C++ implement of Tomas Mikolov's word/document embedding. You may want to feel the basic idea from Mikolov's two orignal papers, word2vec and doc2vec. More recently, Andrew M. Dai etc from Google reported its power in more detail
- g++
- gtest 1.7+ if you wanna run test suites
There are a few pretty nice projects like google's word2vec and gensim has already implemented the algorithm, from which I learned quite a lot. However, I rewrite it for following reasons:
-
speed. I believe c/c++ version has the best speed on CPU. In fact, according to test on same machine and runing with same setting, Fully optimized gensim verison got ~100K words/s, whereas c++ version achieved 200K words/s
-
functionality. After I awared the advantage of c/c++ in term of efficiency, I found few c/c++ project implements both word and document embedding. Moreover, some important application for these embedding have not been fully developed, such as online infer document, likelihood of document, wmd and keyword extraction
-
scalability. I found that it's extremely slow when doing task like "most similar" on large data. One straight-forward way is distributing, the other is putting on GPUs. For these purposes, I prefer to design data structrue by myself
I lauched an expriment on 7,987,287 chinese academic papers' title, which has ~8 words on average.
Following will show you some of the result and how-to
prepare trainning file in format like this:
_*23134 Distributed Representations of Sentences and Documents
_*31356 Document Classification by Inversion of Distributed Language Representations
_*31345 thanks to deep learning, bring us to a new sight
...
train model:
Doc2Vec doc2vec;
doc2vec.train("path-to-taining-file", 50, 0, 1, 0, 15, 10, 0.025, 1e-5, 3, 6);
save model if you want:
FILE * fout = fopen("path-to-model", "wb");
doc2vec.save(fout);
fclose(fout);
load model from file:
FILE * fin = fopen("path-to-model", "rb");
doc2vec.load(fin);
fclose(fin);
similar words:
knn_item_t knn_items[10];
doc2vec.word_knn_words("机器学习", knn_items, 10);
==============机器学习===============
分类器 -> 0.938366
贝叶斯 -> 0.934191
上下文 -> 0.931081
推理 -> 0.927534
向量空间 -> 0.925619
抽取 -> 0.922363
svm -> 0.919414
视图 -> 0.908361
决策树 -> 0.906520
bayesian -> 0.904839
similar documents:
doc2vec.doc_knn_docs("_*1000045631_图书馆信息服务评价指标体系的构建", knn_items, 10);
==============_*1000045631_图书馆信息服务评价指标体系的构建===============
_*43541596_公共图书馆政府信息服务绩效评价指标体系的构建 -> 0.841705
_*32615763_图书馆电子服务质量评价指标体系构建 -> 0.833922
_*34860843_图书馆信息服务绩效评价指标体系研究 -> 0.823280
_*1001040838_图书馆信息资源建设评价指标体系构建研究 -> 0.822471
_*33909320_图书馆信息服务创新研究 -> 0.814595
_*3555700_图书馆与信息服务 -> 0.809152
_*45080320_图书馆信息服务创新理论基础研究 -> 0.808122
_*35544806_高校数字图书馆评价指标体系研究 -> 0.807870
_*6427226_图书馆信息服务创新体系 -> 0.804898
_*22765211_基于网格的图书馆信息服务 -> 0.802323
infer sentence online and get similar documents:
//a relative good case
doc.m_word_num = 11;
buildDoc(&doc, "光伏", "并网发电", "系统", "中", "逆变器", "的", "设计", "与", "控制", "方法", "</s>");
doc2vec.sent_knn_docs(&doc, knn_items, 10, infer_vector);
==============光伏并网发电系统中逆变器的设计与控制方法===============
_*23050250_光伏并网发电系统中逆变器的设计与控制方法 -> 0.923025
_*41522718_基于级联逆变器的光伏并网发电系统控制策略 -> 0.832144
_*7724486_光伏并网发电系统的控制方法 -> 0.787163
_*8031480_小功率光伏并网逆变系统的研制 -> 0.783204
_*7157773_小功率光伏并网逆变器控制的设计 -> 0.782370
_*1001026414_光伏并网发电系统的控制策略研究 -> 0.771843
_*33269514_光伏并网发电系统的MPPT-电压控制策略仿真 -> 0.763618
_*37612602_光伏并网逆变器的设计与控制 -> 0.763109
_*20065898_RTDS应用于直流控制保护系统的仿真试验 -> 0.762502
_*41961421_光伏并网发电系统中孤岛现象的研究 -> 0.760718
//a pretty bad case
doc.m_word_num = 5;
buildDoc(&doc, "遥感信息", "发展战略", "与", "对策", "</s>");
doc2vec.sent_knn_docs(&doc, knn_items, 10, infer_vector);
==============遥感信息发展战略与对策===============
_*29022751_**水稻遥感信息获取区划研究 -> 0.717881
_*21205970_我国观光果园的发展现状、存在问题与对策 -> 0.716743
_*1308712_**土地资源态势潜力及对策 -> 0.714717
_*9456568_我国分布式能源发展战略探讨 -> 0.705569
_*11726518_论**能源发展战略及对策 -> 0.703126
_*4924333_我国城市环境信息化建设发展战略探讨 -> 0.701261
_*6419896_农业结构调整的障碍与对策分析 -> 0.698340
_*21638304_**企业对外直接投资障碍与对策分析 -> 0.697608
_*4505223_**花卉产业现状及发展战略 -> 0.697182
_*18092739_**能源发展战略与石油安全对策研究 -> 0.693231
somthing more interesting is that task of keyword extraction could also benefit from it(via leave-one-out, see codes in test suites):
================ 遥感信息发展战略与对策 ===================
遥感信息 -> -16.50
发展战略 -> -21.81
对策 -> -25.22
与 -> -63.49
================ 光伏并网发电系统中逆变器的设计与控制方法 ===================
逆变器 -> -167.38
并网发电 -> -218.84
光伏 -> -226.92
系统 -> -284.51
控制 -> -413.66
设计 -> -419.63
的 -> -535.65
与 -> -535.65
方法 -> -535.65
中 -> -577.80
further more, I use these word weights to modify the wmd, impoving performance of document similarity:
//the bad case methioned above turned to be acceptable
doc.m_word_num = 6;
buildDoc(&doc, "遥感信息", "发展战略", "与", "对策", "</s>");
doc2vec.wmd()->sent_knn_docs_ex(&doc, knn_items, K);
==============遥感信息发展战略与对策===============
_*6448414_信息时代的经济发展与遥感信息技术集成 -> -0.100316
_*10541315_林业资源遥感信息的尺度问题研究 -> -0.130996
_*40985040_青藏高原湖泊遥感信息提取及湖面动态变化趋势研究 -> -0.139246
_*29022751_**水稻遥感信息获取区划研究 -> -0.146397
_*38794473_陕西省土地利用／覆盖变化以及驱动机制分析——基于遥感信息与 -> -0.150292
_*9827563_基于遥感信息的土地资源可持续利用研究 -> -0.163499
_*43973701_基于遥感信息与水稻模型相结合对镇江地区水稻种植面积与产量的 -> -0.171528
_*10487843_济南南部地区城市扩展遥感信息动态分析 -> -0.174100
_*8443457_论土地利用与覆盖变化遥感信息提取技术框架 -> -0.176658
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