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Title: Neural Network-Based Vector Representation of Documents for Reader-Emotion Categorization
Authors: 謝宇倫
Hsieh, Yulun
Liu, S.-H.
Chang, Y.-C.
Hsu, W.-L.
Contributors: 資訊管理學系
Keywords: Encoding (symbols);Neural networks;Semantics;Document Representation;Embedding method;Primary objective;reader emotion;Semantic context;Similarity measure;Vector representations;word embedding;Information use
Date: 2015
Issue Date: 2017-08-14 15:35:16 (UTC+8)
Abstract: In this paper, we propose a novel approach for reader-emotion categorization using word embedding learned from neural networks and an SVM classifier. The primary objective of such word embedding methods involves learning continuous distributed vector representations of words through neural networks. It can capture semantic context and syntactic cues, and subsequently be used to infer similarity measures among words, sentences, and even documents. Various methods of combining the word embeddings are tested for their performances on reader-emotion categorization of a Chinese news corpus. Results demonstrate that the proposed method, when compared to several other approaches, can achieve comparable or even better performances. © 2015 IEEE.
Relation: Proceedings - 2015 IEEE 16th International Conference on Information Reuse and Integration, IRI 2015, (), 569-573
Data Type: conference
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