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| （相同用户的42个中间修订版本未显示） |
| 第1行： |
第1行： |
| − | ==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 <--> Result is :
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| − | 27.83% 46.53% 2 classify
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| − | Test 25,50-dimension SSA-Model for transform
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| − | Start at : 2014-10-02 <--> End at : 2014-10-03 <--> Result is :
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| − | 27.9% 46.6% 1 classify
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| − | 27.83% 46.53% 2 classify
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| − | 27.43% 46.53% 3 classify
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| − | 25.52% 45.83% 4 classify
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| − | 25.62% 45.83% 5 classify
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| − | 22.81% 42.51% 6 classify
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| − | 11.96% 27.43% 50 classify
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| − | Reason explain : There are some points doesn't belong to class which training data belongs to. So the
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| − | transform doesn't share correct transform matrix.
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| − | The method we want to update is just cluster the training data, and the test
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| − | the performance.
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| − | Simple cluster by 2 class.
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| − | 23.51% 43.21% 2 classify
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| − | Train set as test set
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| − | Start at : 2014-10-06 <--> End at : 2014-10-08 <--> Result is :
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| − | 63.98% 77.57% Simple 2 classify
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| − | 58.81% 73.91% Total 3 classify
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| − | Different compute state :
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| − | Start at : 2014-10-10 <--> End at : 2014-10-10 <--> Result is :
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| − | 23.51% 40.20% 7 classify
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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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| − | Use toolkit Wikipedia_Extractor [http://medialab.di.unipi.it/wiki/Wikipedia_Extractor] waiting
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| − | End at : 2014-10-03 Result :
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| − | Original corpus is about 47G and after preprocessing the corpus is almost 17.8G
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| − | Analysis corpus, and training word2vec by wikipedia.
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| − | Start at : 2014-10-03.
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| − | Design Data Structure :
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| − | { title : "", content : {Abs : [[details],[related link]], h2 : []}, category : []}
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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 <--> Result :
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| − | Original lines is 8973724, Clean corpus (remove sentences which contain words less than 10) is 6033397
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| − | Train Model.
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| − | Start at : 2014-10-02 <--> End at : 2014-10-05
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| − | Tuning Model.
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| − | Start at : 2014-10-05 <--> End at : 2014-10-10
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| − | Result Report :
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| − | 57G phrase in old translation system, 41G phrase in new system. And then testing load speed.
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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 <--> Result :
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| − | Performance is inconsistent to expectations. Best result for Non-Linear is 1e-2.
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| − | Hidden Layer : 400 15.57% 29.14% 995
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| − | 600 19.99% 36.08% 995
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| − | 800 23.32% 39.60% 995
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| − | 1200 19.19% 35.08% 995
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| − | 1400 17.09% 32.06% 995
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| − | Result : According to the result, I will test 800, 1200, 1400, and 1600 hidden layer.
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