Mat*_*att 8 machine-learning mallet
当我在主题建模开发人员指南中尝试示例代码时,我真的想了解该代码输出的含义.
首先在运行过程中,它给出:
Coded LDA: 10 topics, 4 topic bits, 1111 topic mask
max tokens: 148
total tokens: 1333
<10> LL/token: -9,24097
<20> LL/token: -9,1026
<30> LL/token: -8,95386
<40> LL/token: -8,75353
0 0,5 battle union confederate tennessee american states
1 0,5 hawes sunderland echo war paper commonwealth
2 0,5 test including cricket australian hill career
3 0,5 average equipartition theorem law energy system
4 0,5 kentucky army grant gen confederates buell
5 0,5 years yard national thylacine wilderness parks
6 0,5 gunnhild norway life extinct gilbert thespis
7 0,5 zinta role hindi actress film indian
8 0,5 rings south ring dust 2 uranus
9 0,5 tasmanian back time sullivan london century
<50> LL/token: -8,59033
<60> LL/token: -8,63711
<70> LL/token: -8,56168
<80> LL/token: -8,57189
<90> LL/token: -8,46669
0 0,5 battle union confederate tennessee united numerous
1 0,5 hawes sunderland echo paper commonwealth early
2 0,5 test cricket south australian hill england
3 0,5 average equipartition theorem law energy system
4 0,5 kentucky army grant gen war time
5 0,5 yard national thylacine years wilderness tasmanian
6 0,5 including gunnhild norway life time thespis
7 0,5 zinta role hindi actress film indian
8 0,5 rings ring dust 2 uranus survived
9 0,5 back london modern sullivan gilbert needham
<100> LL/token: -8,49005
<110> LL/token: -8,57995
<120> LL/token: -8,55601
<130> LL/token: -8,50673
<140> LL/token: -8,46388
0 0,5 battle union confederate tennessee war united
1 0,5 sunderland echo paper edward england world
2 0,5 test cricket south australian hill record
3 0,5 average equipartition theorem energy system kinetic
4 0,5 hawes kentucky army gen grant confederates
5 0,5 years yard national thylacine wilderness tasmanian
6 0,5 gunnhild norway including king life devil
7 0,5 zinta role hindi actress film indian
8 0,5 rings ring dust 2 uranus number
9 0,5 london sullivan gilbert thespis back mother
<150> LL/token: -8,51129
<160> LL/token: -8,50269
<170> LL/token: -8,44308
<180> LL/token: -8,47441
<190> LL/token: -8,62186
0 0,5 battle union confederate grant tennessee numerous
1 0,5 sunderland echo survived paper edward england
2 0,5 test cricket south australian hill park
3 0,5 average equipartition theorem energy system law
4 0,5 hawes kentucky army gen time confederates
5 0,5 yard national thylacine years wilderness tasmanian
6 0,5 gunnhild including norway life king time
7 0,5 zinta role hindi actress film indian
8 0,5 rings ring dust 2 uranus number
9 0,5 back london sullivan gilbert thespis 3
<200> LL/token: -8,54771
Total time: 6 seconds
所以问题1:什么是"编码LDA:10个主题,4位的话题,1111话题面具"中的第一行是什么意思？我只知道"10个主题"是什么.
问题2:"<10> LL /令牌:-9,24097 <20> LL /令牌:-9,1026 <30> LL /令牌:-8,95386 <40> LL /令牌中的LL /令牌是什么: - 8,75353"是什么意思？它似乎是吉布斯采样的一个指标.但是它不是单调增加的吗？
之后,打印出以下内容:
elizabeth-9 needham-9 died-7 3-9 1731-6 mother-6 needham-9 english-7 procuress-6 brothel-4 keeper-9 18th-8.......
0 0.008 battle (8) union (7) confederate (6) grant (4) tennessee (4)
1 0.008 sunderland (6) years (6) echo (5) survived (3) paper (3)
2 0.040 test (6) cricket (5) hill (4) park (3) career (3)
3 0.008 average (6) equipartition (6) system (5) theorem (5) law (4)
4 0.073 hawes (7) kentucky (6) army (5) gen (4) war (4)
5 0.008 yard (6) national (6) thylacine (5) wilderness (4) tasmanian (4)
6 0.202 gunnhild (5) norway (4) life (4) including (3) king (3)
7 0.202 zinta (4) role (3) hindi (3) actress (3) film (3)
8 0.040 rings (10) ring (3) dust (3) 2 (3) uranus (3)
9 0.411 london (4) sullivan (3) gilbert (3) thespis (3) back (3)
0 0.55
这部分的第一行可能是令牌主题分配,对吧？
问题3:对于第一个主题,
0 0.008 battle (8) union (7) confederate (6) grant (4) tennessee (4)
0.008被称为"主题分布",是否是整个语料库中该主题的分布？然后似乎存在冲突:如上所示的主题0将使其令牌出现在copus 8 + 7 + 6 + 4 + 4 + ...次; 并且在比较中,主题7在语料库中识别出4 + 3 + 3 + 3 + 3 ......次.因此,主题7应该比主题0具有更低的分布.这是我无法理解的.更进一步,最后的"0 0.55"是什么？
非常感谢您阅读这篇长篇文章.希望你能回答它并希望这对其他对Mallet感兴趣的人有所帮助.
最好
我认为我不足以给出一个非常完整的答案,但是这里有一些答案......对于Q1,您可以检查一些代码以查看这些值是如何计算的.对于Q2,LL是模型的对数可能性除以令牌的总数,这是对数据给出模型的可能性的度量.增加值意味着模型正在改进.这些也可以在R主题建模的包中找到.Q2,是的,我认为第一行是正确的.Q3,好问题,我不是很清楚,也许(x)是某种索引,令牌频率似乎不太可能......大概这些都是某种类型的诊断.
可以获得一组更有用的诊断,利用bin\mallet run cc.mallet.topics.tui.TopicTrainer ...your various options... --diagnostics-file diagnostics.xml它可以产生大量的主题质量度量.他们绝对值得一试.
关于所有这些的完整故事,我建议给普林斯顿的David Mimno写一封电子邮件,他是MALLET的主要维护者,或者通过http://blog.gmane.org/gmane列表给他写信..comp.ai.mallet.devel然后在这里发回答案给我们这些好奇MALLET内部运作的人...