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courses:rg:multilingual-noise-robust-supervised-morphological-analysis-using-the-wordframe-model [2011/01/07 18:32] kirschner |
courses:rg:multilingual-noise-robust-supervised-morphological-analysis-using-the-wordframe-model [2011/01/09 17:53] (current) kirschner |
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| ===== Comments ===== | ===== Comments ===== | ||
| + | === Summary === | ||
| * In this paper the author presents a new supervized method for lemmatization, | * In this paper the author presents a new supervized method for lemmatization, | ||
| * This new method is compared to existing End-Of-String method and is proven better in most of the cases. | * This new method is compared to existing End-Of-String method and is proven better in most of the cases. | ||
| * A combination of both methods gives even better results. | * A combination of both methods gives even better results. | ||
| * The results are evaulated on 30 different languages with median accuracy 97.5% | * The results are evaulated on 30 different languages with median accuracy 97.5% | ||
| - | * WordFrame model algorithm | + | * The WordFrame model algorithm |
| | | ||
| + | === Described models === | ||
| + | Both models described in this paper were ment to decompose the word to some basic parts (not morphemes, but similar). | ||
| + | |||
| + | ==Extended End-of-String model== | ||
| + | Decomposition of inflection into | ||
| + | * prefix - // | ||
| + | * primary common substring - //the stem// | ||
| + | * point of suffixation change - // | ||
| + | * suffix/ | ||
| + | |||
| + | ==WordFrame model== | ||
| + | Decomposition of inflection into | ||
| + | * prefix - // | ||
| + | * point of prefixation change - // | ||
| + | * secondary common substring - //the part of stem before stem vowel change// | ||
| + | * vowel change - //the vowel change inside the stem// | ||
| + | * primary common substring - //the part of stem after the vowel change// | ||
| + | * point of suffixation change - // | ||
| + | * suffix/ | ||
| ===== Suggested Additional Reading ===== | ===== Suggested Additional Reading ===== | ||
| Line 20: | Line 40: | ||
| ===== What do we like about the paper ===== | ===== What do we like about the paper ===== | ||
| - | * | + | * Robustness of the algorithm in noisy conditions |
| + | * Evaluation on many different languages | ||
| ===== What do we dislike about the paper ===== | ===== What do we dislike about the paper ===== | ||
| - | * | + | * Doesn' |
| + | * Experiments done only on verbs | ||
| + | * The paper doesn' | ||
| + | * The algorithm only uses features based only on the word itself, it doesn' | ||
| + | * With information given in this paper, we wouldn' | ||
| + | |||
| + | ===== Questions ===== | ||
| + | * Does the term //point of prefixation// | ||
| + | * In section 4 of the paper - // | ||
| + | * On what data the autor did the tuning of the models? Aren't the results ? //For example ommiting the case of deletion of vowels in stem?// | ||
| + | * in section 4.1, Table 5 - shoudn' | ||
| Written by Martin Kirschner | Written by Martin Kirschner | ||