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Prioritizing and aggregating interacting requirements for product-service system development
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Knowledge graph and text jointly embedding
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In: http://ss.sysu.edu.cn/%7Efjl/emnlp2014.pdf (2014)
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Language Specific Issue and Feature Exploration in Chinese Event Extraction
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In: http://cs.nyu.edu/%7Ehengji/hlt09-event.pdf (2009)
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BASE
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Mining multilingual topics from Wikipedia
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In: http://research.microsoft.com/pubs/81536/p1155-ni.pdf (2009)
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BASE
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Noise reduction through summarization for Web-page classification
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In: http://www.cs.ust.hk/~qyang/Docs/2007/dshen summarization.pdf (2007)
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BASE
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Web-page summarization using clickthrough data
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In: http://www.cse.ust.hk/~qyang/Papers/2005/sigir-jiantao.pdf (2005)
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BASE
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Web-page summarization using clickthrough data
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In: http://www.cs.fiu.edu/%7Elli003/Sum/SIGIR/2005/4.pdf (2005)
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BASE
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User Intention Modeling in Web Applications Using Data Mining
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In: http://research.microsoft.com/~zhengc/papers/WWWJ_intention_modeling.pdf (2002)
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BASE
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A Topic Model for Building Fine-grained Domain-specific Emotion Lexicon
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In: http://aclweb.org/anthology/P/P14/P14-2069.pdf
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BASE
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Language specific issue and feature exploration in chinese event extraction
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In: http://www.aclweb.org/anthology-new/N/N09/N09-2053.pdf
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BASE
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Discriminative Training on language model
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In: http://www.microsoft.com/china/research/group/./downloads/g-nlps/NLPSP/n11.pdf
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BASE
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Towards High Performance Multilingual Event Extraction: Language Specific Issue and Feature Exploration
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In: http://nlp.cs.qc.cuny.edu/hlt09-event.pdf
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Algorithms, Experimentation
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In: http://www.cs.ust.hk/~qyang/Docs/2006/p35-shen.pdf
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Knowledge Graph and Text Jointly Embedding
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In: http://emnlp2014.org/papers/pdf/EMNLP2014167.pdf
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Abstract:
We examine the embedding approach to reason new relational facts from a large-scale knowledge graph and a text corpus. We propose a novel method of jointly em-bedding entities and words into the same continuous vector space. The embedding process attempts to preserve the relations between entities in the knowledge graph and the concurrences of words in the text corpus. Entity names and Wikipedia an-chors are utilized to align the embeddings of entities and words in the same space. Large scale experiments on Freebase and a Wikipedia/NY Times corpus show that jointly embedding brings promising improvement in the accuracy of predicting facts, compared to separately embedding knowledge graphs and text. Particularly, jointly embedding enables the prediction of facts containing entities out of the knowledge graph, which cannot be han-dled by previous embedding methods. At the same time, concerning the quality of the word embeddings, experiments on the analogical reasoning task show that jointly embedding is comparable to or slightly better than word2vec (Skip-Gram). 1
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URL: http://emnlp2014.org/papers/pdf/EMNLP2014167.pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.671.1250
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Knowledge Graph and Text Jointly Embedding
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In: http://aclweb.org/anthology/D/D14/D14-1167.pdf
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Mining Translations of Web Queries from Web Click-through Data
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In: http://www.aaai.org/Papers/AAAI/2008/AAAI08-181.pdf
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Graph-based clustering for computational linguistics: A survey
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In: http://aclweb.org/anthology-new/W/W10/W10-2301.pdf
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Cross-lingual Slot Filling from Comparable Corpora
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In: http://cs.nyu.edu/%7Exiangli/papers/clie2011.pdf
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Mining Translations of Web Queries from Web Click-through Data*
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In: http://www.cs.ust.hk/~qyang/Docs/2008/AAAIHuRong.pdf
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Cross-lingual Slot Filling from Comparable Corpora
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In: http://aclweb.org/anthology-new/W/W11/W11-1215.pdf
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