Participation | 10% | |
Quizzes | 20% | |
Homeworks | 30% | |
Midterm | 20% | |
Final | 20% |
B+ | 87--89 | C+ | 77--79 | D+ | 67--69 | E | 0--59 | ||
A | 93--100 | B | 83--86 | C | 73--76 | D | 60--66 | ||
A- | 90--92 | B- | 80--82 | C- | 70--72 |
Writing systems used for language. Representing text on the computer. Digital representations of speech.
What facilities exist for searching for language-based information? Different query languages and what they allow you to do. Differences between specific and general queries. How to evaluate the results of a search.
Techniques for classifying documents. What language(s) are they written in? Are they junk mail? Are statistical techniques better than rule-based ones, or not? When will the techniques fail?
What do so-called ``grammar checkers'' and ``spelling correctors'' do? What do such programs base their advice on? When does it make sense to use such tools and what kind of errors are to be expected?
What do the free internet-based translation services manage to do---and where do they fail? For what purposes can automatic machine translation work reliably? What translation support functions can a computer provide? A closer look at what makes machine translation such a hard task. Is it the grammar, the meaning, the culture, all three, or something else?
Eliza and its surprising success in engaging people in conversation. When are dialog systems used, for what purpose? A closer look at the components of a dialog system. Where is what kind of knowledge needed to make it work?
What is involved in learning a foreign language? What role in language learning can computers play: from vocabulary training, via presentation of learning material, to providing feedback on learner errors and progress.
How do we react to computers that make use of language? What does it mean for the way we see ourselves? What assumptions do we make about every user of language, be it a human or a machine.
Week | Month | Date | Day | Topic | Assignments |
1 | Sep | 20 | W | Introduction | due at 11:30am |
2 | 25 | M | 1. Text and speech encoding (9up) | ||
27 | W | ||||
3 | Oct | 2 | M | ||
4 | W | 2. Searching (9up) | Quiz1, HW1 | ||
4 | 9 | M | |||
11 | W | ||||
5 | 16 | M | 3. Text Classification (Spam filtering) (9up) | Quiz2, HW2, HW2-solutions | |
18 | W | ||||
6 | 23 | M | 4. Writer's aids (9up) | Quiz3 | |
25 | W | ||||
7 | 30 | M | Quiz4, HW3 (Ex. 1 only) | ||
Nov | 1 | W | Midterm (review sheet) | ||
8 | 6 | M | John Nerbonne talk, 122 Oxley Hall | HW3 (Ex. 2 only) | |
8 | W | ||||
9 | 13 | M | 5. Machine Translation (9up) | ||
15 | W | HW3 (Ex. 3--5) | |||
10 | 20 | M | |||
22 | W | Quiz5 | |||
11 | 27 | M | 6. Social context of technology use (9up) | ||
29 | W | HW4 | |||
12 | Dec | 5 | T | Final (review sheet) | |
Note that it's on a TUESDAY! | |||||
Location: CC 345, Time: 11:30-1:18. |
This document was translated from LATEX by HEVEA.