1. An Adaptive Learning with an Application to Chinese Homophone Disambiguation from Yue-shi Lee International Journal of Computer Processing of Oriental Languages, Vol. 15, No. 3 (2002) 245 – 260

2. Introduction Contextual language processing: Given a specified corpus, finding a plausible probabilistic model that translate the perceived syllable sequence into characters, words, or sentences correctly (By the sense of maximum likelihood). Two main errors affect the correctness: Modeling error : Weakness of the language model – enhance or refine the weak language model. Estimation error : Small size training corpus ; Variant run-time context domain ;

3. Introduction (cont.) Estimation error : Small size training corpus: smoothing, class-based and similarity-based models to STATICALLY adjust the unreliable probabilities. Variant run-time context domain: cache-based model: capturing short-term fluctuation of the frequencies of words, effective for frequent words or repeated sentences. multiple language model: Defining a “current time context”, and importing several similar texts from distinct fields for comparison.

4. Introduction (cont.) Author’s work: Goal: To decrease the estimation error under the variant context situation. Method: Developing an adaptive algorithm to adjust statistical information under the variant context environment; Application: Disambiguation of homophone Chinese.

5. Language Model Chinese Fact: 1,300 syllables, 13,094 commonly used characters, 100,000 words. Disambiguation is important: Many different characters share the same syllables.

6. Language Model (cont.) Disambiguation Definition: Translate a sequence of syllables into a sequence of characters (sentence) with correct meaning. Modeling:

7. Language Model (cont.) Simplification:

8. Language Model (cont.) Stored context statistical information: 2-D matrix 1-D vector Contributes of author ’ s work: Adapt the context statistical information according to feedback estimation errors under variant context situation.

9. Adaptive Learning Model Detecting the components needing be adjusted

10. Adaptive Learning Model (cont.) Detecting the components needing be adjusted (cont.)

11. Adaptive Learning Model (cont.) Modeling and Learning algorithm:

12. Experimental Results Two parts of data come from two different context. Part1 has 123 sentences and 1057 characters Part2 has 123 sentences and 977 characters Two Stages of Experiments Stage1: Parts1 as learning data, part2 as testing data Stage2: Parts2 as learning data, part1 as testing data

13. Experimental Results

14. Experimental Results (cont.)

15. Conclusion propose an adaptive learning algorithm for task adaptation to fit best the run-time context domain in the application of Chinese homophone disambiguation. This adaptive learning algorithm is also suitable for incremental training. Personal comment (1) : The author proposed an MLP based method. However, I think the modeling of the learning problem is quite a linear one, therefore the MLP is not necessary. Personal comment (2) : I do not think the experimental results are good enough, however, the adaptive learning idea in nature language processing is interesting. Link : from Inspec database, available on MSU librarywebsite.