{X!SYN} : ’AUX above AUX’ : main : 1 : X.label = AUX
& has( X@id, AUX )
;
{X!SYN} : ’AUX above edge above AUX’ : main : 1 : X.label = AUX
& has( X@id, ’Is above AUX’ )
;
{X!SYN} : ’Is above AUX’ : ancillary : 1 : has( X@id, AUX )
;
Figure 5.6: Constraints extending their reach to their immediate edge neighbour (’AUX above AUX’) and to the neighbour’s neighbour (’AUX above edge above AUX’in combination with’Is above AUX’).
e.g.: Check that the edge label is AUX and the edge below it lies above another edge that lies above edge with label AUX. The constraints for these checks are exemplified in Figure 5.6.
One limitation this approach has not been able to overcome is that ancillary con-straints must be unary and can only be applied on the level of analysis of the calling edge. WCDG’s limitation that constraints can only be defined for edges on a maxi-mum of two levels of analysis thus remains. In our model, this did not impose any fundamental modelling restrictions.1
In summary, we can evaluate global sentence properties for trains of contiguous dependency edges on the same level of analysis by consecutive calls to ancillary constraints. More details on our approach of checking global sentence properties with localised constraints are given in McCrae et al. (2008).
WCDG’s standard lexicon contains syntactic valence definitions for all verbs. Ini-tially, it therefore appeared attractive to derive semantic valence definitions from those existing syntactic valences and simply map them onto each other by a set of correspondence rules. Doing so would bind semantics to syntax and thereby would reduce semantic representation to a mere derivative of syntactic representation. We opted against this approach in order to enable a genuinely bidirectional interaction between the semantic and syntactic levels of representation. Both levels need to build up their own representations that are based on as much independently defined information as possible. If one level of representation were simply a correspondence projection of the other, that level would not contribute any new information to the solution of the constraint satisfaction problem. In fact, the additional level of representation would be nothing more than a rule-based encoding of the original level causing a processing overhead without an actual gain of information.
An important question – both from the perspective of modelling and cognition – is which of a verb’s thematic role relations are required for the definition of its core meaning and hence should be included in a lexeme’s semantic valence representa-tion. From a modelling perspective, the verb needs to entertain enough thematic relations to permit the mapping of its syntactic arguments to the thematic roles – as required by R24. From a cognitive perspective, only those thematic relations should be included that are semantically integral to the definition of the verb’s core meaning. Thematic roles likeLOCATIONandCOMITATIVE, for example, can easily be omitted without violating the semantic completeness of the verb’s meaning. These roles may well be part of a situation description centred around an instantiation of the concept activated by the verb — but they are not an integral component of the verb’s representation of meaning. On the other hand, the omission of roles such as AGENTorTHEME is either semantically completely unacceptable or leads to a significant distortion of the original verb meaning.12
Ferretti et al.(2001) found that situation verbs prime their typical role fillers for the
AGENTandTHEMEroles. Since these thematic roles typically find their syntactic re-alisation in the mandatory verbal arguments of subject and direct object, we include them in our list of required verb-centred thematic roles. While not tested for by Ferretti et al., we also treat the roleRECIPIENTas essential to verb meaning for situ-ation verbs. Encoding the participant that the result of the situsitu-ation is directed to (cf. Table5.1) is an essential aspect of a situation description, which is also reflected
1From our constraint-based perspective on language processing this begs the question how hard these semantic constraints actually are. One challenge in answering this question lies in the difficulty of observing such semantic constraint violations in isolation, i.e., unaccompanied by a constraint violation on another level of linguistic analysis. In natural language, likely candidates for hard semantic constraint violations, such as the omission of an AGENT on an agentive verb, always seem to be accompanied by the violation of a similarly hard syntactic constraint.
2Many cases of humour, metaphor, or figurative usage derive their communicative effect from the deliberate violation ofsoft semantic constraints. A constraint violation binds some of the interlocutor’s cognitive resources in the effort to find an alternative utterance interpretation that results in the removal of the constraint violation or its replacement by a lesser constraint violation.
in the typical realisation as a mandatory indirect object.1 We choose to exclude the role ofINSTRUMENTfrom the semantic valence definitions. The reason for doing so is that the robust priming effect reported inFerretti et al.(2001) for this thematic role only pertains to a small set verbs that describe actions which are closely associated with the corresponding INSTRUMENT. Ferretti et al.’s findings support the view that for this group of verbs the INSTRUMENTinformation does indeed contribute to their generalised representation of meaning. It is unclear, however, how these findings generalise to verbs that describe situation types that are not actions and do not ex-hibit a close semantic tie with an INSTRUMENT. Also, Ferretti et al.’s do not permit conclusions as to whether a situation verb mandatorily needs to be accompanied by a role filler for theINSTRUMENTrole. Sentences containing situation verbs that prime typical INSTRUMENTs, e.g. to stir → spoon or to paint → brush, are semantically perfectly acceptable even in the absence of an explicit mention of the INSTRUMENT. Our decision to exclude the role of INSTRUMENTfrom semantic valence definitions is supported by earlier findings on the strength of inferences from situation verb mean-ing to its correspondmean-ingINSTRUMENTas discussed inFerretti et al.(2001, pp. 524).2 From an implementation point of view, we now need to decide which is the most suit-able way to represent semantic valences in the lexicon. As shown in Equations 4.3 and 4.4, increasing the number of homonyms in a slot also increases the overall number of unary and binary constraint evaluations for the corresponding sentence.
Decisions regarding lexical representation in WCDG may hence directly affect pro-cessing time. One of these decisions is how a verb’s semantic valences shall be matched against the corresponding syntactic valences.
WCDG1’s underspecified syntactic valence representation collapses several valences into a single valence representation that is assigned to a single lexical entry. This con-densed representation has the advantage of reducing the number of homonyms for a given slot string (cf. Section4.2.1). The disadvantage of the WCDG1-representation of semantic valence is that it potentially overgenerates invalid syntactic valence al-ternatives as shown in Figure 4.3. If semantic valences were to be represented by similarly condensed – but overgenerating – representations, the number of overgen-erated invalid lexical forms would increase multiplicatively (see Figure 5.7 for an example). If, on the other hand, we choose to represent every semantic valence combination explicitly, we increase the number of homonyms to be considered in parsing – without exact prior knowledge on how strongly this increase might affect processing times.
1We realise that quoting syntactic evidence in support of our semantic modelling decisions may expose us to Jackendoff’s criticism against thematic roles as a“thinly disguised wild card to meet the exigencies of syntax.” (Jackendoff,1990, p. 46). We stand up to this criticism by emphasising that our model permits full control over the degree to which syntactic decisions can influence semantic role assignments. In our model semantic processing proceeds on separate levels of analysis according to independently formulated semantic WFRs. The interaction between syntactic and semantic processing is bidirectional and fully open to control via the explicitly stated constraints in the syntax-semantics interface.
2This modelling decision proved to be adequate for the majority of verbs studied. A notable exception was provided by the verb ‘erliegen’ to succumb to which, in German, takes a mandatory Dative object that acts as an INSTRUMENT. As a result of our modelling decision, the CIA in its present form does not support semantic processing for sentences containing this verb.
Lexical Entry bezahlen:=[base:bezahlen,cat:VVINF,stress:
unstressed,perfect:haben,sem_val:ag_re?_th?, valence:’a?+d?’,avz:allowed];
Underspecified valence:a?+d?
Syntactic Valence
Correct Syntactic valence:- | valence:a | valence:a+d Valences
Overgenerated valence:d Syntactic Valence
Underspecified sem val:ag re? th?
Semantic Valence
Correct Semantic sem val:ag | sem val:ag th | sem val:ag re th Valences
Overgenerated sem val:ag re Semantic Valences
Valid Valence Combinations
sem val:ag & valence:- | sem val:ag th & valence:a | sem val:ag re th & valence:a+d | Overgenerated
Valence Combinations
sem val:ag & valence:a | sem val:ag & valence:d | sem val:ag & valence:a+d | sem val:ag th & valence:- | sem val:ag th & valence:d | sem val:ag th & valence:a+d | sem val:ag re th & valence:- | sem val:ag re th & valence:a | sem val:ag re th & valence:d
Figure 5.7: Example for the multiplicative increase of overgenerated invalid combinations of syn-tactic and semantic valences for ‘bezahlen’to pay as produced by systematic expansion of under-specified syntactic and semantic valence representations.
In our modelling effort, performance aspects play a subordinate role to the demon-stration of the conceptual feasibility of the context integration. We therefore assign a higher priority to the accuracy and correctness of the lexical representation and the parses resulting from it than to an improved performance of the implementa-tion. Based on this guideline we choose to define a separate lexical entry for each combination of syntactic and semantic valences. For each verb included in the scope of our implementation, we unambiguously specify the permissible combinations of their syntactic and a semantic valences. All other verbs continue to use their lexical representation from WCDG1.
AUX S
SUBJ
er will singen .
AGENT
Figure 5.8: The correct semantic analysis according to our semantic modelling approach in which the auxiliary has semantic valencenulland does not participate in a semantic dependency.
For verbs with multiple syntactic valences this approach counteracts the reduction in lexicon size achieved by the underspecified syntactic valences. At the same time, it eliminates the potentially large number of invalid syntactic and semantic valence combinations resulting from overgeneration. The example in Figure 5.7 shows that for the verb ‘bezahlen’ to pay three individual lexicon entries need to be added to the lexicon. While the condensed representation would only add one entry, it would also give rise to nine invalid valence combinations. This example illustrates how the addition of several lexical entries containing the separate semantic valence specifications can in fact be more economic with respect to the generation of valid homonyms than the addition of a single, underspecified entry.
Semantic valences were hand-annotated and have been included into the lexicon as values for the feature sem val for a subset of 1,063 unique verbs.1 These are the verbs for which thematic role assignments can be performed in the CIA.
Each semantic valence references a verb’s mandatory thematic roles by their initial two letters in lowercase. Multiple two-letter references occur in alphabetical order and are separated by an underscore. An exception to this nomenclature is provided by the semantic valence nullwhich is assigned to auxiliaries and modals that have been modelled such as not to participate in any semantic dependencies. Figure 5.8 provides the correct semantic analysis according to this modelling approach for the sentence ‘Er will singen.’ He wants to sing. In this example, ‘will’wants has semantic valence null and ‘singen’ sing has semantic valence ag.
Exemplary size effects for extending WCDG2’s lexicon according to our approach for representing semantic valence are shown in Table 5.3. The additions to the lexicon primarily result from enriching the lexical entries of verbs with the corresponding
1Initial attempts to automate the process of semantic valence specification were not pursued further.
Semantic valences were extracted from the annotated verb frames in the SALSA Corpus (Burchardt et al., 2006; SALSA Corpus Homepage, 2009), a semantically annotated extension of the TIGER 1.0 Corpus (Brants et al.,2002;TIGER Corpus Homepage,2009). The approach had to be abandoned due to sub-stantial difficulties in trying to define general rules for mapping the strongly lexicalised roles in the corpus to the more generalised thematic roles in our model implementation.
Category Unique Entity Counted WCDG1 WCDG2 Change Verb Infinitives Semantic Valence 8,750 9,321 +6.53%
VVFIN,VAFIN,VMFIN
Adjectives Lexical Baseform 9,158 9,164 + 0.07%
ADJA,ADJD
Nouns Lexical Baseform 27,473 27,485 + 0.04%
NN
Proper Names Lexical Baseform 30,881 30,884 + 0.01%
NE
All Lexical Entry 1,014,864 1,054,451 + 3.90%
Table 5.3: Size comparison of lexicon components for WCDG1 and WCDG2 based on entity counts in the generated full-form lexicons.
semantic valence information. While the figures in Table 5.3 are implementation specific, they document the predicted increase in lexicon size as a result of the cho-sen semantic valence reprecho-sentation. The change in lexicon size is mainly due to the addition of new lexical entries for verbs. Observe that the addition of a semantically annotated verb forms to the full-form lexicon adds more than just the single line for the verbal infinitive since, in the process of semi-automatic lexicon generation, the infinitive is expanded into its potentially large set of corresponding inflected forms, each of which is added as a separate entry. Also note that, strictly speaking, the se-mantically annotated infinitives were notadded to the existing lexicon. Rather, they replaced the corresponding entries that did not carry semantic valence information.