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Abstract:
The frequency with which particular verbs are used
in different syntactic contexts has been shown to be an important
factor in modelling on-line processing performance (Trueswell,
1996; Garnsey, et al., 1997, among others). The standard
methods for assessing such frequencies have been either
sentence-completion studies or counts collected from large
natural-language corpora. This paper presents a new
methodology for assessing the frequency with which verbs are used
in differing syntactic frames, argument structure estimation, and
compares it with a standard methodology, sentence
completion.
Two groups of participants drawn from the same
population were asked to assess the transitivity bias of
optionally transitive verbs like "type" and "phone". The
two groups performed different tasks. The first group
were presented with sentence fragments containing the verbs and
asked to complete them in a standard sentence completion
task:
When Alex phoned ...
The second group of subjects were presented with a
list of verbs and asked to estimate "how many phrases" each verb
occurred with in its most frequent use. Relevant phrases
were defined as those describing "actors and entities that play a
role in the event the verb describes." They circled the number of
phrases on a scale from 1 to 4. In both tasks, the target
verbs were interspersed among a number of fillers with
varying argument structures.
Correlation analyses performed on the resulting
means showed that the two measures were strongly correlated for
verbs with mid to high token frequency in the CELEX database:
r[2]=0.751 for verbs with past-tense token frequencies between
1000 and 2000, r[2]=0.608 for verbs with a token frequency of
2000 or more. However, the two measures were relatively
poorly correlated for verbs with low token frequencies:
r[2]=0.302 for verbs with frequencies of 200 or less.
Regression analyses revealed that variation in the sentence
completion norms was the source of this discrepancy. Token
frequency was a reliable predictor of sentence completion values
(p<.02), but not argument structure estimates
(p<.5).
This comparison indicates that while norms drawn
from the two methodologies are highly correlated, sentence
completion norms may be more strongly influenced by the token
frequency of the verbs being rated than argument structure
estimation norms, particularly at low frequencies. This in
turn suggests that argument structure estimation may provide more
stable estimates of syntactic frame preferences, especially when
looking at verbs with very low token frequencies.
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