38
independent variables. The coefficient B, standard error S.E, odds ratio expB, and 95 CI of the linguistic features were basically obtained from the
analysis of the numbers in the SPSS. This kind of analysis belongs to the domain of quantitative research. This fact confirms one of the characteristics of Corpus
Linguistic Study suggest by Biber 1998. In addition, the research also belongs to the domain of probabilistic
grammar. Theoretically, the research adopt the idea of Bresnan 2007 to apply a dynamic probabilistic grammar Bybee Hooper 2001; Bod
et al.
2003; Gahl Garnsey 2006; Gahl Yu 2006 to the domain of syntactic variation. Besides
aiming at finding the significant features which are relevant to the choice of benefactive, this research also tried to predict the occurrences of certain instances
based on the features found. Given the coefficients of the significant features, the probabilistic model was able to predict what construction tends to be used by the
speakers.
3.2 Research Data
The data of the research was taken from Corpus of Historical American English
COHA which
can be
accessed through
web page
http:corpus.byu.educoha. The corpus was created in 2007 by Mark Davies, a linguistics professor at Brigham Young University with funding from US National
Endowment for the Humanities. The corpus provided 400 million words of text of American English and covered the period of time from 1810 to 2009. The
diachronic data is taken as it presents the use of the benefactive constructions during years so that any possible change of usage is covered. The idea of taking
39
the data from this fairly long range of years refers to Colleman 2007 who claims that the semantic range of ditransitive seems to have narrowed rather than
broadened over the years. In addition, Colleman states that some ditransitive verbs disappeared from Late Modern English period, or are on the decline in terms of
frequency and or lexical possibilities. These facts tempted the researcher to take the diachronic data in the present research. However, in benefactive cases
especially in the verbs chosen by the researcher, the phenomenon as in Colleman‟s does not exist.
The instances in COHA corpus were derived from fictions, new fiction books, newspapers and magazines back from year 1810 until 2009. Thus, the data
in the corpus represented the real usage of the benefactive construction. The instances of the COHA corpus were assumed to correspond to the underlying past
variation and present-day probabilistic grammar. The corpus consisted of keyword in context display, yet there was no automatic input to obtain benefactive
construction. The COHA corpus was chosen because it is new and relatively unexplored
yet it provides the range data from 1800s up to 2000s which rarely covered by others corpus machine. The wide range of the data in TIME corpus facilitated the
richer data of benefactive construction and was expected to provide more valid data with diachronic stability. The data over the years was explored to find the
features affecting the benefactive construction and was then used to predict the occurrences of instance in other data source or real context, in general.
In addition, COHA corpus gave some helping menus which made it easy to generate. The queries could be done by inputting word, phrase, alternates,
40
substring, part of speech, lemma, synonyms, and customized lists. The corpus also provided chart listings, which showed totals for all matching forms in each genre
and sub-genre or year, and table listings, which showed the frequency for each matching form in each genre or year. The special syntax query was also enabled in
this corpus. The query like part of speech [pos], lemma all forms of a word, e.g. [sing], synonym [=word], e.g. [=strong], and other combinations of those syntax
queries. The researcher can combine word with part of speech, e.g. strike.[v] or lemma with part of speech, e.g. [strike].[v]. The first combination will provide
the instances with
strike
only as a verb, while the second will provide the instances with many forms of
strike
as verb. So when the researcher wanted to find instances with all forms of the verb
get,
the syntax query put into string will be [get].[v].
To help checking the model accuracy, instances from other corpus data set were employed. The data to check external validity and to check the cross-model
benefactive-dative accuracy was taken from TIME magazine corpus compiled by Mark Davies 2006. The corpus consisted of 100 million words and can be
accessed through web page http:corpus.byu.edutime. The corpus provided the range of the data from 1923 until 2006. It gave advantage as the data from years
was equally comparable to the data set from COHA. The process of collecting the data was started by sorting out the
benefactive ditransitive verbs and classifying them into 4 clusters of semantic categories: the verbs carrying meaning
„make available‟, „creation‟, performance‟, and
„preparation‟. The first semantic class was represented by the verb
get.
The
41
second semantic class was represented by the verb
make.
The third class was represented by the verb
play.
The last class is represented by the verb
fix.
The second step was extracting the benefactive constructions from the corpora. The data was manually selected from the occurrences in the Corpus
BYU, the main domain of COHA corpus on February 10, 2013. The four classes of verb were put into the corpus string to get the instances. To obtain all forms of
the verbs, special syntax query was used. In order to get all forms of the second verb class
make
for example, the entry to the string was lemma and part of speech. The formula was [lemma].[part of speech], thus in this case was [make].[v].
When this syntax query was put into search string, the corpora provided all the instances containing the verb make in many forms. The instances with verbs
make, makes, making, made, and maketh were obtained. The corpora automatically presented the occurrences of each verb form along with the ratio.
The research planned to use 400 instances as the data set, so 100 instances were taken from each semantic class. To get 100 instances from each verb class, the
instances were selected manually. The instances were selected from each 100 occurrences of the corpora data until 100 benefactive constructions of each
semantic verb class were obtained. This selection process differed from each semantic class due to the different and relatively low occurrences of every verb
class. The verb
make
for instance, has only 127 occurences per-ten-thousand- words
pttw.
When 100 benefactive constructions were obtained, the process proceeded to the next semantic verb class. The same steps were done to the rest of
the semantic class verbs.
42
The third step, the researcher rechecked the relevance of the 400 instances taken as data set of benefactive alternations. All the extracted instances were then
rechecked and judged for their relevance. When an irrelevant instance to benefactive construction was found, the instance was left out, and the researcher
gathered the replacement for the instance straight away. For instance, in example 1, the benefactive PP
for my birthday
has the meaning of
on the occasion of
or
at the time of
instead of benefactive meaning.
1
licked me back to life. That cheered me up. What did you get me
for my birthday
? I asked. She started to chew on my Such kind of doubtful instances were checked several times and when the final
decision about relevance was made, the fix data set was obtained. During this third step, the researcher decided to exclude some instances
with special syntactic and semantic phenomena in order to minimize random effect of the grammatical patterns. The first pattern namely non-canonical
constituent orderings Bresnan, 2007 was excluded from the analysis. In some cases, the benefactive verbs are used in benefactive constructions that do not seem
to match either benefactive PP or double object construction.
2 rest of the verse: For all her household are clothed with scarlet She
maketh
for herself carpets of tapestry
, Her clothing is of fine linen and purple...
3
While on the silver willow in the grove I hang my harp -- olia play
for me A farewell song
, when this sad life shall cease to be 4
roads tax the people in their territory 155.000,000 a year for
transportation. This pool fixes
for each of these roads the rates
which it shall charge and the proportion of
43
5
and it does not carry more to me than that one line. But it fixes for me a
time in all of our lives. It was in October of 6
-- in crowds -- serene -- or sad -- In shade or shine -- they play [ ]
for thee
I too last evening joined the throng, I too beheld in 7
he said. When we go out and play a show, I play [ ]
for myself
. Its an outlet. Some people like to garden. I
The heavy noun-phrase shift and the reverse double object construction in 2, 3, 4, and 5 and the dimonotransitive constructions as in 6 and 7 were excluded
from the analysis due to their rarity and special non-canonical constituent orderings. The heavy NP shift put the preposition in the theme-beneficiary
realizations, while the dimonotransitive construction neglected the theme. The factors determining this kind of variability are beyond this scope of the study.
The second pattern which was excluded from the analysis was passivization. The construction put the theme in pre-verbal position, and it is
predicted to be problematic when included in the analysis. In this theme- beneficiary construction, the theme was indeed preceding the beneficiary, yet as
its position is pre-verbal it is hardly comparable to the usual post-verbal them- beneficiary construction.
8
The preceding day
had been fixed
for our return
to our fathers house, but a storm occurred, which
The third phenomenon which was put aside in this research is the bias effect of verb sense. An individual verb representing the semantic class „make
available‟
get,
for instance carry some verb senses. Examples are given in 9. 9
a. get =
get
in obtaining or earn something:
gets forty thousand dollar a year for her family
44
b. get =
get
in preparing something :
getting the dinner ready for the kids.
c. get =
get
in abstract sense :
get that a lot.
After re-checking, omitting, and re-selecting a hundred instances from each verb representing a semantic class, four hundred instances in total were
obtained. Those four hundred instances become the data set for this research.The data set of the benefactive constructions is presented in Appendix 1.
3.3 Data Analysis
