireworks’, and Löwenzahn ‘dandelion’ in Ger- highly relevant for the distributional models.
man, where both the grammatical head (in English Relying on the 11.7 billion words in the web
and German, this is typically the rightmost con- corpus DECOW14AX 2 (Schäfer and Bildhauer,
stituent) and the modifier are nouns. We are inter- 2012; Schäfer, 2015), we extracted all words that
ested in the degrees of compositionality of noun- were identified as common nouns by the Tree Tag-
noun compounds, i.e., the semantic relatedness be- ger (Schmid, 1994) and analysed as noun com-
tween the meaning of a compound (e.g., snowball) pounds with exactly two nominal constituents by
and the meanings of its constituents (e.g., snow the morphological analyser SMOR (Faaß et al.,
and ball). More specifically, this paper aims to 2010). This set of 154,960 two-part noun-noun
explore factors that have been found to influence compound candidates was enriched with empiri-
compound processing and representation, such as cal properties relevant for the gold standard:
• frequency-based factors, i.e., the frequencies • corpus frequencies of the compounds and the
of the compounds and their constituents (van constituents (i.e., modifiers and heads), rely-
Jaarsveld and Rattink, 1988; Janssen et al., ing on DECOW14AX;
2008);
• productivity of the constituents i.e., how
• the productivity (morphological family size), many compound types contained a specific
i.e., the number of compounds that share a modifier/head constituent;
constituent (de Jong et al., 2002); and
• number of senses of the compounds and the
• semantic variables as the relationship be- constituents, relying on GermaNet (Hamp
tween compound modifier and head: a teapot and Feldweg, 1997; Kunze, 2000).
is a pot FOR tea; a snowball is a ball MADE
OF snow (Gagné and Spalding, 2009; Ji et From the set of compound candidates we extracted
al., 2011). a random subset that was balanced3 for
In addition, we were interested in the effect of am- • the productivity of the modifiers: we cal-
biguity (of both the modifiers and the heads) re- culated tertiles to identify modifiers with
garding the compositionality of the compounds. low/mid/high productivity;
Our explorations required gold standards of
compounds that were annotated with all these • the ambiguity of the heads: we distinguished
compound and constituent properties. Since most between heads with 1, 2 and >2 senses.
previous work on computational predictions of For each of the resulting nine categories (three
compositionality has been performed for English productivity ranges × three ambiguity ranges),
and for German, we decided to re-use existing we randomly selected 20 noun-noun compounds
datasets for both languages, which however re- 2
http://corporafromtheweb.org/decow14/
quired extensions to provide all properties we 3
We wanted to extract a random subset that at the same
wanted to take into account. We also created a time was balanced across frequency, productivity and am-
novel gold standard. In the following, we describe biguity ranges of the compounds and their constituents, but
defining and combining several ranges for each of the three
the datasets.1 criteria and for compounds as well as constituents would have
1
The datasets are available from http://www.ims. led to an explosion of factors to be taken into account, so we
uni-stuttgart.de/data/ghost-nn/. focused on two main criteria instead.
150from our candidate set, disregarding compounds constituent compositionality ratings on a scale
with a corpus frequency < 2,000, and disregard- from 1 (definitely semantically opaque) to 6 (def-
ing compounds containing modifiers or heads with initely semantically transparent). Another five na-
a corpus-frequency < 100. We refer to this dataset tive speakers provided additional annotation for
of 180 compounds balanced for modifier produc- our small core subset of 180 compounds on the
tivity and head ambiguity as Gh OST-NN/S. same scale. As final compositionality ratings, we
We also created a subset of 5 noun-noun com- use the mean compound–constituent ratings across
pounds for each of the 9 criteria combinations, by the 13 annotators.
randomly selecting 5 out of the 20 selected com- As alternative gold standard for German noun-
pounds in each mode. This small, balanced sub- noun compounds, we used a dataset based on a
set was then systematically extended by adding selection of noun compounds by von der Heide
all compounds from the original set of compound and Borgwaldt (2009), that was previously used
candidates with either the same modifier or the in computational models predicting composition-
same head as any of the selected compounds. Tak- ality (Schulte im Walde et al., 2013; Salehi et al.,
ing Haarpracht as an example (the modifier is 2014). The dataset contains a subset of their com-
Haar ’hair’, the head is Pracht ’glory’), we added pounds including 244 two-part noun-noun com-
Haarwäsche, Haarkleid, Haarpflege, etc. as well pounds, annotated by compositionality ratings on
as Blütenpracht, Farbenpracht, etc.4 We refer to a scale between 1 and 7. We enriched the existing
this dataset of 868 compounds that destroyed the dataset with frequencies, and productivity and am-
coherent balance of criteria underlying our ran- biguity scores, also based on DECOW14AX and
dom extraction, but instead ensured a variety of GermaNet, to provide the same empirical infor-
compounds with either the same modifiers or the mation as for the Gh OST-NN datasets. We refer
same heads, as Gh OST-NN/XL. to this alternative German dataset as VD HB.
The two sets of compounds (Gh OST-NN/S and
Gh OST-NN/XL) were annotated with the seman- English Noun-Noun Compound Datasets
tic relations between the modifiers and the heads, Reddy et al. (2011) created a gold standard for
and compositionality ratings. Regarding seman- English noun-noun compounds. Assuming that
tic relations, we applied the relation set sug- compounds whose constituents appeared either
gested by Ó Séaghdha (2007), because (i) he as their hypernyms or in their definitions tend
had evaluated his annotation relations and anno- to be compositional, they induced a candidate
tation scheme, and (ii) his dataset had a similar compound set with various degrees of compound–
size as ours, so we could aim for comparing re- constituent relatedness from WordNet (Miller et
sults across languages. Ó Séaghdha (2007) him- al., 1990; Fellbaum, 1998) and Wiktionary. A
self had relied on a set of nine semantic rela- random choice of 90 compounds that appeared
tions suggested by Levi (1978), and designed and with a corpus frequency > 50 in the ukWaC
evaluated a set of relations that took over four corpus (Baroni et al., 2009) constituted their
of Levi’s relations (BE, HAVE, IN, ABOUT) gold-standard dataset and was annotated by
and added two relations referring to event partici- compositionality ratings. Bell and Schäfer (2013)
pants (ACTOR, INST(rument)) that replaced annotated the compounds with semantic relations
the relations MAKE, CAUSE, FOR, FROM, using all of Levi’s original nine relation types:
USE. An additional relation LEX refers to lexi- CAUSE, HAVE, MAKE, USE, BE, IN,
calised compounds where no relation can be as- FOR, FROM, ABOUT. We refer to this dataset
signed. Three native speakers of German anno- as R EDDY.
tated the compounds with these seven semantic Ó Séaghdha developed computational models
relations.5 Regarding compositionality ratings, to predict the semantic relations between modi-
eight native speakers of German annotated all fiers and heads in English noun compounds (Ó
868 gold-standard compounds with compound– Séaghdha, 2008; Ó Séaghdha and Copestake,
2013; Ó Séaghdha and Korhonen, 2014). As
4
The translations of the example compounds are hair gold-standard basis for his models, he created a
washing, hair dress, hair care, floral glory, and colour glory. dataset of compounds, and annotated the com-
5
In fact, the annotation was performed for a superset of
1,208 compounds, but we only took into account 868 com- pounds with semantic relations: He tagged and
pounds with perfect agreement, i.e. IAA=1. parsed the written part of the British National Cor-
151Annotation
Language Dataset #Compounds
Frequency/Productivity Ambiguity Relations
Gh OST-NN/S 180 DECOW GermaNet Levi (7)
DE Gh OST-NN/XL 868 DECOW GermaNet Levi (7)
VD HB 244 DECOW GermaNet –
R EDDY 90 ENCOW WordNet Levi (9)
EN
OS 396 ENCOW WordNet Levi (6)
Table 1: Noun-noun compound datasets.
pus using RASP (Briscoe and Carroll, 2002), and In this paper, we use VSMs in order to model
applied a simple heuristics to induce compound compounds as well as constituents by distribu-
candidates: He used all sequences of two or more tional vectors, and we determine the semantic re-
common nouns that were preceded or followed by latedness between the compounds and their mod-
sentence boundaries or by words not representing ifier and head constituents by measuring the dis-
common nouns. Of these compound candidates, tance between the vectors. We assume that the
a random selection of 2,000 instances was used closer a compound vector and a constituent vec-
for relation annotation (Ó Séaghdha, 2007) and tor are to each other, the more compositional (i.e.,
classification experiments. The final gold standard the more transparent) the compound is, regard-
is a subset of these compounds, containing 1,443 ing that constituent. Correspondingly, the more
noun-noun compounds. We refer to this dataset as distant a compound vector and a constituent vec-
OS. tor are to each other, the less compositional (i.e.,
Both English compound datasets were enriched the more opaque) the compound is, regarding that
with frequencies and productivities, based on the constituent.
ENCOW14AX 6 containing 9.6 billion words. We Our main questions regarding the VSMs are
also added the number of senses of the con- concerned with the influence of constituent prop-
stituents to both datasets, using WordNet. And we erties on the prediction of compositionality. I.e.,
collected compositionality ratings for a random how do the corpus frequencies of the compounds
choice of 396 compounds from the OS dataset and their constituents, the productivity and the am-
relying on eight experts, in the same way as the biguity of the constituents, and the semantic rela-
Gh OST-NN ratings were collected. tions between the constituents influence the qual-
ity of the predictions?
Resulting Noun-Noun Compound Datasets
Table 1 summarises the gold-standard datasets. 4.1 Vector Space Models (VSMs)
They are of different sizes, but their empirical and
semantic annotations have been aligned to a large We created a standard vector space model for
extent, using similar corpora, relying on WordNets all our compounds and constituents in the vari-
and similar semantic relation inventories based on ous datasets, using co-occurrence frequencies of
Levi (1978). nouns within a sentence-internal window of 20
words to the left and 20 words to the right of
4 VSMs Predicting Compositionality the targets.7 The frequencies were induced from
the German and English COW corpora, and trans-
Vector space models (VSMs) and distributional in-
formed to local mutual information (LMI) values
formation have been a steadily increasing, integral
(Evert, 2005).
part of lexical semantic research over the past 20
Relying on the LMI vector space models, the
years (Turney and Pantel, 2010): They explore
cosine determined the distributional similarity
the notion of “similarity” between a set of tar-
between the compounds and their constituents,
get objects, typically relying on the distributional
which was in turn used to predict the degree
hypothesis (Harris, 1954; Firth, 1957) to deter-
mine co-occurrence features that best describe the 7
In previous work, we systematically compared window-
words, phrases, sentences, etc. of interest. based and syntax-based co-occurrence variants for predicting
compositionality (Schulte im Walde et al., 2013). The current
6
http://corporafromtheweb.org/encow14/ work adopted the best choice of co-occurrence dimensions.
152of compositionality between the compounds and 4.3 Influence of Compound Properties on
their constituents, assuming that the stronger the VSM Prediction Results
distributional similarity (i.e., the cosine values),
Figures 1 to 5 present the core results of this paper:
the larger the degree of compositionality. The vec-
They explore the influence of compound and con-
tor space predictions were evaluated against the
stituent properties on predicting compositionality.
mean human ratings on the degree of composition-
Since we wanted to optimise insight into the influ-
ality, using the Spearman Rank-Order Correlation
ence of the properties, we selected the 60 maxi-
Coefficient ρ (Siegel and Castellan, 1988).
mum instances and the 60 minimum instances for
each property.9 For example, to explore the in-
4.2 Overall VSM Prediction Results
fluence of head frequency on the prediction qual-
Table 2 presents the overall prediction results ity, we selected the 60 most frequent and the 60
across languages and datasets. The mod column most infrequent compound heads from each gold-
shows the ρ correlations for predicting only the standard resource, and calculated Spearman’s ρ
degree of compositionality of compound–modifier for each set of 60 compounds with these heads.
pairs; the head column shows the ρ correlations Figure 1 shows that the distributional model
for predicting only the degree of compositional- predicts high-frequency compounds (red bars) bet-
ity of compound–head pairs; and the both col- ter than low-frequency compounds (blue bars),
umn shows the ρ correlations for predicting the across datasets. The differences are significant for
degree of compositionality of compound–modifier Gh O S T-NN/XL.
and compound–head pairs at the same time.
Dataset mod head both
Gh OST-NN/S 0.48 0.57 0.46
DE Gh OST-NN/XL 0.49 0.59 0.47
VD HB 0.65 0.60 0.61
R EDDY 0.48 0.60 0.56
EN
OS 0.46 0.39 0.35
Table 2: Overall prediction results (ρ).
The models for VD HB and R EDDY represent
replications of similar models in Schulte im Walde
et al. (2013) and Reddy et al. (2011), respectively,
but using the much larger COW corpora. Figure 1: Effect of compound frequency.
Overall, the both prediction results on VD HB
are significantly8 better than all others but R EDDY; Figure 2 shows that the distributional model
and the prediction results on OS compounds are predicts compounds with low-frequency heads
significantly worse than all others. We can also better than compounds with high-frequency heads
compare within-dataset results: Regarding the two (right panel), while there is no tendency regarding
Gh OST-NN datasets and the R EDDY dataset, the the modifier frequencies (left panel). The differ-
VSM predictions for the compound–head pairs are ences regarding the head frequencies are signifi-
better than for the compound–modifier pairs. Re- cant (p = 0.1) for both Gh O S T-NN datasets.
garding the VD HB and the OS datasets, the VSM Figure 3 shows that the distributional model
predictions for the compound–modifier pairs are also predicts compounds with low-productivity
better than for the compound–head pairs. These heads better than compounds with high-
differences do not depend on the language (ac- productivity heads (right panel), while there
cording to our datasets), and are probably due to is no tendency regarding the productivities of
properties of the specific gold standards that we modifiers (left panel). The prediction differences
did not control. They are, however, also not the regarding the head productivities are significant
main point of this paper. for Gh O S T-NN/S (p < 0.05).
8 9
All significance tests in this paper were performed by For R EDDY, we could only use 45 maximum/minimum
Fisher r-to-z transformation. instances, since the dataset only contains 90 compounds.
153Figure 2: Effect of modifier/head frequency.
Figure 3: Effect of modifier/head productivity.
Figure 4: Effect of modifier/head ambiguity.
154Figure 4 shows that the distributional model 5 Discussion
also predicts compounds with low-ambiguity
While modifier frequency, productivity and am-
heads better than compounds with high-ambiguity
biguity did not show a consistent effect on the
heads (right panel) –with one exception (Gh OST-
predictions, head frequency, productivity and
NN/XL)– while there is no tendency regarding the
ambiguity influenced the predictions such that
ambiguities of modifiers (left panel). The predic-
the prediction quality for compounds with low-
tion differences regarding the head ambiguities are
frequency, low-productivity and low-ambiguity
significant for Gh O S T-NN/XL (p < 0.01).
heads was better than for compounds with high-
Figure 5 compares the predictions of the dis- frequency, high-productivity and high-ambiguity
tributional model regarding the semantic rela- heads. The differences were significant only for
tions between modifiers and heads, focusing on our new Gh OST-NN datasets. In addition, the
Gh O S T-NN/XL. The numbers in brackets refer to compound frequency also had an effect on the pre-
the number of compounds with the respective re- dictions, with high-frequency compounds receiv-
lation. The plot reveals differences between pre- ing better prediction results than low-frequency
dictions of compounds with different relations. compounds. Finally, the quality of predictions
also differed for compound relation types, with
BE compounds predicted best, and ACTOR com-
pounds predicted worst. These differences were
ascertained mostly in the Gh OST-NN and the OS
datasets. Our results raise two main questions:
(1) What does it mean if a distributional model
predicts a certain subset of compounds (with
specific properties) “better” or “worse” than
other subsets?
(2) What are the implications for (a) psycholin-
guistic and (b) computational models regard-
Figure 5: Effect of semantic relation. ing the compositionality of noun compounds?
Regarding question (1), there are two options
why a distributional model predicts a certain sub-
Table 3 summarises those differences across
set of compounds better or worse than other sub-
gold standards that are significant (where filled
sets. On the one hand, one of the underlying gold-
cells refer to rows significantly outperforming
standard datasets could contain compounds whose
columns). Overall, the compositionality of
compositionality scores are easier to predict than
BE compounds is predicted significantly better
the compositionality scores of compounds in a
than the compositionality of HAVE compounds
different dataset. On the other hand, even if
(in R EDDY), INST and ABOUT compounds (in
there were differences in individual dataset pairs,
Gh OST-NN) and ACTOR compounds (in Gh OST-
this would not explain why we consistently find
NN and OS). The compositionality of ACTOR
modelling differences for head constituent proper-
compounds is predicted significantly worse than
ties (and compound properties) but not for modi-
the compositionality of BE, HAVE, IN and
fier constituent properties. We therefore conclude
INST compounds in both Gh OST-NN and OS.
that the effects of compound and head properties
are due to the compounds’ morphological con-
HAVE INST ABOUT ACTOR stituency, with specific emphasis on the influences
BE R EDDY Gh OST Gh OST Gh OST, OS
of the heads.
HAVE OS Gh OST, OS
IN Gh OST, OS Looking at the individual effects of the com-
INST Gh OST, OS pound and head properties that influence the dis-
tributional predictions, we hypothesise that high-
Table 3: Significant differences: relations.
frequent compounds are easier to predict because
they have a better corpus coverage (and less
155sparse data) than low-frequent compounds, and compounds across various frequency, productivity
that they contain many clearly transparent com- and ambiguity ranges.
pounds (such as Zitronensaft ‘lemon juice’), and
at the same time many clearly opaque compounds 6 Conclusion
(such as Eifersucht ‘jealousy’, where the literal
translations of the constituents are ‘eagerness’ and We explored the role of constituent properties
‘addiction’). Concerning the decrease in predic- in English and German noun-noun compounds,
tion quality for more frequent, more productive when predicting compositionality within a vec-
and more ambiguous heads, we hypothesise that tor space model. The results demonstrated that
all of these properties are indicators of ambiguity, the empirical and semantic properties of the com-
and the more ambiguous a word is, the more diffi- pounds and the head nouns play a significant role.
cult it is to provide a unique distributional predic- Therefore, psycholinguistic experiments as well as
tion, as distributional co-occurrence in most cases computational models are advised to carefully bal-
(including our current work) subsumes the con- ance their selections of compound targets accord-
texts of all word senses within one vector. For ex- ing to compound and constituent properties.
ample, more than half of the compounds with the
most frequent and also with the most productive
Acknowledgments
heads have the head Spiel, which has six senses
in GermaNet and covers six relations (BE, IN, The research presented in this paper was funded
INST, ABOUT, ACTOR, LEX). by the DFG Heisenberg Fellowship SCHU 2580/1
Regarding question (2), the results of our distri- (Sabine Schulte im Walde), the DFG Research
butional predictions confirm psycholinguistic re- Grant SCHU 2580/2 “Distributional Approaches
search that identified morphological constituency to Semantic Relatedness” (Stefan Bott), and the
in noun-noun compounds: Our models clearly dis- DFG Collaborative Research Center SFB 732
tinguish between properties of the whole com- (Anna Hätty).
pounds, properties of the modifier constituents,
and properties of the head constituents. Further-
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