教師資料查詢 | 類別: 會議論文 | 教師: 戴敏育 Min-Yuh Day (瀏覽個人網頁)

標題:Machine Learning for Imbalanced Datasets of Recognizing Inference in Text with Linguistic Phenomena
學年104
學期1
發表日期2015/08/13
作品名稱Machine Learning for Imbalanced Datasets of Recognizing Inference in Text with Linguistic Phenomena
作品名稱(其他語言)
著者Min-Yuh Day; Cheng-Chia Tsai
作品所屬單位
出版者
會議名稱2015 IEEE 16th International Conference on Information Reuse and Integration (IEEE IRI 2015)
會議地點San Francisco, California, USA
摘要Recognizing inference in text (RITE) plays an important role in the answer validation modules for a Question Answering (QA) system. The problem of class imbalance has received increased attention in the machine learning community. In recent years, several attempts have been made on the linguistic phenomena analysis, however, little is known about the effects of imbalanced datasets with linguistic phenomenon in recognizing inference in text. The objective of this paper is to provide an empirical study on learning imbalanced datasets of recognizing inference in text with linguistic phenomena for a better understanding of the effects of imbalanced datasets with linguistic phenomenon in recognizing inference in text. In this paper, we proposed an analysis of imbalanced datasets of recognizing inference in text with linguistic phenomena using NTCIR 11 RITE-VAL gold standard dataset and development dataset. The experimental results suggest that the distribution of imbalanced datasets of recognizing inference in text with linguistic phenomenon could be dramatically varied on the performance of a machine learning classifier.
關鍵字Imbalanced Datasets;Linguistic Phenomena;Machine Learning;Recognizing Inference in Text;Textual Entailment
語言英文(美國)
收錄於
會議性質國際
校內研討會地點
研討會時間20150813~20150815
通訊作者Min-Yuh Day
國別美國
公開徵稿
出版型式
出處Proceedings of the 2015 IEEE 16th International Conf2015), Sanerence on Information Reuse and Integration, pp. 562-568
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