=Paper=
{{Paper
|id=Vol-1347/paper40
|storemode=property
|title=Classification of German verbs using nouns in argument positions and aspectual features
|pdfUrl=https://ceur-ws.org/Vol-1347/paper40.pdf
|volume=Vol-1347
|dblpUrl=https://dblp.org/rec/conf/networds/RichterH15
}}
==Classification of German verbs using nouns in argument positions and aspectual features==
https://ceur-ws.org/Vol-1347/paper40.pdf
Classification of German verbs using nouns in argument positions and
aspectual features
Michael Richter Jürgen Hermes
Radboud University Nijmegen University of Cologne
mprrichter@t-online.de hermesj@uni-koeln.de
the aspect-based classification of Richter and van
Abstract Hout (2015) into five classes which extends the
typology of Vendler (1967), i.e.
This paper provides evidence that
accomplishments, achievements, states and
aspectual verb classes (Vendler, 1967)
activities by the class accomplishments with an
can be induced from nominal fillers in
affected subject.
argument positions and aspectual
This classification into five aspectual verb
features. We classified 35 German verbs
classes was derived by combining two user based
in a supervised learning procedure using
classifications induced by cluster analyses from
a support vector machine classifier and a
raters’ judgments and associations with stimulus
classification into five aspectual classes
verbs and two usage based classifications
(Richter and van Hout, 2015) as gold
induced from corpus data (Richter and van Hout,
standard and observed excellent and
2015). We took this classification as gold
substantial agreements.
standard as we were interested in the correlation
1 Introduction of the semantics of the nominal fillers in
argument positions of verbs and the aspectual
This study aims to empirically validate aspectual properties of verbs thereby following Klein
verb classes in German using large corpus data. (2009) who defines aspect as a grammatical
Siegel (1997) and Siegel and McKeown (2000) category of verbs.
induced the two aspectual classes states and In the present study we represent verbs as vectors
events in the frame of a vector space model from that consist of nouns in argument positions
corpora, however an induction of the complete separated into areas according to their noun
Vendlerian typology has not yet been classes, which were induced by cluster analyses
undertaken. We hypothesize that aspectual verb from similarity data. In addition, we added
classes can be automatically induced from the aspectual features as defined by Vendler (1967)
classified nominal fillers in the argument to the vectors in order to compare the predictive
position of verbs. Our hypothesis refers to the power of the noun classes in argument positions
Distributional Hypothesis (Rubenstein and against the predictive power of the aspectual
Goodenough, 1965; Schütze and Pedersen, 1995; features, respectively. The test set of verbs was
Landauer and Dumais, 1997; Pantel, 2005) classified in a supervised learning procedure
which says that semantically related linguistic using a support vector machine (SVM) classifier.
elements appear in semantically related contexts. In order to compare the results with aspectual
The present study in the framework of a vector verbs classes as gold standard with a gold
space model is also driven by the Statistical standard-classification based on concrete
Semantics Hypothesis (Weaver, 1955; Furnas et semantic categories compatible with
al., 1983; Turney and Pantel, 2010) which states Schumacher's typology (1986) of German verbs,
that linguistic meaning can be derived from we trained the SVM classifier with a
statistic linguistic patterns. In order to test our classification based on ten verb classes which
hypothesis, we took a test set of verbs from comprises classes such as verbs of consumption
Schumacher (1986) and determined the nominal and verbs of handicraft working (Richter and van
fillers and their classes in argument positions. Hout, 2015). This classification was induced
That is, in subject, direct, indirect, and
prepositional object positions by parsing a very
large German corpus. As gold standard we used
Copyright © by the paper’s authors. Copying permitted for private and academic purposes.
In Vito Pirrelli, Claudia Marzi, Marcello Ferro (eds.): Word Structure and Word Usage. Proceedings of the NetWordS Final
Conference, Pisa, March 30-April 1, 2015, published at http://ceur-ws.org
177
from the co-occurrence data bank (CCDB) of the by the TF-IDF measure and classified by cluster
Institut für Deutsche Sprache (IDS).1 analyses carried out on a matrix with similarity
values taken from the co-occurrence data bank
2 Method (CCDB) of the Institut für Deutsche Sprache
We classified 35 common German verbs used by (IDS).5 On the matrix of the similarity values, a
Schumacher (1986), who defines seven lexical cluster analysis with Ward’s method and
semantic macrofields and 30 subfields. We chose Euclidean distance was carried out. According to
the verbs from all subfields, the only criterion the Bayesian Information Criterion there are two
being the representation of every subfield in optimal noun classes for all arguments. We
order to cover the total semantic range of interpreted the resulting noun classes using our
Schumacher’s typology (1986). We checked the intuition thereby applying the criterion of
frequency of the verbs in the first one million animacy (Croft, 2003; Aissen, 2003): The
sentences containing at least one of our selected resulting two noun classes can be interpreted as
verbs of the web based 880 million word denoting predominantly animate and inanimate
SDEWAC corpus2. The verbs of our test set things, respectively class 1[+animate] for instance,
occurred in more than one million sentences with contains nouns such as Arzt ’doctor’ Lehrkraft
a mean frequency of approximately 30,000 ’teacher’ and class 2[-animate] contains nouns such
occurrences per verb. 66 percent of the verbs was as Entwicklung ’development’, Organisation
in the interval between 5,000 and 40,000 ’organization’ and Wahrnehmung ’perception’.
occurrences, the more frequent outliers being The verbs’ vectors consist of areas for each
müssen ‘to must’ with 500,965 and halten für ‘to argument type. There are four areas in total and
take so./sth. for so./sth.’ with 123,595 each area is split into areas for each noun class as
occurrences. We added five verbs; hämmern ‘to is depicted in (1):
hammer’, schneiden ‘to cut’, aufessen ‘to eat up’,
laufen ‘to walk/to run’, and zersägen ‘to saw into
.
pieces’ since these verbs since a previous study
.
(Richter and van Hout 2015) showed (i) that
.
laufen ‘to walk/to run’ and zersägen ‘to saw into
pieces’ are typical activity and accomplishment
verbs respectively and (ii) that aufessen ‘to eat =
up’ is a typical accomplishment with an affected
.
subject verb. Schneiden ‘to cut’ and hämmern ‘to
.
hammer’ were ambiguous (Richter and van
Hout, 2015), but we decided to classify in this .
study the former as accomplishment and the
( : ℎ !! )
latter as a process verb.
In order to determine the verbs’ arguments we
parsed at most 30.000 sentences per verb using
the Mate-Tools dependency-parser (Bohnet, Figure 1. Dimensions of verb vectors: Weighted verbs
2010)3. The whole code we used for filtering and in noun class areas.
parsing the sentences, and aggregating the
actants and aspectual features (see below) is In addition, the vectors were completed by
available at GitHub.4 The 35 verbs of our test set aspectual features that Vendler (1967) suggested
(Richter and van Hout, 2015) are represented as in order to distinguish aspectual verb classes.
139 dimensional vectors containing the 30 most The aspectual features indicate, for instance,
frequent nouns in the verbs argument positions: whether the verbs occur in sentences with
subjects, direct objects, indirect objects and temporal specifications of duration or a limited
prepositional objects. The nouns were weighted time span with prepositions in and for,
respectively, as in he wrote the letter in an hour
1
http://corpora.ids-mannheim.de/ccdb/. The similarity versus he wrote the letter for an hour, whether
values were provided by Cyril Belica. the verbs can be embedded by matrix verbs such
2
The SdeWaC Corpus is available at the WaCky as persuade or whether they occur in imperative
Corporadownload page at forms. In order to classify the 35 verbs we used a
http://wacky.sslmit.unibo.it/doku.php?id=corpora
3
See https://code.google.com/p/mate-tools/
4 5
https://github.com/spinfo/verbclass The similarity values were provided by Cyril Belica.
178
SVM classifier with a non-linear kernel which müssen* ‘to must’, einschlafen* ‘to fall asleep’ ,
achieved the best results. vergehen ‘to go (by)/ to pass/to disappear’,
We first trained the SVM using the classification übersehen ‘to overlook’, fehlen ‘to lack’,
of Richter and van Hout (2014) as a gold verlieren ‘to loose’, verhindern ‘to prevent’,
standard and tested it with a 10-fold cross- abgrenzen ‘to mark off/ to define’, abweichen
validation. The gold standard classification in ‘to deviate
detail: 4. verbs of transfer (of information):
mitteilen ‘to inform’, übermitteln ‘to
1. accomplishments: communicate/to forward’
aufbauen auf ‘to build on/to be based on’, 5. verbs of examination (by mental activity):
herstellen ‘to produce’, schneiden ‘to cut‘, nachprüfen ‘to ascertain/to check’, erörtern ‘to
zersägen ’to saw into pieces‘, verlängern ‘to debate’, untersuchen ‘to examine’
extend’, mitteilen ‘to tell/to inform’, übermitteln 6. verbs of production:
‘to communicate/to forward’, verhindern ‘to aufbauen auf ’to build on‘/acc to be based on’,
prevent’, abgrenzen ‘mark off/to define’ herstellen ‘to produce’
2. accomplishments with affected subject: 7. verbs of beginning and rising processes:
untersuchen ‘to examine‘, bedenken ’to anfangen ‘to begin’, ansteigen ‘to rise/ to
consider‘, erörtern ‘to debate’, nachprüfen ‘to increase’
ascertain/to check’, aufessen ‘to eat up’, essen 8. verbs of discussion and consideration:
‘to eat’ betreffen ‘to concern’, bedenken ‘to consider’,
3. activities: eingehen auf ‘to respond to so./sth.’, halten für
laufen ‘to walk/to run‘, eingehen auf ‘to respond ‘to take, richten auf ‘to direct towards‘,
to so./sth.’, hämmern ‘to hammer’, ansteigen ‘to orientieren an ‘to be geared to’
increase‘ 9. verbs of membership and agreement:
4. achievements: angehören ‘to belong to’, übereinstimmen mit ’to
einschlafen ‘to fall asleep‘, vergehen ‘to go agree with’
(by)/to pass/to diasappear‘, übersehen ‘to 10. folgen aus ‘to follow from’, laufen ‘to
overlook’, verlieren’to loose’, anfangen ‘to walk/to run’, existieren ‘to exist’,
begin‘, abweichen ‘to deviate‘, sich orientieren verlängern ‘to extend’
an ‘to be geared to‘, richten auf ‘to direct
towards/to focus’ 2.1 Results
5. states:
existieren ‘to exist‘, fehlen ‘to lack‘, müssen ‘to In order to evaluate the consistency of the
must‘, halten für ‘to take so./sth.for so./sth.‘, comparisons of the classifications against the
folgen aus ‘to follow from‘, angehören ‘to gold standards we calculated both accuracy and
belong to‘, übereinstimmen ‘to agree‘, betreffen Cohen’s kappa. The latter measure considers the
‘to concern’, abweichen ‘to deviate’ , verhindern number of classes which differ in the two gold
‘to prevent’ standards and, in addition, gives the significance
levels.
The classification into classes of concrete lexical Taking the classification with five aspectual
properties which we induced from the co- verbs classes as gold standard the subject feature
occurrence data bank (see above) is given below clearly outperforms the remaining features with
(the class labels are compatible with .857 accuracy (which means that 30 of 35 verbs
Schumacher’s labels and are assigned using our were classified correctly) and k = .812. Kappa
linguistic intuitions; class 10 is incoherent and values above .61 are characterized as substantial,
could not be labelled): above .81 as almost perfect agreement and
therefore highly significant. The combinations
1. verbs of activities manipulating a substance subject-direct object-prepositional object-
(normally with a tool): aspectual features and subject-direct object-
hämmern ‘to hammer’, schneiden ‘to cut’, aspectual features yield .828 accuracy, k = .775
zersägen ‘to saw into pieces’ and k = .773, respectively. The combinations
2. verbs of consumption: subject-prepositional object-aspectual features,
aufessen ‘to eat up’, essen ‘to eat‘ subject-direct object-prepositional object and
3. verbs of difference, ‘negative’ processes, non- subject- aspectual features yield .8 accuracy each
existence: with k = .741, k = .739 and k = .71, respectively.
179
In contrast the remaining features, including the Figure 2. Accuracy of the argument and aspectual
aspectual feature which yields .514 % accuracy, features using five aspectual verb classes vs. ten
with k = .317 (fair agreement), perform poorly. classes with concrete lexical properties as gold
Taking the classification according to concrete standard.
semantic properties into ten classes as the gold
Note: s: subject, d: direct object, i: indirect object, p:
standard we observed that the hierarchy remains prepositional object, as: aspectual features, and
almost the same, the subject feature outperforms combinations of predictors, for instance, das: direct
the remaining features. However, the accuracy is object and aspect, sp: subject and prepositional object.
considerably lower compared to the
classification with 5 aspectual verb classes. The 3 Conclusion
subject achieves .657 accuracy, k = .573. The The study provides evidence for the hypothesis
combinations subject-direct object-aspectual that aspectual verb classes can be induced from
features and subject-direct object-prepositional classified nominal fillers in argument positions.
object yield .628 accuracy with k = .458, For the five aspectual verb classes used as the
followed by the combinations subject-direct gold standard (Richter and van Hout, 2014) it
object and subject-aspectual features with .6 turned out that noun classes in subject positions
accuracy each and k = .495. These combinations have the highest predictive power compared to
exhibit a moderate agreement. Again, the the nouns in the remaining argument positions
aspectual feature performs poorly with .428 and the aspectual features derived from Vendler
accuracy, k = .266 which is a fair agreement. In (1967). This result is surprising since the
figure 2 the accuracy of the argument and Vendlerian aspectual categories were formulated
aspectual features for the comparisons against in order to distinguish aspectual classes. Future
both gold standard classifications are given. research should explore a comparison of the
predictive power of nominal and aspectual
features.
Using a classification into concrete lexical fields
as the gold standard of the predictive values we
observed a considerable decrease in the
predictive values indicated by the lower kappa
values. We explain this result by the difference
in information provided by the argument
structures of the verbs in the 5 class-gold
standard classification in contrast to the
information provided by co-occurrences that is,
lexical information of any type in the context of
verb in the 10-class gold standard classification.
The results of this study show that: 1. Aspectual
verb classes can be empirically validated, 2.
Classified nouns in subject argument positions
are reliable predictors of aspectual verb classes,
i.e. the meaning of nouns in combination with
their noun classes correlates with aspectual parts
of the verbal meaning. In order to confirm these
results further research with an extended test set
of verbs is needed.
Acknowledgments
Roeland van Hout suggested to evaluate the
classifications with Cohen‘s Kappa and was very
helpful in the calculations of the k-values.
180
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