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Titlebook: Natural Language Processing and Chinese Computing; 4th CCF Conference, Juanzi Li,Heng Ji,Yansong Feng Conference proceedings 2015 Springer

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樓主: 變成小松鼠
21#
發(fā)表于 2025-3-25 07:12:11 | 只看該作者
22#
發(fā)表于 2025-3-25 10:17:11 | 只看該作者
Transition-Based Dependency Parsing with Long Distance Collocations, we propose a method to improve the performance of transition-based parsing with long distance collocations. With these long distance collocations, our method provides an approximate global view of the entire sentence, which is a little bit similar to top-down parsing. To further improve the accura
23#
發(fā)表于 2025-3-25 13:47:11 | 只看該作者
Recurrent Neural Networks with External Memory for Spoken Language Understandingto associate a semantic label to each word in an input sequence. The success of RNN may be attributed to its ability to memorise long-term dependence that relates the current-time semantic label prediction to the observations many time instances away. However, the memory capacity of simple RNNs is l
24#
發(fā)表于 2025-3-25 18:59:17 | 只看該作者
25#
發(fā)表于 2025-3-25 22:59:47 | 只看該作者
Entity Translation with Collective Inference in Knowledge Graphice the availability of KB data varies from language to language, which greatly limits potential usage of knowledge base. In this paper, we propose a novel method to construct or enrich a knowledge base by entity translation with help of another KB but compiled in a different language. In our work,
26#
發(fā)表于 2025-3-26 01:45:00 | 只看該作者
27#
發(fā)表于 2025-3-26 05:02:42 | 只看該作者
Clustering Sentiment Phrases in Product Reviews by Constrained Co-clusterings of reviews. There are mainly two components in a sentiment phrase, the aspect word and the opinion word. We need to cluster these two parts simultaneously. Although several methods have been proposed to cluster words or phrases, limited work has been done on clustering two-dimensional sentiment ph
28#
發(fā)表于 2025-3-26 11:55:08 | 只看該作者
A Cross-Domain Sentiment Classification Method Based on Extraction of Key Sentiment Sentenceal sentiment classification approaches usually perform poorly to address cross-domain problems. So, this paper proposed a cross-domain sentiment classification method based on extraction of key sentiment sentence. Firstly, based on the observation that not every part of the document is equally infor
29#
發(fā)表于 2025-3-26 14:04:18 | 只看該作者
30#
發(fā)表于 2025-3-26 18:53:30 | 只看該作者
Automatic Detection of Rumor on Social Network shallow features of messages, including content and blogger features. But such shallow features cannot distinguish between rumor messages and normal messages in many cases. Therefore, in this paper we propose an automatic rumor detection method based on the combination of new proposed implicit feat
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