PengYuan@PKU: Extracting Infrequent Sense Instance with the Same N-Gram Pattern for the SemEval-2010 Task 15

Proceedings of the 5th International Workshop on Semantic Evaluation, ACL 2010, pages 371–374, Uppsala, Sweden, 15-16 July 2010. c⃝2010 Association for Computational Linguistics PengYuan@PKU: Extracting Infrequent Sense Instance with the Same N-gram Pattern for the SemEval-2010 Task 15 Peng-Yuan Liu1 Shui Liu2 Shi-Wen Yu1 Tie-Jun Zhao2 1Institute of Computational Linguistics, Peking University, Beijing, China 2Department of Computer Science, Harbin Institute of Technology, Harbin, China {liupengyuan,yusw}@pku.edu.cn,{tjzhao,liushui}@mtlab.hit.edu.cn Abstract This paper describes our infrequent sense identification system participating in the SemEval-2010 task 15 on Infrequent Sense Identification for Mandarin Text to Speech Systems. The core system is a supervised system based on the ensembles of Naïve Bayesian classifiers. In order to solve the problem of unbalanced sense distribution, we intentionally extract only instances of infrequent sense with the same N-gram pattern as the complemental training data from an untagged Chinese corpus – People’s Daily of the year 2001. At the same time, we adjusted the prior probability to adapt to the distribution of the test data and tuned the smoothness coefficient to take the data sparseness into account. Official result shows that, our system ranked the first with the best Macro Accuracy 0.952. We briefly describe this system, its configuration options and the features used for this task and present some discussion of the results. 1 Introduction We participated in the SemEval-2010 task 15 on Infrequent Sense Identification for Mandarin Text to Speech Systems. This task required systems to disambiguating the homograph word, a word that has the same POS (part of speech) but different pronunciation. In this case, we still considered it as a WSD (word sense disambiguation) problem, but it is a little different from WSD. In this task, two or more senses of the same word may correspond to one pronunciation. That is, the sense granularity is coarser than traditional WSD. The challenge of this task is the much skewed distribution in real text: the most frequent pronunciation accounts for usually over 80%. In fact, in the training data provided by the organizer , we found that the sense distribution of some words are distinctly unbalanced. For each of these words, there are fewer than ten instances of one sense whereas the dominant sense instances are hundreds or more. At the same time, according to the task description on the task 15 of SemEval- 2010(http://semeval2.fbk.eu/semeval2.php?locati on=tasks), the test dataset of this task is intentionally divided into the infrequent pronunciation instances and the frequent ones by half and half. Apparently, if we use traditional methods and only the provided training dataset to train whatever classifier, it is very likely that we will get an disambiguation result that all (at least the overwhelming number) the test instances of these words would be labeled with the most frequent pronunciation (sense) tag. Then our system is meaningless for the target of the task is focused on the performance of identifying the infrequent sense. In order to solve the problem of the unbalanced sense distribution in the training data and the fairly balanced sense distribution …

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