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1
Prioritizing and aggregating interacting requirements for product-service system development
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2
Knowledge graph and text jointly embedding
In: http://ss.sysu.edu.cn/%7Efjl/emnlp2014.pdf (2014)
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3
Language Specific Issue and Feature Exploration in Chinese Event Extraction
In: http://cs.nyu.edu/%7Ehengji/hlt09-event.pdf (2009)
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4
Mining multilingual topics from Wikipedia
In: http://research.microsoft.com/pubs/81536/p1155-ni.pdf (2009)
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5
Noise reduction through summarization for Web-page classification
In: http://www.cs.ust.hk/~qyang/Docs/2007/dshen summarization.pdf (2007)
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6
Web-page summarization using clickthrough data
In: http://www.cse.ust.hk/~qyang/Papers/2005/sigir-jiantao.pdf (2005)
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7
Web-page summarization using clickthrough data
In: http://www.cs.fiu.edu/%7Elli003/Sum/SIGIR/2005/4.pdf (2005)
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8
User Intention Modeling in Web Applications Using Data Mining
In: http://research.microsoft.com/~zhengc/papers/WWWJ_intention_modeling.pdf (2002)
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9
A Topic Model for Building Fine-grained Domain-specific Emotion Lexicon
In: http://aclweb.org/anthology/P/P14/P14-2069.pdf
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10
Language specific issue and feature exploration in chinese event extraction
In: http://www.aclweb.org/anthology-new/N/N09/N09-2053.pdf
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11
Discriminative Training on language model
In: http://www.microsoft.com/china/research/group/./downloads/g-nlps/NLPSP/n11.pdf
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12
Towards High Performance Multilingual Event Extraction: Language Specific Issue and Feature Exploration
In: http://nlp.cs.qc.cuny.edu/hlt09-event.pdf
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13
Algorithms, Experimentation
In: http://www.cs.ust.hk/~qyang/Docs/2006/p35-shen.pdf
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14
Knowledge Graph and Text Jointly Embedding
In: http://emnlp2014.org/papers/pdf/EMNLP2014167.pdf
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
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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15
Knowledge Graph and Text Jointly Embedding
In: http://aclweb.org/anthology/D/D14/D14-1167.pdf
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16
Mining Translations of Web Queries from Web Click-through Data
In: http://www.aaai.org/Papers/AAAI/2008/AAAI08-181.pdf
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17
Graph-based clustering for computational linguistics: A survey
In: http://aclweb.org/anthology-new/W/W10/W10-2301.pdf
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18
Cross-lingual Slot Filling from Comparable Corpora
In: http://cs.nyu.edu/%7Exiangli/papers/clie2011.pdf
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19
Mining Translations of Web Queries from Web Click-through Data*
In: http://www.cs.ust.hk/~qyang/Docs/2008/AAAIHuRong.pdf
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20
Cross-lingual Slot Filling from Comparable Corpora
In: http://aclweb.org/anthology-new/W/W11/W11-1215.pdf
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