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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 11 of 11 1 How does Attention Affect the Model? ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Hua, Lingyu; Li, Qiuchi. - : Underline Science Inc., 2021 BASE Show details 2 Exploiting Position Bias for Robust Aspect Sentiment Classification ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Ma, Fang; Song, Dawei. - : Underline Science Inc., 2021 BASE Show details 3 A Generalized Language Model in Tensor Space ... Zhang, Lipeng; Zhang, Peng; Ma, Xindian. - : arXiv, 2019 BASE Show details 4 A quantum-inspired sentiment representation model for twitter sentiment analysis Zhang, Yazhou; Song, Dawei; Zhang, Peng; Li, Xiang; Wang, Panpan. - 2019 Abstract: Sentiment analysis aims to capture the diverse sentiment information expressed by authors in given natural language texts, and it has been a core research topic in many artificial intelligence areas. The existing machine-learning-based sentiment analysis approaches generally focus on employing popular textual feature representation methods, e.g., term frequency-inverse document frequency (tf-idf), n-gram features, and word embeddings, to construct vector representations of documents. These approaches can model rich syntactic and semantic information, but they largely fail to capture the sentiment information that is central to sentiment analysis. To address this issue, we propose a quantum-inspired sentiment representation (QSR) model. This model can not only represent the semantic content of documents but also capture the sentiment information. Specifically, since adjectives and adverbs are good indicators of subjective expression, this model first extracts sentiment phrases that match the designed sentiment patterns based on adjectives and adverbs. Then, both single words and sentiment phrases in the documents are modeled as a collection of projectors, which are finally encapsulated in density matrices through maximum likelihood estimation. These density matrices successfully integrate the sentiment information into the representations of documents. Extensive experiments are conducted on two widely used Twitter datasets, which are the Obama-McCain Debate (OMD) dataset and the Sentiment140 Twitter dataset. The experimental results show that our model significantly outperforms a number of state-of-the-art baselines and demonstrate the effectiveness of the QSR model for sentiment analysis. URL: http://oro.open.ac.uk/60693/ https://doi.org/10.1007/s10489-019-01441-4 BASE Hide details 5 Quantum-inspired Complex Word Embedding Uprety, Sagar; Li, Qiuchi; Song, Dawei. - : Association for Computational Linguistics, 2018 BASE Show details 6 Characterizing pure high-order entanglements in lexical semantic spaces via information geometry Hou, Yuexian; Song, Dawei. - 2009 BASE Show details 7 Towards operational abduction from a cognitive perspective Bruza, Peter; Cole, Richard; Song, Dawei. - 2006 BASE Show details 8 Information flow analysis with Chinese text Cheong, Paulo; Song, Dawei; Bruza, Peter. - : Springer Verlag, 2005 BASE Show details 9 A Correlation Analysis on LSA and HAL Semantic Space Models Yan, Xin; Li, Xue; Song, Dawei. - 2004 BASE Show details 10 Fuzzy K-means clustering on a high dimensional semantic space Cao, Guihong; Song, Dawei; Bruza, Peter. - : Springer Verlag, 2004 BASE Show details 11 Towards context sensitive information inference Song, Dawei; Bruza, Peter D. In: American Society for Information Science and Technology. Journal of the American Society for Information Science and Technology. - New York, NY : Wiley 54 (2003) 4, 321-334 BLLDB Show details

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