基於已知名稱搜尋結果的網路實體辨識模型建立工具(A Tool for Web NER Model Generation Using Search Snippets of Known Entities) [In Chinese]
黃雅筠 Ya-Yun Huang 國立中央大學資訊工程學系 Department of Computer Science and Information Engineering National Central University a2425320032002@gmail.com 張嘉惠 Chia-Hui Chang 國立中央大學資訊工程學系 Department of Computer Science and Information Engineering National Central University chia@csie.ncu.edu.tw 周建龍 Chia-Hui Chang 國立中央大學資訊工程學系 Department of Computer Science and Information Engineering National Central University formatc.chou@gmail.com
Semi-joint Labeling for Chinese Named Entity Recognition
Named entity recognition (NER) is an essential component of text mining applications. In Chinese sentences, words do not have delimiters; thus, incorporating word segmentation information into an NER model can improve its performance. Based on the framework of dynamic conditional random fields, we propose a novel labeling format, called semi-joint labeling which partially integrates word segmentation information and named entity tags for NER. The model enhances the interaction of segmentation tags and NER achieved by traditional approaches. Moreover, it allows us to consider interactions between multiple chains in a linear-chain model. We use data from the SIGHAN 2006 NER bakeoff to evaluate the proposed model. The experimental results demonstrate that our approach outperforms state-of-the-art systems.
Japanese Named Entity Recognition based on a Simple Rule Generator and Decision Tree Learning
Named entity (NE) recognition is a task in which proper nouns and numerical information in a document are detected and classified into categories such as person, organization, location, and date. NE recognition plays an essential role in information extraction systems and question answering systems. It is well known that hand-crafted systems with a large set of heuristic rules are difficult to maintain, and corpus-based statistical approaches are expected to be more robust and require less human intervention. Several statistical approaches have been reported in the literature. In a recent Japanese NE workshop, a maximum entropy (ME) system outperformed decision tree systems and most hand-crafted systems. Here, we propose an alternative method based on a simple rule generator and decision tree learning. Our experiments show that its performance is comparable to the ME approach. We also found that it can be trained more efficiently with a large set of training data and that it improves readability.