Titlebook: Knowledge Science, Engineering and Management; 9th International Co Franz Lehner,Nora Fteimi Conference proceedings 2016 Springer Internati

DIS

Yang Yan,Tingwen Liu,Li Guo,Jiapeng Zhao,Jinqiao Shi and retirement savings are low for those throughout the life course, made especially concerning for those who are near or already in retirement and for members of more vulnerable groups. This chapter outlines the goals of one of the 12 Grand Challenges for Social Work—to Build Financial Capability

使长胖

BOWL: Bag of Word Clusters Text Representation Using Word Embeddings text representation methods. Although the BOW and TF-IDF are simple and perform well in tasks like classification and clustering, its representation efficiency is extremely low. Besides, word level semantic similarity is not captured which results failing to capture text level similarity in many si

CRUMB

官僚统治

1FAWN

A Practical Method of Identifying Chinese Metaphor Phrases from Corpus, and information retrieval) are affected if metaphors can not be identified appropriately. This paper presents a three-phase method for recognizing Chinese metaphor phrases from a large-scale corpus. First, we acquire the context of every candidate phrase. Then hierarchical clustering is used to cl

勾引

right-atrium

Increasing Topic Coherence by Aggregating Topic Modelsence than individual models. When generating a topic model a number of parameters must be specified. Depending on the parameters chosen the resulting topics can be very general or very specific. In this paper the process of aggregating multiple topic models generated using different parameters is in

护航舰

Learning Chinese-Japanese Bilingual Word Embedding by Using Common Characterson, word sense disambiguation and so on. However, no model has been universally accepted for learning bilingual word embedding. In this work, we propose a novel model named CJ-BOC to learn Chinese-Japanese word embeddings. Given Chinese and Japanese share a large portion of common characters, we exp

intoxicate

Analyzing Topic-Sentiment and Topic Evolution over Time from Social Mediareviews or ratings, those annotations are more intuitive to express the sentiment of the users. Topic model is proved more effective to analyze the text information, however, most existing topic models focus on either extracting static topic sentiment or tracking topics over time but ignoring sentim

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