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Titlebook: Web and Big Data; First International Lei Chen,Christian S. Jensen,Xiang Lian Conference proceedings 2017 Springer International Publishin

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發(fā)表于 2025-3-23 10:02:11 | 只看該作者
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發(fā)表于 2025-3-23 15:36:26 | 只看該作者
0302-9743 International Joint Conference, APWeb-WAIM 2017, held in Beijing, China in July 2017...The 44 full papers presented together with 32 short papers and 10 demonstrations papers were carefully reviewed and selected from 240 submissions. The papers are organized around the following topics: spatial data
13#
發(fā)表于 2025-3-23 18:16:38 | 只看該作者
Combining Node Identifier Features and Community Priors for Within-Network Classifications of nodes in a partially labeled network. In this paper, we propose a new algorithm called identifier based relational neighbor classifier (IDRN) to solve the within-network multi-label classification problem. We use the node identifiers in the egocentric networks as features and propose a within-n
14#
發(fā)表于 2025-3-23 23:01:56 | 只看該作者
Combining Node Identifier Features and Community Priors for Within-Network Classifications of nodes in a partially labeled network. In this paper, we propose a new algorithm called identifier based relational neighbor classifier (IDRN) to solve the within-network multi-label classification problem. We use the node identifiers in the egocentric networks as features and propose a within-n
15#
發(fā)表于 2025-3-24 05:44:40 | 只看該作者
An Active Learning Approach to Recognizing Domain-Specific Queries From Query Logy classification relying on external resources or annotated training queries, we take query log as the only resource for recognizing domain-specific queries. In the proposed approach, we represent query log as a heterogeneous graph and then formulate the task of domain-specific query recognition as
16#
發(fā)表于 2025-3-24 10:00:20 | 只看該作者
An Active Learning Approach to Recognizing Domain-Specific Queries From Query Logy classification relying on external resources or annotated training queries, we take query log as the only resource for recognizing domain-specific queries. In the proposed approach, we represent query log as a heterogeneous graph and then formulate the task of domain-specific query recognition as
17#
發(fā)表于 2025-3-24 13:08:51 | 只看該作者
18#
發(fā)表于 2025-3-24 18:11:56 | 只看該作者
19#
發(fā)表于 2025-3-24 22:08:23 | 只看該作者
20#
發(fā)表于 2025-3-25 01:34:10 | 只看該作者
Joint Emoji Classification and Embedding Learningerances with emoji are produced by humans manually in social media platforms every day, which make emoji great influence on the human life. For the academic community, researchers are always with the help of utterances including emoji as annotated data to work on sentiment analysis, yet lack of adeq
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