Modeling Interestingness with Deep Neural Networks
| Autor: | Xiaodong He, Li Deng, Michael Gamon, Jianfeng Gao, Patrick Pantel |
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| Rok vydání: | 2014 |
| Předmět: |
Focus (computing)
Information retrieval Computer science business.industry Feature vector media_common.quotation_subject Context (language use) Space (commercial competition) computer.software_genre Reading (process) Deep neural networks Source document Artificial intelligence business computer Natural language processing media_common |
| Zdroj: | EMNLP |
| DOI: | 10.3115/v1/d14-1002 |
| Popis: | An "Interestingness Modeler" uses deep neural networks to learn deep semantic models (DSM) of "interestingness." The DSM, consisting of two branches of deep neural networks or their convolutional versions, identifies and predicts target documents that would interest users reading source documents. The learned model observes, identifies, and detects naturally occurring signals of interestingness in click transitions between source and target documents derived from web browser logs. Interestingness is modeled with deep neural networks that map source-target document pairs to feature vectors in a latent space, trained on document transitions in view of a "context" and optional "focus" of source and target documents. Network parameters are learned to minimize distances between source documents and their corresponding "interesting" targets in that space. The resulting interestingness model has applicable uses, including, but not limited to, contextual entity searches, automatic text highlighting, prefetching documents of likely interest, automated content recommendation, automated advertisement placement, etc. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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