A computational semantics approach to aphasic sentence comprehension

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A computational semantics approachto aphasic sentence comprehensionDavid Glenn Clark aa University of Alabama at Birmingham and the BirminghamVeteran's Affairs Hospital , ALPublished online: 28 Nov 2008.

To cite this article: David Glenn Clark (2009) A computational semantics approach to aphasicsentence comprehension, Aphasiology, 23:1, 33-51, DOI: 10.1080/02687030701657226

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http://www.psypress.com/aphasiology DOI: 10.1080/02687030701657226

A computational semantics approach to aphasic

sentence comprehension

David Glenn Clark

University of Alabama at Birmingham and the Birmingham Veteran’s Affairs Hospital,

AL, USA

Background: Patients with brain damage often exhibit difficulty understandingsentences, with certain grammatical constructions posing more of a problem thanothers. Differences between sentences that are ‘‘hard’’ or ‘‘easy’’ to process have beencharacterised in terms of modern syntactic theory, leading to a number of insightfulproposals regarding the nature of sentence comprehension problems in aphasia.However, little attention has been devoted to the semantic aspects of aphasic sentencecomprehension.Aims: The primary aim of this research is to validate the use of a computationalsemantic approach for modelling aphasic sentence comprehension.Methods & Procedures: The model presented here is an extension of natural languageprocessing software designed by Blackburn and Bos (2006). The original program parsesa natural language expression by means of a simple definite clause grammar, assigning asemantic representation to each node in the parse tree. The final result of a successfulparse is a sentence from first-order logic that describes the meaning of the naturallanguage sentence. The program was made relevant for the study of aphasia byextending the grammar to parse 14 sentences that present variable degrees of difficultyto aphasic patients. In addition, each constituent in the grammar was endowed with anintegrity feature that contained an integer with a maximum value of 100. This numberconstituted the percent chance that the node would be successfully realised and wasreduced in proportion to the node’s height in the syntactic tree. The constant ofproportionality was then manipulated to simulate various degrees of aphasia severity.Qualities of the model’s performance were compared to qualities of aphasic patientperformance on four key sentences. The model’s quantitative performance on 12sentences was compared to that of 46 patients with left hemisphere lesions.Outcomes & Results: There was a significant correlation between the performance of themodel and that of the patients (r 5 .85, p , .001). Sentence length was not significantlycorrelated with patient performance (r 5 2.52, ns). The probabilistic output of themodel resembles variability in performance by aphasic patients.Conclusions: A computational semantics approach to aphasic sentence comprehensionmay provide a means for explaining continuous variation in degrees of aphasia severityas well as qualitative patterns of agrammatic comprehension.

Keywords: Agrammatism; Aphasia; Natural language processing; Semantics; Syntax.

Address correspondence to: David Glenn Clark, Sparks Center, 360C 1720 7th Ave S., Birmingham,

AL 35294, USA. E-mail: [email protected]

APHASIOLOGY, 2009, 23 (1), 33–51

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APHASIC SENTENCE COMPREHENSION

Aphasic speakers of English often exhibit poor comprehension of semantically

reversible sentences with noncanonical word order despite apparently good

comprehension of sentences with canonical word order. For example, these patients

generally perform ‘‘at chance’’ when selecting pictures that depict the action

described in passive voice sentences, such as (1), and sentences with embedded

object-relative clauses, such as (2). Nevertheless, they often perform above

chance on the same task when presented with simple, active voice sentences, such

as (3), or sentences in which semantic cues and deductive processes facilitate

interpretation (4) (Caramazza & Zurif, 1976). Although patients who exhibit this

pattern of comprehension failure perform well when interpreting the thematic

roles assigned to the embedded verb in a subject-relative sentence, such as (5),

they perform poorly when attempting to interpret the verb in the matrix clause

(Hickok, Zurif, & Canseco-Gonzalez, 1993). That is, in the case of (5), patients

in the Caramazza and Zurif (1976) study typically knew that the cat scratched

the girl, but often selected a picture showing that the girl, rather than the cat, was

angry.

(1) The man is pushed by the boy.

(2) The cat that the girl scratched is angry.

(3) The man pushed the boy.

(4) The apple was eaten by the boy.

(5) The cat that scratched the girl is angry.

This pattern of deficits depends on the syntactic structure of sentences. Accordingly,

there have been many attempts to characterise the lesion in terms of syntax. The

term ‘‘agrammatism’’, which had originally been applied to a defect of language

production (Goodglass, 1993, pp. 103–106), has been applied to these comprehen-

sion deficits as well. Some effort has been put forth to link syntax to Broca’s area,

and Broca’s aphasia to agrammatism (Grodzinsky, 2000b). However, studies that

have investigated the relationship between clinical aphasia syndrome, lesion

localisation, and sentence comprehension have suggested that sentence processing

is not necessarily associated with a particular clinical subtype of aphasia, and is

supported by a large-scale neurocognitive network involving at least five regions of

the left hemisphere, with possible participation by the right hemisphere and

cerebellum (Caplan, Hildebrandt, & Makris, 1996; Dick et al., 2001; Dronkers,

Wilkins, Van Valin, Redfern, & Jaeger, 2004; Kaan & Swaab, 2002; Marien et al.,

1996).

Although some of the hypotheses that have been advanced have been couched

in cognitive psychological terms (Bates, Friederici, & Wulfeck, 1987; Caplan &

Waters, 1999), most have been phrased in terms of syntactic theory (Druks &

Marshall, 1995; Friederici & Gorrell, 1998; Grodzinsky, 2000b; Hickok et al.,

1993; Mauner, Fromkin, & Cornell, 1993; O’Grady & Lee, 2001, 2005), with the

‘‘lesion’’ defined as damage to some more or less abstract theoretical device,

such as Case, access to tree structures, or traces. One potential problem with

this approach is that a theory that defines a lesion in terms of loss of one of

these theoretical constructs makes discrete predictions about patient perfor-

mance, rather than predictions that accommodate a continuous spectrum of

performance.

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MODELS OF APHASIC SENTENCE COMPREHENSION

All attempts to model agrammatic comprehension computationally have utilised an

explicit, symbol-manipulating component (i.e., a parser—see, for example,

Haarman, Just, & Carpenter, 1997; Kolk, 1995; Vosse & Kempen, 2000). In general,

this has been combined with connectionist elements, such as activation states for

lexical items and trees, and connection strengths. The models were then ‘‘lesioned’’

by manipulating one or more parameters that affected their ability to integrate

information. This approach has seen important advances, most notably by Haarman

et al. (1997) and by Vosse and Kempen (2000). The central hypothesis behind the

model proposed by Haarman et al. (1997) is that variation in the language

performance of normal adults and aphasic patients is determined by individual

differences in syntactic working memory capacity. The model consisted of three

components: a lexicon, a parser, and a system that used activation states in the

parser to compute the activation states of thematic role representations. With this

third component it was possible to calculate how well the model ‘‘understood’’

various sentence structures. By manipulating the syntactic working memory capa-

city the authors were able to fit the model’s comprehension accuracy (i.e., thematic

role activation) to the average performance of a group of aphasic patients on

nine sentence types. The authors pointed out that the model performed somewhat

better than aphasic patients on dative constructions, and worse than the patients on

cleft object constructions. The authors also made an effort to demonstrate the

model’s capacity to account for variation in patient data by running the model

with two different levels of syntactic working memory capacity impairment. The

interaction between sentence complexity and severity of impairment was shown to be

similar between the model and the performance of two patients, but the authors

indicated that such an interaction did not appear to be a general property of

the model, with certain sentence complexity contrasts exhibiting the opposite

interaction.

Vosse and Kempen (2000) presented a model based on unification grammar that

reproduced a number of findings from psycholinguistic studies of normal sentence

processing. In addition they made an effort to model the same pool of aphasic

sentence comprehension data used by Haarman et al. (1997). In this case, however, it

was necessary to search a space of five variables in order to fit the model to the

patient data. While the Spearman correlation between the lesioned model and the

patient data was high, it is possible that a model this powerful is capable of fitting

patterns that are not observed among aphasic patients, and the five variables are not

easily related to any particular neural structures or cognitive operations. To account

for a range of patient performance data, the authors evaluated the model’s

performance at various degrees of degradation. This was done by manipulating the

values of the five variables along a continuous line between those resulting in good

performance and those resulting in agrammatic performance. At low levels of

degradation the model exhibited increasingly frequent failure to generate a full parse

for object-relative sentences, while at higher levels of degradation subject-relative

sentences were also degraded. The authors did not demonstrate a continuous range

of performance for other sentence structures, such as passives. In addition, this

model did not include a semantic component. Thus, sentence comprehension deficits

were viewed only in terms of parse failure, with no attempt to derive meaning from

any parse.

COMPUTATIONAL SEMANTICS AND COMPREHENSION 35

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MOTIVATIONS FOR THE CURRENT MODEL

A parsimonious representational explanation for the typical syntactic dissociation

that may occur in aphasia has been elusive (Kean, 1995), but inspection of the

literature readily identifies several qualities that would be desirable for any new,

putative account of the data. First, with regard to aphasic sentence production, recent

work indicates that the accuracy with which patients produce affixes depends on the

position at which the affix is introduced into the syntactic phrase marker tree, with

higher levels in the tree being affected more severely (Friedmann, 2001; Friedmann &

Grodzinsky, 1997; Hagiwara, 1995; Izvorski & Ullman, 1999). Although this view is

sometimes presented alongside the trace deletion hypothesis (see Grodzinsky,

2000a), there is no clear reason why two such different phenomena (i.e., trace

deletion and tree pruning) would be linked by a single brain lesion. Friedmann

(2006) has recently proposed a hypothesis of sentence comprehension that more

closely resembles the tree-pruning hypothesis. Although dissociations between

sentence comprehension and production clearly occur, an approach that has the

capacity to characterise both types of syntactic deficits in terms of a single type of

computational problem is clearly desirable. That is, although the brain regions

supporting sentence comprehension and production might be only partially

overlapping, it seems theoretically more parsimonious to propose similar syntactic

mechanisms for both processes.

Second, as discussed above, a hypothesis that accommodates a range of

impairments, rather than predicting a single, discrete pattern of performance, would

be a step forward for the field. Moreover, recent evidence suggests that individual

patients do not perform consistently on repeat testing, and that an explanation of

aphasic sentence comprehension might require the invocation of random ‘‘noise’’ in

the comprehension system (Caplan, DeDe, & Michaud, 2006; Caplan, Waters,

DeDe, Michaud & Reddy, 2007). Third, although characterisations of sentence

comprehension deficits in terms of linguistic theory are not without fault, all of the

accounts that have been put forth are insightful and seem to be presenting important

facets of language breakdown. It would therefore be useful to propose a common

(preferably computational) basis for these accounts.

Fourth, previous accounts have generally focused on syntax, without paying very

much attention to mappings from syntax to semantics (but see O’Grady & Lee, 2001,

2005; Saddy, 1995; also Thompson, Tait, Ballard, & Fix, 1999, who raise the issue

briefly). Since the patients have deficits of comprehension, and comprehension must

involve a mapping from sound to meaning, it stands to reason that some variability

in patient performance could be accounted for by giving some consideration to the

meanings themselves.

The research presented here is a demonstration of a new hypothesis with the

strengths outlined above. The hypothesis is that the probability of successfully

realising a syntactic constituent is reduced in proportion to the node’s height in the

syntactic tree. Failure of comprehension can be characterised by formally evaluating

the meanings of the constituents that are successfully realised during a parse. As

suggested by the tree-pruning hypothesis, the model (when lesioned) maintains the

ability to build syntactic structure in the lower parts of the tree, and has increasing

difficulty as higher nodes are projected. This is accomplished by assigning every

constituent a feature of integrity. This feature is meant to represent the quality of the

neural representation of that constituent (perhaps as hypothesised by Pulvermuller,

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2002), and influences the function of the parse in the following manner. If node A

dominates node B in the phrase marker tree, and the integrity of node B is low, then

the integrity of node A will be even lower. Thus ‘‘noise’’ introduced into the system

propagates as higher structures are built, leading to faulty recognition and

interpretation of large constituents. Aphasic deficits of different severities can be

modelled by introducing different quantities of noise, but with repeated testing at

certain levels of noise the model’s performance might vary between extremes that

straddle any given cut-off score that is used to distinguish normal from abnormal

performance.

METHOD

The model

The program used for this research was modified from software written in Prolog by

Blackburn and Bos (2006), available as a free download from: http://www.cogsci.e-

d.ac.uk/,jbos/comsem/book1.html). Fundamentals of computational semantic

theory and the use of the software are detailed in a textbook from the same

authors. The syntactic theory used in the book, and for this model, involved a

fundamental ‘‘definite clause’’ grammar, also known as a context-free grammar.

Briefly, such a grammar consists of a set of rules of the form:

S R NP VP (i.e., a sentence is formed by a noun phrase followed by a verb phrase)

NP R Det N (a noun phrase is formed by a determiner followed by a noun)

VP R TV NP (a verb phrase is formed by a transitive verb followed by a noun phrase)

…

In computational semantics, all words are associated with a meaning that is

expressed using a blend of lambda calculus and first-order logic. The grammar

determines which groups of words may be constituents, and how these lambda

expressions are to be combined when new constituents are recognised (i.e., which

expression should act as the function and which as the argument). Parsing of a

natural language sentence results in simultaneous formation of a sentence from first-

order logic that expresses the meaning of the natural language sentence.

The Prolog software for basic sentence parsing and interpretation was modified

by expanding the definite clause grammar to recognise 14 sentence structures of

varying complexity. Appropriate modifications were made to the semantic

component of the program to accommodate these sentences. The sentences that

were added included a subset of those that were used for aphasia assessment by

Caplan et al. (1996). An example of each sentence type is listed in Table 1.

An integrity feature was added to the feature matrix of every potential constituent

in the definite clause grammar. Individual lexical items were given a default integrity

value of 100, which endowed them with a 100% chance of being realised during a

parse. A lower integrity value corresponded to a lower percent chance that a

constituent would be realised. Although word comprehension deficits are common in

aphasia, sentence and word comprehension deficits are dissociable. Since patients

with severe word comprehension problems were excluded from the Caplan et al.

(1996) study, it seems appropriate to endow the model with accurate word

comprehension.


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TABLE 1Examples of each of the 14 sentence types

Sentence Minimal interpretation for accurate inference

Patient

data Model

(1) A boy that kissed a girl is happy. lq.’x.(GIRL(x) ‘ KISS(q,y)) [embedded verb only] – 409

(2) A boy that a girl kissed is happy. ’y.(BOY(y) ‘ ’x.(GIRL(x) ‘ KISS(x,y))) [embedded verb only] – 224

(3) A boy kissed a girl. lq.’x.(GIRL(x) ‘ KISS(q,x)) 92 328

(4) A boy was kissed by a girl. ’y.(BOY(y) ‘ KISS(,no agent.,y)) with lz.’x.(GIRL(x) ‘ z@x) 70 200

(5) A rabbit gave a cow to a goat. la.’p.(COW(p) ‘ ’g.(GOAT(g) ‘ GIVE(a,p,g))) 77 184

(6) A monkey scratched a rabbit and patted a frog. ly.’x.(RABBIT(x) ‘ ’z.(FROG(z) ‘ SCRATCH(y,x) ‘ PAT(y,z))) 69 168

(7) A frog patted a monkey and a rabbit. ly.’x.(RABBIT(x) ‘ ’z.(MONKEY(z) ‘ PAT(y,x) ‘ PAT(y,z))) 68 213

(8) A rabbit was patted. ’x.(RABBIT(x) ‘ PAT(,no agent.,x) 67 178

(9) It was a cow that a rabbit kissed. ’y.(COW(y) ‘ ’x.(RABBIT(x) ‘ KISS(x,y))) 70 239

(10) A monkey that a rabbit scratched shook a goat. ’y.(MONKEY(y) ‘ ’x.(RABBIT(x) ‘ SCRATCH(x,y)) ‘ ’z.(GOAT(z) ‘ SHAKE(y,z))) 37 58

(11) A goat hit a rabbit that kissed a cow. ’y.(GOAT(y) ‘ ’x.(RABBIT(x) ‘ HIT(y,x))) with ’x.(RABBIT(x) ‘ ’z.(COW(z) ‘KISS(x,z)))

49 134

(12) A monkey tickled a frog that a goat shook. ’y.(MONKEY(y) ‘ ’x.(FROG(x) ‘ TICKLE(y,x))) with ’x.(FROG(x) ‘ ’z.(GOAT(z) ‘SHAKE(z,x))))

42 123

(13) A frog that held a cow caught a rabbit. ’y.(FROG(y) ‘ ’x.(COW(x) ‘ HOLD(y,x)) ‘ ’z.(RABBIT(z) ‘ SHAKE(y,z))) 60 64

(14) A cow was hit by a monkey and a rabbit. ’x.(COW(x) ‘ HIT(,no agent.,x) with lw.’z.(RABBIT(z) ‘ ’y.(MONKEY(y) ‘ w@y)

‘ w@z)

65 165

Notes: Fourteen sentences for which the model’s behavior was assessed are listed in the leftmost column. The first four sentences have been considered the ‘core’ data of

agrammatism, while the last 12 of the 14 were used by Caplan, Hildebrandt, and Makris (1996) to assess comprehension in 46 subjects with left hemisphere lesions. The second

column lists the minimal amount of semantic information considered to result in accurate interpretation of each of the sentences. (For the first two sentences, only

interpretation of the embedded verb was considered, since these have been the focus of most research on aphasic sentence comprehension.) Patient data are percentages taken

from Table 1 of Caplan et al. (1996, p. 937). The model’s performance was quantified by counting the number of times that a successful interpretation occurred over a total of

420 runs of the sentence (20 runs at each of 21 levels of degradation). The ‘at’ symbol (@) represents application of a lambda expression to an argument.

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Parsing began with a query to Prolog regarding whether an input was a sentence.

Prolog performed a left-to-right, depth-first search of the space of possible sentences

generated by the definite clause grammar. That is, for each context-free rule in the

grammar (e.g., S R NP VP), Prolog evaluated whether the input matched the

constituents listed to the right of the arrow in the rule, working from left to right as

long as each constituent was found in the input. If the first constituent was

recognised, Prolog would move on to the second constituent. Recognition of each

constituent usually involved satisfaction of additional context-free rules beforeProlog would move on to the next constituent. (This is why the order of evaluation is

considered to be ‘‘depth-first.’’ See the Appendix for an example.) In order to model

failure of comprehension, the parser was designed to occasionally reject con-

stituents that should have been permitted by the grammar. The chance that such a

rejection would take place at any given node in the tree was proportional to

the height of the node in the tree, and was determined by the integrity value for

that node. Each time the parser made an attempt to realise a constituent, the

integrity value of the new constituent was derived by selecting the lowest integrityvalue from among its daughter nodes and subtracting a constant (degradeconst).

Therefore, to put together an NP from the determiner ‘‘a’’ and the noun ‘‘girl’’,

the integrity value for each word would be accessed and compared. Since single

words always had an integrity of 100, the number 100 would be used to derive the

integrity for the new NP by subtracting degradeconst. If this constant were equal to

2, the integrity of the new NP would be 98. A random number between 1 and 100

was then selected and the constituent was accepted only if the random number was

lower than the integrity value for the constituent. Since only single words wereassigned integrity values initially, integrity values for all other nodes had to ‘‘trickle

up’’ the tree from the level of the word nodes. Aphasia severity was modelled by

varying the value of degradeconst. The Appendix contains a simplified trace of the

steps undertaken by the model when parsing a simple active sentence with

degradeconst set to 2.

Whenever the parser identified a constituent (any constituent, even a single word),

a semantic representation was computed for that constituent. For constituents

composed of two sub-constituents, this amounted to taking the lambda expressionassociated with one daughter node and applying it to the lambda expression for the

other daughter node. For every constituent the parser identified, the semantic

representation that was computed was written to a text file for later quantification.

Thus, even if the model had failed to parse an S from the input, it still might have

parsed the initial NP or the VP and the semantic representation for these

constituents would have been written to the file.

Procedure and analysis

The program was run 20 times for each sentence, with degradeconst set to each of 21

different values (0–20), for a total of 420 runs for the input sentences listed in

Table 1. The first four sentences listed have been considered to represent the ‘‘core’’

data of agrammatism (Dick et al., 2001; Hickok & Avrutin, 1995). The latter 12 of

the 14 sentences represent structures used by Caplan et al. (1996) for assessment of

46 patients with left hemisphere lesions.

Strings representing the semantic interpretations of certain key fragments ofsyntactic structure were counted in each output file. Some of these strings were


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completely intact semantic interpretations of the input sentence. However, since

degradation of the model’s function resulted in incomplete parses, some attention

had to be directed to the quality of semantic interpretations of these incomplete

parses. Therefore, some a priori stipulation had to be made regarding the minimum

quantity of information that should lead to correct performance on tasks assessing

sentence comprehension. The definition of this minimum quantity of necessary

information was motivated partly by linguistic and partly by extra-linguistic

considerations. From a linguistic standpoint, Friederici and Gorrell (1998) notedthat when patients with typical agrammatic comprehension provide judgements of

sentence meaning, they exhibit a tendency to assign the Agent role to the highest NP

in the sentence structure. However, this behaviour is indistinguishable from the

behaviour one would see if aphasic participants assigned the Patient role to the lower

NP in the structure. Expressing Friederici and Gorrell’s observation in terms of the

lower NP is important to the interpretation of the model’s behaviour, since lower

NPs are more likely to be incorporated into the structure and thus to be available for

compositional interpretation.From an extra-linguistic standpoint, I presume only that aphasic participants

have an intact capacity for making two simple deductions. Deductive processes and

semantic world knowledge are known to influence sentence comprehension in

agrammatic participants, since these participants perform ‘‘above chance’’ when

interpreting semantically irreversible sentences, even if they are non-canonical (e.g.,

‘‘The apple was eaten by the boy.’’) The first of these two simple deductions is as

follows: if an aphasic listener gleans the following facts: (1) the speaker said

something about a boy, and (2) the speaker said someone hit a girl, then the listeneris very likely to infer that the boy was the one who did the hitting. Along with

Friederici and Gorrell’s observation, the capacity to make this inference raises the

possibility that a noun that has not been assigned a thematic role through normal

compositional mechanisms might be interpreted as the Agent of the event. For

purposes of this research, the model was considered to make such a deductive leap

under one circumstance: when the available semantic information included (1) a

lambda term for one N or NP that had not been assigned a thematic role (including

NPs composed of conjoined NPs), and (2) a VP (including those composed ofconjoined VPs) with one unassigned thematic role or undischarged lambda term. It

should be noted that such an inference leads to a correct interpretation only for

canonical sentences. The second deduction that patients might be expected to make

is even simpler. If a patient is able to interpret an utterance as containing an

assertion P and an assertion Q, then the patient can infer that the utterance contains

the assertion P & Q. The ability to make this deduction would facilitate the

interpretation of non-truncated passives and of sentences with relative clauses in

object position, but not those with relative clauses in subject position. This is due tothe fact that verbs in the former sentence types have a more local relationship with

their arguments than verbs in the latter type (see Table 1).

Performance of the model across sentence structures was evaluated. For the

first four sentences of Table 1, thematic role assignments were investigated in

detail and graphed. The model’s performance on these canonical and noncano-

nical sentences was compared with chi-square. The model’s performance on the

12 sentences from Caplan et al. (1996) was quantified for direct comparison to

the patient data. Following visualisation with a scattergram, a correlation wascomputed.

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The influence of random noise on the model’s performance was evaluated by

running three sentences 20 times each for 10 consecutive runs with degradeconst set

to 8. The three sentence types were simple active, truncated passives, and full

passives. These structures were used by Caplan et al. (2007) as baseline and test

sentences. Performance by the model over the 10 runs was graphed.

RESULTS

Patterns of performance by the model closely matched those of actual patients on

the four sentences that constitute the ‘‘core’’ of agrammatic comprehension data.

The model performed relatively well (328/420 sentence presentations) with active

voice sentences. Figure 1 shows that the model generated rather few complete

parses, but performed somewhat better at generating the ‘‘minimally sufficient’’

parse at each level of noise. For this sentence structure, a partial parse in which the

VP was given an interpretation was considered to be minimally sufficient. The

model did not perform as well with passive-voice sentences (200/420 sentence

presentations). As can be seen in Figure 2, the model did not generate very many

complete parses. When the model’s performance was quantified according to the

number of minimally sufficient parses, it fared somewhat better. For passives, the

minimally sufficient parse included the parse of the first part of the sentence

(identical to a truncated passive) and a lambda term for the agentive by-phrase. The

figure also depicts the number of parses, at each level of degradation, in which the

verb and agentive by-phrase were parsed together without integration of the subject

NP. In the absence of the minimally sufficient parse, these were counted as failures,

Figure 1. The model’s performance (vertical axis) with simple active sentences at each of 21 levels of

degradation (horizontal axis). Solid line (*): complete parses, with resulting complete semantic

interpretations. Dashed line marked (o): minimally sufficient interpretations, in which the parse contains

a VP with the verb and the patient NP.


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due to the axiom that VPs with only one argument would be interpreted as verb +Patient.

The object-relative and subject-relative sentences used to compare the model’s

performance to the core data of agrammatism had only one transitive verb (within

the relative clause), while the matrix clause contained a predicate adjective. Although

comprehension (i.e., generation of an adequate semantic representation) of the

matrix verb was poor for both of the sentence types at higher levels of degradation,

comprehension of the transitive verb in the relative clause varied in the same mannerthat was observed for active voice and passive voice sentences. Measured according

to performance with the relative clause verb, the model performed relatively well

with subject-relative sentences (409/420). Figure 3 shows the model’s performance,

quantified over 20 iterations at each of 21 levels of degradation. Again, across all

levels of degradation, the model did not generate many complete parses, but often

formed a parse containing the minimally sufficient information for accurate

performance, i.e., at the very least a VP with the embedded verb and its Patient

NP. The model generated parses associated with adequate interpretations for 224/420 object-relative sentences (Figure 4). For these sentences, the model generated

even fewer complete parses and minimally sufficient parses. For object-relative

sentences, a parse was considered minimally sufficient if it contained the embedded

verb and both of its arguments. The figure also depicts the number of parses in which

the embedded verb was parsed with only the lower NP, resulting in faulty

interpretation of this argument as Patient. When the model’s semantic interpreta-

tions were judged as adequate or inadequate on these grounds, the model was

accurate on 87.7% of canonical sentences, but only on 50.5% of non-canonicalsentences (x25271.4, p,.001).

Figure 2. The model’s performance (vertical axis) with passive voice sentences at each of 21 levels of

degradation (horizontal axis). Dashed line (o): complete parses, with resulting complete semantic

interpretations. Dash-dot line (*): adequate interpretations, i.e., either a complete parse or a parse with

both truncated passive and the agentive by-phrase. Solid line (D): Incomplete parses containing a VP with

the agentive by-phrase but without the subject (Patient) NP.

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The model was tested on 12 of the sentence types used by Caplan et al. (1996),

some of which contain ‘‘empty’’ referential dependencies. The sentences with relative

clauses used for this part of the experiment contained two transitive verbs. The task

employed by Caplan et al. (1996) required enactment of each sentence with stuffed

animals or puppets. This task should have been quite unforgiving, since an error with

either verb would have interfered with the participant’s enactment of a given

sentence. For these sentences, only a parse in which all thematic roles were correctly

assigned was considered to be adequate for comprehension by the model, since the

aphasic patients were required to comprehend both verbs. It is important to note

that for sentences with a relative clause in the subject NP this required a complete

parse, but for sentences with a relative clause in the object NP it was sufficient to

have two simultaneous parses, each of which contained a semantic representation of

one of the transitive verbs along with appropriate thematic role assignments (see

Table 1). The derivation of an accurate interpretation from these two simultaneous

partial parses requires the use of the second of the two extra-linguistic deductions

described in the Method section. The correlation between the model’s performance

and patient performance was significant (r5.85, p,.001; see Figure 5). The

correlation between patient performance and sentence length was not significant

(r52.52, ns).

Repeated testing of the model at a single level of degradation revealed overlap

between quantifications of the model’s performance on active and passive voice

sentence structures. Specifically, the model’s best performance with full passives was

better than its worst performance with actives, and its best performance with

Figure 3. The model’s performance (vertical axis) with subject-relative sentences at each of 21 levels of

degradation (horizontal axis). Solid line (o): complete parses, with resulting complete semantic

interpretations. Solid line (*): complete interpretations of the embedded verb, regardless of the matrix

verb interpretation. Solid line (D): minimally sufficient interpretations. For subject relatives with predicate

adjectives in the matrix clause, this requires formation of a parse containing the embedded verb with the

appropriate Patient argument.


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Figure 5. A scattergram depicting the strong linear relationship between the model’s performance

(horizontal axis) and patient performance (vertical axis) on 12 of the sentence structures used by Caplan

et al. (1996). The coefficient of correlation is .85 (p,.001).

Figure 4. The model’s performance with object-relative sentences (vertical axis) at each of 21 levels of

degradation (horizontal axis). Dashed line (o): complete parses, with resulting complete semantic

interpretations. Dash-dot line (*): minimally sufficient interpretations. For object-relatives these result

either from a complete parse or a parse in which the embedded verb is associated with both the Agent and

Patient arguments. Solid line (D): incomplete parses in which the verb was paired with only one argument.

Although this argument should have been interpreted as an Agent, since no other argument was available

it was instead interpreted as Patient.

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truncated passives was better than its worse performance with full passives

(Figure 6). Importantly, on one run the model performed at the above chance level

(marked by the horizontal dotted line) on full passives, while on the next run

performance on actives was within the at chance range.

DISCUSSION

The model presented here demonstrates that patterns of sentence comprehension in

aphasia may be explained in part by a parsing impairment. Rather than considering

the parsing impairment to be ‘‘all or nothing’’ or to occur at a specific level in the

phrase marker tree, I propose that there is a reduction in the probability that each

constituent will be accurately realised, and that this reduction is proportional to the

node’s height in the tree. Examination of the semantic representations associated

with the constituents that are accurately parsed reveals patterns that bear a strong

resemblance to those observed in aphasic patients.

The explanation this model offers for understanding aphasic sentence compre-

hension is that for certain incomplete parses, the available semantic representation

contains a transitive verb with only one filled thematic role. This thematic role is

filled by the lowest NP in the syntactic structure. I propose that patients commonly

infer that this argument is the Patient of the action, and that this inference occurs

due to the overabundance (in English) of sentences in which the first argument of the

verb (the lowest NP) receives the Patient role. This inference is correct for active

voice sentences and incorrect for passive voice sentences. A similar pattern is

observed for the embedded verb of subject-subject and subject-object relative

Figure 6. A graph of the model’s performance on 10 runs (x-axis) of 20 consecutively presented simple

actives (o), full passives (n), and truncated passives (*). The y-axis represents the number of times an

accurate interpretation was formed on each run. The dashed line represents the level at which performance

is significantly above chance (chi-square, p,.05). Although the use of random noise in the model causes its

performance to vary from run to run, the model’s average performance with the three sentence structures

reflects the same difficulties seen among aphasic patients (truncated passives . full passives . simple

actives).


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sentences. Since the task employed by Caplan et al. (1996) involved enacting two

events described by transitive verbs in sentences with relative clauses in the subject

(one embedded verb and one matrix verb), participants would need to form a

complete parse of these sentences in order to fill all the thematic roles, and only

complete parses by the model were counted as successful. In contrast, piecemeal

comprehension of sentences with relative clauses in an object NP seems feasible,

owing to the fact that both verbs have local relationships with their arguments, while

the matrix verb in subject-subject and subject-object relative sentences isdisconnected from its agent argument by the intervening relative clause.

There is increasing evidence in the psycholinguistic literature that normal listeners

rely on a combination of ‘‘heuristic’’ strategies and strict compositional interpreta-

tion (Ferreira, Bailey & Ferraro, 2002; Ferreira & Patson, 2007), and that these

processes might compete during normal sentence interpretation, often leading to

sentence misinterpretation. Although the authors refer to the heuristic processes as

being non-compositional, it is clear that the meanings derived through the heuristics

are composed from meanings of words in the sentences. The meanings are only non-compositional in the sense that listeners do not derive them from a global syntactic

structure, but instead rely on other cues to derive thematic relationships. These other

cues include semantic background knowledge, anticipated argument structures, and

locality of verbs and potential NP arguments. These findings are relevant to the

study of aphasic sentence comprehension, and provide a preliminary explanation for

Friederici and Gorrell’s (1998) Structural Prominence Hypothesis: the observation

that aphasic listeners assign the Agent role to the most structurally prominent NP.

For purposes of this model I have rephrased the structural prominence hypothesis asa tendency to assign the Patient role to the lowest NP, since these two

complementary cognitive operations cannot be dissociated on the basis of the

behavioural data. In terms of the work by Ferreira and her colleagues, this tendency

arises from occasional failure to compute a global syntactic structure for complete

compositional interpretation. In the absence of the complete global syntactic

structure, other cues (such as string locality) prevail in thematic role assignment.

Perhaps this competition between local and global compositional processes will

prove to be useful for understanding the apparent greater difficulty of non-canonicalsentences, as measured by fMRI (Cooke et al., 2001), and for understanding

conflicting observations regarding structural vs linear thematic role assignment

(Beretta, 2001; Law, 2000).

The main reason for adopting this approach to explaining features of aphasic

sentence comprehension is that the lesion introduced may be characterised in neural

terms, i.e., as reduced priming, activation, or coherence in a neural sequence detector

that supports syntactic (and possibly semantic) processing (Pulvermuller, 2002). For

this reason, the approach may be considered to be similar to those that attributesentence comprehension deficits to a deficiency in some kind of resource (Caplan &

Waters, 1999; Haarman et al., 1997). However, it is important to note that certain

aspects of sentence complexity (particularly with regard to relative clauses) are

merely stipulated by the current model. Functional imaging research indicates that

the processing of object-relative sentences is associated with larger increases in left

frontal blood flow than the processing of subject-relative sentences (Cooke et al.,

2001; Just, Carpenter, Keller, Eddy, & Thulborn, 1996). It is reasonable to infer that

these differences relate to the dissociations seen among aphasic patients.Syntacticians often give more complex derivations to object-relative sentences. For

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example, in at least one variant of transformational grammar, object-relative clauses

contain two traces, while subject-relative clauses contain only one (Hickok et al.,

1993). Combinatory categorial grammar (Steedman, 2001) requires type-raising and

function composition operations in the derivation of object-relatives that are not

necessary for subject-relatives. The grammar used by this model is no different, since

parsing of an object-relative is associated with introduction of a ‘‘gap’’ that adds a

computational step to the derivation. Thus, this model’s performance with object-

relative sentences arises in part from their greater ‘‘complexity’’, and in part from thefact that accurate interpretation of the object-relative clause requires assimilation of

a greater number of words into a single constituent. Yet the model does not include

any explanation (in neural terms) for the greater complexity of object-relative

sentences. Thus, it is possible that the variable manipulated in this model does not

represent all of the factors that result in differing degrees of difficulty with sentence

interpretation. Perhaps more of the variation can be accounted for with the addition

of one variable representing a form of working memory, possibly implemented as an

activation level for individual lexical items (or their meanings) that undergoesspontaneous decay. On the other hand, this is hardly conceptually different from the

‘‘integrity’’ value manipulated by current model—the main difficulty lies in

developing a grammatical analysis that hasn’t already incorporated the fact that

object-relative sentences are more difficult to process.

There are several other reasons to adopt this approach to understanding aphasic

comprehension deficits. First, since the constant of proportionality (degradeconst)

that determines the reduction in the parser’s accuracy at each node can be varied, it is

possible to account for many degrees of sentence-level comprehension impairment.Second, the model provides a first approximation for conceptualising the interaction

between global and local processes of sentence interpretation. The hypothesis is

therefore aligned with previous hypotheses that posit a structural interpretation

strategy—notably the Structural Prominence Hypothesis of Friederici and Gorrell

(1998) and the Double Dependency Hypothesis of Mauner et al. (1993). In addition

to compositionality, however, the possibility of using ‘‘good-enough’’ representa-

tions or simple logical deductions in the absence of a globally correct parse was taken

into account while quantifying the model’s performance. Third, defining the defect interms of recursive processing (i.e., either tree structure building or systematic

recombination of semantic representations) opens the possibility of unifying

characterisations of agrammatic comprehension with those of agrammatic produc-

tion (cf. the tree-pruning hypothesis of Friedmann). A final reason to use this

approach is that it may be used to make predictions of patient performance on other

sentence structures, such as active and passive voice sentences with coordinated

subjects or sentences with adjectives or other modifiers.

This research has a number of limitations. Hickok et al. (1993) have reported thataphasic patients often mis-assign the Agent role of verbs in the matrix clause of

subject-subject relative sentences. That is, given sentence (5), patients believe that the

girl is the one who is angry. The current model could not produce this parse due to

the highly constrained nature of the definite clause grammar. A grammar permitting

a more parallel approach to parsing (such as a lexicalist grammar) could derive this

parse as well as its resulting semantic interpretation. The current model was not

designed to address the fact that agrammatic patients exhibit the relatively preserved

capability to detect many grammatical errors (Linebarger, Schwartz & Saffran,1983). However, more recent evidence suggests that the dissociation is not as strong


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as once believed (Wilson & Saygın, 2004). The proposal that this dissociation arises

from an interaction between the relative utility and cost of interpreting cues (Dick

et al., 2001) is an important idea that may be integrated into future models. It is

important to note that the current model’s simple definite clause grammar does not

take into account some aspects of sentence processing that might be very important

for defining agrammatic comprehension, such as tense and agreement morphology.

These additional layers of recursive structure might be important for truly explaining

details of aphasic sentence comprehension (Friedmann, 2006). This model was not

designed to address aspects of agrammatic comprehension that have only begun to

be explored, such as the interpretation of wh-questions and quantifiers (Hickok &

Avrutin, 1995; Saddy, 1995; Thompson et al., 1999). However, it might be better to

approach such problems with the tools of computational semantics rather than with

those of syntax alone. This work is limited in scope to the interpretation of roles for

transitive verbs that take Agent and Patient arguments. However, some transitive

verbs take arguments with other thematic roles, such as Experiencer, Range, or

Theme.

Future work may address the question of whether the extra-grammatical deduc-

tive processes outlined here might lead to specific (correct or incorrect) assignments of

these other thematic roles. Finally, the current work pertains only to agram-

matism in speakers of English. Although one might expect the model to continue to

behave in accordance with Friederici and Gorrell’s Structural Prominence Hypothesis,

this will require empirical verification with other languages. Since the Structural

Prominence Hypothesis was motivated by observations of aphasia in speakers of

German, Japanese, and Dutch, attention will be directed next to these languages. The

availability of other cues in these languages, particularly richer grammatical

morphology, might reduce the impact of syntactic hierarchy on sentence interpretation

(Bates et al., 1987)—nevertheless, it is likely that syntactic structure will continue to

have greater explanatory value than raw word order or sentence length.

In summary this is a probabilistic hypothesis, couched in terms of computational

semantics, for the occurrence of sentence-level comprehension deficits in aphasia.

The hypothesis proposes that a defect of syntactic structure building propagates as

information is recombined, resulting in failure to integrate non-local syntactic

information into a unified representation that permits complete semantic inter-

pretation. The effects of brain damage were modelled by systematically reducing the

probability that a constituent would be realised in proportion to the constituent’s

height in the phrase marker tree. The model’s output closely resembles data from

patients with left hemisphere lesions. Further work is required to assess the utility of

this approach for languages other than English, especially those with richer

morphology.

Manuscript received 30 May 2007

Manuscript accepted 30 August 2007

First published online 27 November 2007

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APPENDIX

This is a simplified trace showing the major steps that Prolog would take while

parsing a simple active sentence. The answer to any given question posed during the

query might depend on the answers to other questions, which are written (indented)

beneath the question that depends on them. For purposes of this demonstration, the

following definite clause grammar is assumed:

S R NP VPNP R Det N VP R V NP

Det R a N R girl, rabbit V R chased

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It should be noted that if degradeconst had been higher, the integrity values wouldhave been lower. For example, if degradeconst had been 4, the integrity values would

have been 96, 96, 92, and 88. In addition, if the final random number generated

during this simulation had been any higher, the overall parse would have failed,

although semantic representations for the NP and VP would have been saved to the

output file.

Question asked by the parser

Integrity of

constituent Random number

Is the input an S?

Does the input begin with an NP?

Does the constituent begin with a Det?

Does the constituent begin with the word ‘‘a’’?

Yes, the constituent begins with the word ‘‘a’’. 100

Is the next word an N?

Is the word ‘‘girl’’?

Yes, the word is ‘‘girl’’. 100

What is the integrity of the putative NP? 10022598

Is a random number between 1 and 100 lower than the

integrity value?

32

Yes, the random number is lower.

Yes, the input begins with an NP.

Is the next constituent a VP?

Is the next word a V?

Is the next word ‘‘chased’’?

Yes, the word is ‘‘chased’’. 100

Is the next constituent an NP?

Does the next constituent start with a Det?

Does the next constituent start with ‘‘a’’?

Yes, the constituent starts with ‘‘a’’. 100

Is the next word an N?

Is the next word ‘‘girl’’?

No, the next word is not ‘‘girl’’.

Is the next word ‘‘rabbit’’?

Yes, the next word is ‘‘rabbit’’. 100

What is the integrity of the putative NP? 10022598

Is a random number lower than the integrity value? 71


Yes, the constituent is an NP.

What is the integrity of the putative VP? 9822596



Yes, the second constituent is a VP.

What is the integrity of the putative S? 9622594



Yes, the input is an S.


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Documents

A computational semantics approach to aphasic sentence comprehension