Homework Assignment 1:
Select a dissertation area. You might want to
pursue two in
parallel. This should be at a fairly high level initially.
As you pursue the area in greater depth, you will refine the
topic. Write a brief description of the topic (less than one
page). Make the appropriate entries in your notebook. Put
the written piece on your webpage in HTML please and send me
the URL for posting.
I am interested in computational linguistics. Until I am aware of the topic sufficiently, I intend to pursue two possible paths. Professor Gustavson has helped me with suggested sources for literature research. I have recently purchased Endnote 4.0 for windows and am using that software as my central repository for all research documentation. However, I will still use the notebook to record my thoughts related to possible topics.
Possible Dissertation Topic 1
Automated Writing Maturity Program. Since graduate school, I have pondered the possibility of automating the process of evaluating English compositions. Some programs have succeeded in identifying one or two basic attributes of compositions, assigning some numerical result, and interpreting the result as an indicator of writing maturity. Unfortunately, most such programs do not even begin to reflect the overall assessment of human graders. This problem (being unable to emulate the subjective overall assessments of human graders for English compositions) strikes me as a potential candidate for AI-related expert systems analysis (thanks to Professor Gustavson for her guiding comments in this regard). I foresee at least two major areas that need significant research. First, one could attempt to capture protocols from expert, “calibrated” graders. Developing criteria that expert graders would agree might allow the possibility of tracing comments back to specific language in the composition. To the extent that such language can be parsed and analyzed objectively for the presence, absence, or degree of an attribute, one might be able to make inroads on a writing maturity model. In short, the aim would be to translate subjective comments into objective analyses. Crude attempts to do this sort of automated assessment often harshly restrict the input. For example, a model might limit assessments to essays written in response to a known prompt. Furthermore, the students often all have similar writing skills. The essay might have key words required and length (maximum and minimum) limitations. Within such narrowly defined compositions, one might be able to determine relative merit. My hope is to be able to develop assessment criteria in a more general way to apply to less defined writing samples. Though it would be beyond the scope of my topic to develop a program and apply it to compositions, I think that I might be able to make a significant contribution by recording protocols of human experts and translating them into objective criteria for expert systems programmers.
Possible Dissertation Topic 2
Developing BNF guidelines to aid Speech Recognition Programming
Professor Ron Frank spoke with me at our first fall outing this year
(September 8th 2000). After discussing my interest and areas of expertise,
he opined that there might be a need for a generalized BNF guidance for
those developing Speech Recognition Programs (or even natural language
programs in general). The point here is to home in on what has yet
to be done. Speech recognition, in the sense of I say it, the computer
can repeat it, is well on its way. The next hurdle will be to do
something with the input. Since spoken language varies significantly
from written language in several ways, programmers might find useful some
guidance on how to develop programs to decipher text. There are many hurdles
in this area, but the specific portion of this topic into which I might
make inroads has to do with developing sufficient restrictions on both
input and interpretation that some form of usable speech recognition might
go a step further to begin “speech understanding.” Similar to topic
1 above, this area would require categorization of speech in ways that
allow a program to derive meaning in context. Obviously, a computer
that can provide meaningful prompts for clarification would be superior
to one that takes what it gets and proceeds without clarification.
Just as we engage in human speech communication with feedback, a program
might benefit from similar activity, acting as an interlocutor, not merely
a listener. I have no idea at this point how far one might be able
to proceed along this idea, but any productive effort to narrow the scope
of the problem and suggest appropriate activity would be beneficial to
speech recognition research. It is only a matter of time before complete
recognition of the spoken word will be essentially perfected. We
must then turn our attention toward meaning: speech acts and the effect
of context on the interpretation of speech.