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| − | ==Paper Recommendation== | + | =Chaos Work= |
| − | Pre-Trained Multi-View Word Embedding.[http://cslt.riit.tsinghua.edu.cn/mediawiki/images/3/3c/Pre-Trained_Multi-View_Word_Embedding.pdf]
| + | [[SLT]] |
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| − | Learning Word Representation Considering Proximity and Ambiguity.[http://cslt.riit.tsinghua.edu.cn/mediawiki/images/b/b0/Learning_Word_Representation_Considering_Proximity_and_Ambiguity.pdf]
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| − | Continuous Distributed Representations of Words as Input of LSTM Network Language Model.[http://cslt.riit.tsinghua.edu.cn/mediawiki/images/5/5a/Continuous_Distributed_Representations_of_Words.pdf]
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| − | WikiRelate! Computing Semantic Relatedness Using Wikipedia.[http://cslt.riit.tsinghua.edu.cn/mediawiki/images/c/cb/WikiRelate%21_Computing_Semantic_Relatedness_Using_Wikipedia.pdf]
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| − | Japanese-Spanish Thesaurus Construction Using English as a Pivot[http://cslt.riit.tsinghua.edu.cn/mediawiki/images/e/e8/Japanese-Spanish_Thesaurus_Construction.pdf]
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| − | ==Chaos Work==
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| − | ===SSA Model===
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| − | Build 2-dimension SSA-Model.
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| − | Start at : 2014-09-30 <--> End at : 2014-10-02
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| − | Test 25,50-dimension SSA-Model for transform
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| − | Start at : 2014-10-02
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| − | Test All-Belong SSA model for transform
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| − | Start at : 2014-10-02
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| − | ===SEMPRE Research===
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| − | ====Work Schedule ====
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| − | Download SEMPRE toolkit.
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| − | Start at : 2014-09-30
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| − | ====Paper related====
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| − | Semantic Parsing via Paraphrasing [http://cslt.riit.tsinghua.edu.cn/mediawiki/images/8/85/Semantic_Parsing_via_Paraphrasing.pdf]
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| − | ===Knowledge Vector===
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| − | Pre-process corpus.
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| − | Start at : 2014-09-30
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| − | ===Moses translation model===
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| − | Pre-process corpus, remove the sentence which contains rarely seen words.
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| − | Start at : 2014-09-30 <--> End at : 2014-10-02 <--> Related Data :
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| − | ===Non Linear Transform Testing===
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| − | ====Work Schedule====
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| − | Re-train best mse for test data.
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| − | Start at : 2014-10-01 <--> End at : 2014-10-02 <--> Performance is inconsistent to expectations. Best result for Non-Linear is 1e-2.
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