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| "raw_str": "h one-of-N i w l N i w l 1 0 i j k (1) x i (t) t i i 1 N N \u23aa \u23a9 \u23aa \u23a8 \u23a7 \u2208 = 0 if 1 ) ( i t x i (1) h (t=3) 3N x x V V V (2) j v ji j i \u03b8 j j net j (t) j y j (t) j ) ( ) ( ) ( ) ( j j i j i ji j net f t y t x v t net = + = \u2211 \u03b8 (2) f(net j ) (Activation Function) 0 1 (Sigmoid Function)", | |
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| "text": "x e x f ) (", | |
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| "raw_str": "\u2212 + = 1 1 ) ( (3) W (4) ) ( ) ( ) ( ) ( k k j k j kj k net g t y t y w t net = + = \u2211 \u03b8 (4) w kj k j \u03b8 k k net k (t) k y k (t) k 1 g(net k ) (Softmax Activation Function) (Transfer Function)", | |
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| "raw_str": "N l h i w ) | ( h l w P i = 1990", | |
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| "text": "Elman (Elman Networks) [3] (Jordan Networks) [4] (Bi-directional RNN) [ ", | |
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| "text": "( ) N (Relevance Information) w 1 w 3 w 2 S 1 1 0 0 0 0 1 0 1 0 1 0 1 0 0 0 0 0 0 0 1 1 0 1 0 1 1 0 1 0 1 1 w R 3 w R 2 w R 1 w 3 w 2 w w 1 w 3 w 5 R 1 w 1 w 3 w 2 S 1 1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 1 1 0 0 1 0 1 0 1 1 w R 3 w R 2 w R 1 w 3 w 2 w w 1 w 3 w 4 1 w R w 1 w 2 w 3 3 w R w 1 w 3 w 5 2 w R (Sentence Relevance Information) (Word Relevance Information) 1 S 1 S 1 w 3 w 2 w 1 R 1 S 1 w R 3 w R 2 w R 1 w 3 w 1 0 1 1 0 1 0 (6) \u23aa \u23a9 \u23aa \u23a8 \u23a7 \u22c5 + \u22c5 \u2212 = \u2264 < = = \u2212 t t t t R R R L t if R R if \u03b1 \u03b1 1 ' 0 ' ) 1 ( , 0 0, t (6) t R t ' t R L \u03b1 0 \u03b1 0.6 1 S w ( ) w 1 w 2 w 3 S 1 w 4 w 5 4 3 2 1 4 6 . 0 24 . 0 096 . 0 064 . 0 ' w w w w w R R R R R + + + =", | |
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| "text": "Proceedings of the Twenty-Fourth Conference on Computational Linguistics and Speech Processing (ROCLING 2012) ", | |
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| "raw_str": "(Unigram Word Vector) (Clustering) K (K-means)[7] S S (Mean Vector) (7) s L k k s s L v v s \u2211 = = 1 , (7) k s v , S k s v S s L s (Similarity) M 2 2 ) , cos( s k s k s k v u v u v U \u22c5 = (8) U k k k u k M s v s (1) (9) ) , cos( max arg s k s U v U RNNLM k = (9) k U RNNLM (9) P ) ( ) ( k RNNLM k U P U P K U \u2248 (10) k U (10) (2) S p k , \u03b3 \u2211 = = S s s k s k s k v u v u 1 ' ' , ) , cos( ) , cos( \u03b3 (11) ) ( ) ( 1 , k RNNLM S s s k k U P U P s \u2211 = \u22c5 = \u03b3 (12) ) ( k U P ) ( k RNNLMs U P s (3) (Uniform) s k, \u03b2 S s k 1 , = \u03b2 (13) S", | |
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| "sec_num": null | |
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| { | |
| "text": "Proceedings of the Twenty-Fourth Conference on Computational Linguistics and Speech Processing(ROCLING 2012)", | |
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| "text": "w Proceedings of the Twenty-Fourth Conference on Computational Linguistics and Speech Processing (ROCLING 2012)", | |
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| "content": "<table><tr><td>(Feature Extraction)</td><td>(Input Layer)</td><td>(Hidden Layer)</td><td colspan=\"2\">(Output Layer)</td></tr><tr><td/><td>(Projection Layer)</td><td/><td/></tr><tr><td/><td/><td>(Acoustic Model)</td><td/><td>(Language</td></tr><tr><td>Model)</td><td/><td/><td/></tr><tr><td>N</td><td/><td/><td/><td>(Linguistic</td></tr><tr><td>Decoding)</td><td/><td/><td/></tr><tr><td/><td>(Synapse)</td><td>N</td><td/></tr><tr><td/><td/><td colspan=\"3\">(Neural Network Language Models, NNLM)</td></tr><tr><td colspan=\"4\">(Recurrent Neural Network Language Models, RNNLM)</td><td>1994</td></tr><tr><td>[1]</td><td/><td/><td/></tr><tr><td>( )</td><td/><td/><td/></tr><tr><td colspan=\"2\">(Neural Networks)</td><td>(Artificial Intelligence)</td><td/></tr><tr><td colspan=\"2\">(Artificial Neural Networks, ANN)</td><td>1940</td><td/><td>(Neuron)</td></tr><tr><td/><td>(Perceptron)</td><td/><td/></tr><tr><td>( )</td><td/><td/><td/></tr><tr><td/><td/><td/><td/><td>[2]</td></tr></table>", | |
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| "content": "<table><tr><td colspan=\"11\">Proceedings of the Twenty-Fourth Conference on Computational Linguistics and Speech Processing (ROCLING 2012)</td></tr><tr><td>( )</td><td/><td/><td/><td/><td/><td/><td/><td/><td/><td>1</td><td>(</td><td>)</td></tr><tr><td/><td/><td/><td/><td/><td/><td colspan=\"2\">30,600 Oracle</td><td/><td/><td>(</td><td>23</td><td>100</td></tr><tr><td/><td/><td/><td/><td>)</td><td/><td colspan=\"2\">1,998</td><td/><td/><td>93.22%</td><td>3</td><td>1.5</td></tr><tr><td/><td/><td/><td/><td/><td/><td colspan=\"2\">1,997</td><td/><td/><td>1.5</td></tr><tr><td colspan=\"2\">1.2%</td><td/><td/><td/><td/><td/><td/><td/><td/><td>1.56%</td></tr><tr><td>9.52%</td><td/><td/><td/><td/><td/><td/><td/><td/><td/></tr><tr><td colspan=\"2\">( ) RNN (Global) RNNLM</td><td colspan=\"2\">(3) (RNN+BG) 232.31</td><td>(4)</td><td colspan=\"6\">(%) 85.67 236.97 100(M=100) 236.97</td><td>(%) 85.17% 85.17</td><td>(%) RNN -</td><td>(%) -</td></tr><tr><td>( )</td><td>0 0.1</td><td/><td>230.05</td><td/><td colspan=\"2\">84.29 234.93 85.65</td><td/><td/><td/><td>85.88</td><td>84.29 85.36 85.55</td><td>(Edit distance) 84.29 0.19 85.59</td><td>1.26</td></tr><tr><td/><td>0.2</td><td/><td>230.13</td><td/><td colspan=\"2\">234.75 85.73</td><td/><td/><td colspan=\"2\">Mikolov 85.83</td><td>[11] 85.34 85.73</td><td>Recurrent Neural 0.17 1.16 0.24% 85.71</td></tr><tr><td colspan=\"11\">Network Language Modeling Toolkit (RNNLM) 0.3 85.86</td><td>85.86</td><td>85.78</td></tr><tr><td>1</td><td>0.4</td><td/><td>230.12</td><td/><td colspan=\"2\">234.83 85.87</td><td/><td/><td/><td>85.77</td><td>85.40 85.81</td><td>0.23</td><td>85.69</td><td>1.52</td></tr><tr><td colspan=\"2\">0 RNNLM 0.5</td><td/><td/><td/><td/><td>85.79</td><td/><td/><td/><td>85.77</td><td>85.60</td></tr><tr><td/><td>0.6</td><td/><td/><td/><td/><td>85.61</td><td/><td/><td/><td>85.50</td><td>85.52</td></tr><tr><td/><td>0 0.7</td><td/><td/><td>1</td><td colspan=\"3\">85.34% 84.29 85.39</td><td/><td/><td>84.29 85.35</td><td>84.29 85.35</td></tr><tr><td/><td>0.1 0.8</td><td/><td/><td/><td/><td>85.64 85.15</td><td/><td/><td/><td>85.68 85.13</td><td>100 85.58 85.17% 85.05</td></tr><tr><td colspan=\"3\">0.04% 0.2 0.9</td><td>100</td><td/><td/><td>85.87 84.71</td><td>4</td><td colspan=\"3\">0.08%</td><td>85.88 84.66</td><td>1</td><td>85.94 84.63</td><td>0.14%</td></tr><tr><td/><td>0.3 1</td><td/><td/><td/><td/><td>85.86 82.75</td><td/><td/><td/><td>85.92 83.39</td><td>85.94 83.31</td></tr><tr><td/><td>0.4 0.5</td><td/><td/><td/><td>P</td><td colspan=\"2\">) 85.90 ( k U = \u03b2 1 1 S s k \u2211 = 1 85.81</td><td>,</td><td>s</td><td>s 0 rnnlm RNN P \u22c5</td><td>( 0 U</td><td>) 85.91 k 85.78</td><td>85.81 85.74</td><td>(14) 0.19%</td></tr><tr><td colspan=\"4\">0.17% 0.23% 0.6</td><td/><td/><td colspan=\"5\">1.26% 1.16% 1.52% 85.70 85.57</td><td>85.44</td></tr><tr><td/><td>0.7</td><td/><td/><td/><td>1</td><td>85.46</td><td>0</td><td/><td colspan=\"2\">(%) (%)</td><td>(%) 85.43</td><td>(%)</td><td>(%)</td><td>85.29</td><td>(%) (%)</td></tr><tr><td/><td>0.8</td><td/><td/><td/><td/><td>1 85.05</td><td colspan=\"4\">85.33</td><td>84.97</td><td>0.16</td><td>84.99 1.06</td></tr><tr><td/><td>0.9</td><td/><td>450.93</td><td/><td colspan=\"2\">459.06 84.60</td><td colspan=\"4\">84.73 85.31</td><td>83.61 84.55 0.14</td><td>-</td><td>84.52 0.94</td><td>-</td></tr><tr><td>( ) (BG)</td><td>1</td><td/><td/><td/><td/><td>82.56</td><td colspan=\"4\">85.32</td><td>82.62</td><td>0.15</td><td>82.59 1.02</td></tr><tr><td>RNN</td><td/><td/><td>607.07 85.5</td><td/><td colspan=\"2\">623.50</td><td/><td colspan=\"3\">(Mandarin Across Taiwan-Broadcast News, 82.31 82.41 -1.2 -7.32</td></tr><tr><td colspan=\"2\">MATBN)[8] RNN+BG</td><td/><td>232.31 85.45</td><td/><td colspan=\"2\">2001 236.97</td><td/><td colspan=\"3\">2003 85.67 (%) (%)</td><td>85.17 (%) (%)</td><td>1.56 (%) (%)</td><td>(SLG) 9.52 (%) (%)</td></tr><tr><td colspan=\"3\">(PTS) Oracle RNN RNN ) 23 ) N</td><td colspan=\"8\">197 2002 -236.97 236.97 1,997 ( 1.5 2001 232.31 -85.4 232.31 (RNN) 2001 ) 30,600 93.22 85.67 85.67 (%) 2002 2001 2002 92.66 85.17 85.17 1,998 ( 1.5 (%) 85.25 623.50 223.63 229.01 85.63 85.09 -0.08 --3 -30,600 ( 23 ---(%) ) 30,632 ( (Central News -0.56 230.51 236.45 85.71 85.21 0.04 0.27 85.3 6 85.41 0.24 1.60 85.35 9 229.72 234.35 85.84 85.26 0.09 0.58 85.24 0.07 0.46 12 231.42 236.19 85.83 85.34 0.17 1.14 85.29 0.12 0.82 N</td></tr><tr><td colspan=\"3\">Agency, CNA) (BG) 1 1</td><td>226.04 85.2 229.98</td><td/><td colspan=\"2\">231.19 234.62</td><td/><td/><td colspan=\"2\">85.56 85.86 (out-of-vocabulary, OOV) 85.03 85.34</td><td>-0.14 0.17</td><td>-0.95 1.16</td></tr><tr><td colspan=\"2\">0 0</td><td/><td/><td/><td colspan=\"6\">SRI Language Modeling Toolkit (SRILM)[9]</td></tr><tr><td colspan=\"3\">Katz Back-off</td><td/><td colspan=\"3\">[10]</td><td/><td/><td/></tr><tr><td/><td/><td/><td colspan=\"4\">(RNN+BG)</td><td/><td/><td/></tr></table>", | |
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| "num": null, | |
| "text": "Proceedings of the Twenty-Fourth Conference on Computational Linguistics and Speech Processing(ROCLING 2012)" | |
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| } | |
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