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Titlebook: Natural Language Processing and Chinese Computing; Second CCF Conferenc Guodong Zhou,Juanzi Li,Yansong Feng Conference proceedings 2013 Spr

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11#
發(fā)表于 2025-3-23 11:07:06 | 只看該作者
Semi-supervised Text Categorization by Considering Sufficiency and Diversitye performance by exploring the knowledge in both labeled and unlabeled data. In this paper, we propose a novel bootstrapping approach to semi-supervised TC. First of all, we give two basic preferences, i.e., . and . for a possibly successful bootstrapping. After carefully considering the . preferenc
12#
發(fā)表于 2025-3-23 17:05:28 | 只看該作者
13#
發(fā)表于 2025-3-23 19:25:55 | 只看該作者
Discriminative Latent Variable Based Classifier for Translation Error Detectionistical machine translation (SMT). It uses latent variables to carry additional information which may not be expressed by those original labels and capture more complicated dependencies between translation errors and their corresponding features to improve the classification performance. Specificall
14#
發(fā)表于 2025-3-23 23:13:43 | 只看該作者
Incorporating Entities in News Topic Modelingtities, words and topics through a large amount of news articles is nontrivial. Topic modeling like Latent Dirichlet Allocation has been applied a lot to mine hidden topics in text analysis, which have achieved considerable performance. However, it cannot explicitly show relationship between words a
15#
發(fā)表于 2025-3-24 04:17:19 | 只看該作者
16#
發(fā)表于 2025-3-24 08:55:09 | 只看該作者
17#
發(fā)表于 2025-3-24 14:00:52 | 只看該作者
Collective Corpus Weighting and Phrase Scoring for SMT Using Graph-Based Random Walk weighting or phrase scoring, but these two types of methods were often investigated independently. To leverage the dependencies between them, we propose an intuitive approach to improve translation modeling by collective corpus weighting and phrase scoring. The method uses the mutual reinforcement
18#
發(fā)表于 2025-3-24 16:41:27 | 只看該作者
19#
發(fā)表于 2025-3-24 20:43:07 | 只看該作者
Research on Building Family Networks Based on Bootstrapping and Coreference Resolutionhips. We propose a novel method to construct personal families based on bootstrapping and coreference resolution on top of a search engine. It begins with seeds of personal relations to discover relational patterns in a bootstrapping fashion, then personal relations are further extracted via these l
20#
發(fā)表于 2025-3-25 00:23:55 | 只看該作者
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