Syntactic Complexity Measure?
plunkettbernadette at gmail.com
Sat Jun 13 13:22:11 UTC 2015
once you have found a good measure, do you have in mind what you are going
to measure it against? Does anyone know whether such measures have been
applied to the speech of the adults in some of the child corpora, for
example? I know some people will be of the view that the child directed
speech is simpler than normal adult speech, but it would provide some
degree of measure and looking at some of my own data the adults speaking to
three and four year olds are using pretty sophisticated syntax.
Sophisticated though it may be, it doesn't however, always include alot of
embedding so I don't know how representative simply counting dependent
clauses would be.
I'd be happy to hear what measures you fix on once you've experimented a
On 13 June 2015 at 09:44, RSteinkrauss <r.steinkrauss at web.de> wrote:
> Dear Kim,
> in our spoken data of elderly L1 attriters and L2 learners, we
> hand-coded a specific syntactic complexity tier for information such as
> clause type, number of finite and non-finite verbs, and noun phrase
> length tier to measure syntactic complexity because the measures we were
> interested in were not directly supported by CLAN. We based our choice
> of measures on Bulté and Housen's and Norris and Ortega's work; you
> might want to look into their findings.
> Regarding your data, a relatively simple way to get at two widely-used
> syntactic complexity data would be to proceed the way described in the
> material you cited and additionally introduce a tier where you code for
> every clause whether it is a independent or dependent clause. Using CLAN
> to calculate the number of dependent and independent clauses in each
> transcript as well as the total number of words in each transcript would
> then allow you to calculate the average length of a T-unit in words
> (total words/number of indep. clauses) and a kind of subordination ratio
> (dependent/independent clauses).
> However, I don't know if these measures are appropriate for relating
> them to the risk of AD. Also, since you are dealing with spoken data,
> I'd recommend looking into AS- instead of T-units as your unit of
> analysis (see Foster's work).
> Rasmus Steinkrauss
> Am Freitag, 12. Juni 2015 16:13:21 UTC+2 schrieb Kimberly Mueller:
>> Dear All,
>> Kimberly Mueller here from UW Madison, using CHAT/CLAN (thank you!!!) to
>> transcribe 5-10 minute language samples from adults ages 40 - 80 who are at
>> risk for developing Alzheimer's Disease.
>> Would anyone (and/or everyone!) please provide guidance on measures you
>> recommend to capture syntactic complexity? We have been segmenting
>> utterances using the guidance in the CHAT manual (see below). Any help
>> with codes/commands would also be appreciated!
>> Many thanks,
>> Utterance Manual for CHAT/CLAN
>> *Utterances in CHAT/CLAN are separated using a T-unit classification. *
>> *A T-unit consists of an independent clause and its corresponding
>> depending clauses. *
>> *An independent clause includes a subject and a verb. *
>> *A dependent clause provides additional information to an independent
>> clause, but it cannot stand by itself. *
>> *For example, if you were transcribing “I went to the store, but I didn’t
>> find anything to buy,” you would separate this into two utterances in
>> CHAT/CLAN. *
>> **PAR: I went to the store. *
>> **PAR: but I didn’t find anything to buy. *
>> *Even though these two parts go together in a sentence, they would be
>> separated into two utterances per the T-unit classification. *
>> *An example of a dependent and independent clause would be as follows: *
>> **PAR: If I show up late the teacher will give me a tardy. *
>> *In CHAT/CLAN, you also need to make a few judgement calls. *
>> *For example, in speech, we often start sentences with “because.”
>> “Because” is typically at the beginning of a dependent clause in written
>> communication; however, we use this to start sentences when speaking and
>> thus a transcriber needs to decide whether the “because” is actually
>> starting the sentence in a spoken utterance. *
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