TAILIEUCHUNG - Báo cáo khoa học: "Words and Echoes: Assessing and Mitigating the Non-Randomness Problem in Word Frequency Distribution Modeling"

Frequency distribution models tuned to words and other linguistic events can predict the number of distinct types and their frequency distribution in samples of arbitrary sizes. We conduct, for the first time, a rigorous evaluation of these models based on cross-validation and separation of training and test data. Our experiments reveal that the prediction accuracy of the models is marred by serious overfitting problems, due to violations of the random sampling assumption in corpus data. We then propose a simple pre-processing method to alleviate such non-randomness problems. . | Words and Echoes Assessing and Mitigating the Non-Randomness Problem in Word Frequency Distribution Modeling Marco Baroni CIMeC University of Trento Bettini 31 38068 Rovereto Italy Stefan Evert IKW University of Osnabriick Albrechtstr. 28 49069 Osnabriick Germany Abstract Frequency distribution models tuned to words and other linguistic events can predict the number of distinct types and their frequency distribution in samples of arbitrary sizes. We conduct for the first time a rigorous evaluation of these models based on cross-validation and separation of training and test data. Our experiments reveal that the prediction accuracy of the models is marred by serious overfitting problems due to violations of the random sampling assumption in corpus data. We then propose a simple pre-processing method to alleviate such non-randomness problems. Further evaluation confirms the effectiveness of the method which compares favourably to more complex correction techniques. 1 Introduction Large-Number-of-Rare-Events LNRE models Baayen 2001 are a class of specialized statistical models that allow us to estimate the characteristics of the distribution of type probabilities in type-rich linguistic populations such as words from limited samples our corpora . They also allow us to extrapolate quantities such as vocabulary size the number of distinct types and the number of hapaxes types occurring just once beyond a given corpus or make predictions for completely unseen data from the same underlying population. LNRE models have applications in theoretical linguistics . for comparing the type richness of morphological or syntactic processes that are attested to 904 different degrees in the data Baayen 1992 . Consider for example a very common prefix such as re-and a rather rare prefix such as meta-. With LNRE models we can answer questions such as If we could obtain as many tokens of meta- as we have of re- would we also see as many

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