By Yuji Matsumoto, Richard Sproat, Kam-Fai Wong, Min Zhang
This ebook constitutes the completely refereed complaints of the twenty first foreign convention on computing device Processing of Oriental Languages, ICCPOL 2006, held in Singapore in December 2006, colocated with ISCSLP 2006, the fifth foreign Symposium on chinese language Spoken Language Processing.
The 36 revised complete papers and 20 revised brief papers awarded have been rigorously reviewed and chosen from 169 submissions. The papers are equipped in topical sections on details retrieval, record type, questions and solutions, summarization, laptop translation, notice segmentation, chunking, abbreviation enlargement, writing-system matters, parsing, semantics, and lexical resources.
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Additional resources for Computer Processing of Oriental Languages. Beyond the Orient: The Research Challenges Ahead: 21st International Conference, ICCPOL 2006, Singapore, December
2 Tri-training Semi-supervised Learning and Its Modiﬁcations In this section, we describe the original Tri-training algorithm and give two proposals to improve it. 5; li ← 0 6 end for ... 25 hi ← Learni (L ∪ Li ); ei ← ei ;li ← |Li | ... 5; li ← 0 6 end for ... 25 hi ← Learni (viewi (L ∪ Li )); ei ← ei ;li ← |Li | ... c) Tri-training with multiple y∈label i:hi (x)=y a) Original Tri-training algorithm learning algorithms and views Fig. 1. 1 Semi-supervised Tri-training Algorithm In Tri-training algorithm , three classiﬁers: h1 , h2 and h3 are initially trained from a set by bootstrap-sampling the labelled set L.
14. Zhi-Hua Zhou and Ming Li, “Tri-training: Exploiting unlabeled data using three classiﬁers,” IEEE Transactions on Knowledge and Data Engineering, Vol 17, No. 11, (2005). net Abstract. Query similarity computation is one of important factors in the process of query clustering. It has been used widely in the field of information processing. In this paper, a unified model for query similarity computation is presented based on system similarity. The novel approach of similarity computation uses the literal, semantic and statistical relative features of query.
If all the classiﬁers are identical, then for any of three classiﬁers, the unlabelled instances labelled by the other two classiﬁers will be the same as those labelled by itself, thus, Tri-training becomes self-training with a single classiﬁer. The pseudo-code of the algorithm is described in Fig. 1a, where Learn is a classiﬁcation algorithm; Si is a training set bootstrap-sampled from original set L; ei is the error rate of hi in the (t-1)th round. 5; ei is the error rate of hi in the tth round; Li is the set of instances that are labelled for hi in the tth round; li is the size of Li at (t-1)th round and in the ﬁrst round it is estimated by [ eei + 1]; Subsample(Li, s) funci tion randomly removes |Li | − s number of instances from Li in order to make current round have better performance than that of previous round as proved in ; M easureError(hj &hk ) function attempts to estimate the classiﬁcation error rate of the hypothesis derived from the combination of hj and hk .
Computer Processing of Oriental Languages. Beyond the Orient: The Research Challenges Ahead: 21st International Conference, ICCPOL 2006, Singapore, December by Yuji Matsumoto, Richard Sproat, Kam-Fai Wong, Min Zhang