From f1fa3ef0ff7abb18843f8ad0baeb137e66c26452 Mon Sep 17 00:00:00 2001 From: Eyob Yirdaw Date: Mon, 28 Jan 2019 17:31:11 +0300 Subject: [PATCH 1/6] update readme --- README.md | 35 ++++++++++++++++++++++++++++++++++- docs/assets/singnet-logo.jpg | Bin 0 -> 15043 bytes 2 files changed, 34 insertions(+), 1 deletion(-) create mode 100644 docs/assets/singnet-logo.jpg diff --git a/README.md b/README.md index 865a0e5..470dbed 100644 --- a/README.md +++ b/README.md @@ -1 +1,34 @@ -# topic-analysis \ No newline at end of file +[![SingnetLogo](docs/assets/singnet-logo.jpg?raw=true 'SingularityNET')](https://singularitynet.io/) + +# Topic Analysis Services + + +This repository contains various topic analysis services for SingularityNET. The topic analysis methods would include: + +* Latent semantic analysis (LSA) +* Probabilistic latent semantic analysis (PLSA) +* Latent Diritchlet allocation (LDA) +* LDA2vec + +The services are wrapped using gRPC. + +The user provides a collection of documents for [topic analysis](#https://en.wikipedia.org/wiki/Topic_model) and the service would return discoverd topics. Each topic +consists of a collection of words that represent a given topic. + + +## Resources + +LSA: + * [Original paper: Indexing by Latent Semantic Analysis](#http://citeseer.ist.psu.edu/viewdoc/download?doi=10.1.1.108.8490&rep=rep1&type=pdf) + * [Wikipedia entry] (#https://en.wikipedia.org/wiki/Latent_semantic_analysis) + +PLSA: + * [Original paper: Unsupervised Learning by Probabilistic Latent Semantic Analysis](#http://www.cs.bham.ac.uk/~pxt/IDA/plsa.pdf) + * [Wikipedia entry] (#https://en.wikipedia.org/wiki/Probabilistic_latent_semantic_analysis) + +LDA: + * [Original paper: Latent Dirichlet Allocation](#http://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf) + * [Wikipedia entry] (#https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation) + +LDA2vec: + * [Original paper: Mixing Dirichlet Topic Models and Word Embeddings to Make lda2vec](#https://arxiv.org/abs/1605.02019) \ No newline at end of file diff --git a/docs/assets/singnet-logo.jpg b/docs/assets/singnet-logo.jpg new file mode 100644 index 0000000000000000000000000000000000000000..86292575dcf6338bdde83c379269fc24ab31b816 GIT binary patch literal 15043 zcmdUV2UHZzwr)=#=bVG&oU_1?gNWpeq#@@hQIg~+Ac!EKfTHA_1<9ZY5+r9N2L}}# zN%Kbk^Ly^P_pW#EJ?p)<)~lK7{-$Qv?%LJ0cYRges5#UcKzvJ0Qw;!t06+u%0Z{9J zziN<+BLHY?13Ul#U;|JPEr5YWXc0gjME3`)fw%z3?{Y8z#Jd2{f5_ZHpMNE^w%>LB zI)k5q|CRtPJ%jv>0k2=8s3Sm1*V)V8%g@=%n^{yq2#`|J)Q0}jh)%yT`)^1*&5$ym z4%9>UB5`sLkxJpH4FYOSWo2t!eH}H;+p50}A;7lv_I3vo1AvF8zpuW!60@n9IWyJ< zfD14HSb#LZV+;55R@BwK{Y&TH=imDO9L{Ebw;h<`|E23s@qZ7H*gN>a(Z=hd#qI3< z;2vn)h|UPOx350{KuXc&tbzXCzwj^`ll!6@h{j95u+v`{@eAAi#=rZ4+}BWF833Tv z=v#tsYwPO-0QhWZ8RmO%C$t`X6*Ly|w0H4DWAtx89`0;wkH(E?%;(|Z^$V||F_+!H z(c|=Qu&u4#ADycoe`8PgJHPJtZ=H@VYMOsz z7k^E|zp;a#s=?pb*GvD;wH$qw75^@`^+lt9=m~H$`cv-gp!r9Ch_j)|-!k`HjDLB= z@9X-x8T@Hah_mvaa^C>`Kkaw$R9E_2-qBa>kL@0Q=+b}aady%CQ`g_w@Rx`E)(^M+ zXZsyI75}Nz&-72*>>X58|CV>~H2Pz&x4+V#Yk9l>_Kn{<9o*Ibl>6N?_*2&(Js|(q z@OJr_%`?37eSQR|15J2LdOMkAApbkSq5JKfc6LI;dA`Y zGOic^AP@wAK0SDV@4dhDL4Tbv(9wVhAO~mwMt~LI0{GEwlK^f43g~v;0(1aFzznbg z;D9sW0r&!eKo}4O!~w~`Lm(T-1B!qW;1y5>)B`O*C(r{70`Gy3z$ai4SOd0z0{{V> z0T&<;2n$32A_LKYm_Y0xUXTb#3M3Cw1>FYegUmoSASaL~C;$`&dH_lWWrFfR&q1$2 zb)Ys-4`>850h$M`gZ4mQLFZry7#~ayW&m@51;J8aMX(mw5PTQx1oi=kfMdXE;9PJq zxDwn9?g76Ce*&+855T7o0D=dhgxrAeL!=av;T!YDha|2r>m( zgB(JBLNTFaP-ZAU^d?jTY6OKteW2mc6lfmw6|@=J51oRpLlMwx3<3;#3|VKZTiV5?!9V|!vpW9ML(V|QUsV((yI z;1J`mzI~;=1BS;pXC2;|}01;eNxz!(+jd z#?!-d!HdMp#jC{|##_hxiBF2pgRhKli64NUhF^yN7JnZ98vy|U2Y~{CIe|Yx8o_IV zK7thjBq1500HG$KJz)f4K4CNAB;hd;4iP(%B9RqQ2vH7E1JO9qAu$dy2eC4-Epa$; 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G^ZyHD?3l>_ literal 0 HcmV?d00001 From 8ce140a7abf3fd0391fcb56facea06df09d2b557 Mon Sep 17 00:00:00 2001 From: Eyob Yirdaw Date: Mon, 28 Jan 2019 17:33:49 +0300 Subject: [PATCH 2/6] update readme 2 --- README.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 470dbed..55f9fa3 100644 --- a/README.md +++ b/README.md @@ -19,16 +19,16 @@ consists of a collection of words that represent a given topic. ## Resources LSA: - * [Original paper: Indexing by Latent Semantic Analysis](#http://citeseer.ist.psu.edu/viewdoc/download?doi=10.1.1.108.8490&rep=rep1&type=pdf) + * [Original paper: Indexing by Latent Semantic Analysis](http://citeseer.ist.psu.edu/viewdoc/download?doi=10.1.1.108.8490&rep=rep1&type=pdf) * [Wikipedia entry] (#https://en.wikipedia.org/wiki/Latent_semantic_analysis) PLSA: - * [Original paper: Unsupervised Learning by Probabilistic Latent Semantic Analysis](#http://www.cs.bham.ac.uk/~pxt/IDA/plsa.pdf) + * [Original paper: Unsupervised Learning by Probabilistic Latent Semantic Analysis](http://www.cs.bham.ac.uk/~pxt/IDA/plsa.pdf) * [Wikipedia entry] (#https://en.wikipedia.org/wiki/Probabilistic_latent_semantic_analysis) LDA: - * [Original paper: Latent Dirichlet Allocation](#http://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf) + * [Original paper: Latent Dirichlet Allocation](http://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf) * [Wikipedia entry] (#https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation) LDA2vec: - * [Original paper: Mixing Dirichlet Topic Models and Word Embeddings to Make lda2vec](#https://arxiv.org/abs/1605.02019) \ No newline at end of file + * [Original paper: Mixing Dirichlet Topic Models and Word Embeddings to Make lda2vec](https://arxiv.org/abs/1605.02019) \ No newline at end of file From 33549f7394ccd7ccb5114a8953390414304e2258 Mon Sep 17 00:00:00 2001 From: Eyob Yirdaw Date: Mon, 28 Jan 2019 17:35:59 +0300 Subject: [PATCH 3/6] update readme 3 --- README.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index 55f9fa3..2189418 100644 --- a/README.md +++ b/README.md @@ -19,16 +19,16 @@ consists of a collection of words that represent a given topic. ## Resources LSA: - * [Original paper: Indexing by Latent Semantic Analysis](http://citeseer.ist.psu.edu/viewdoc/download?doi=10.1.1.108.8490&rep=rep1&type=pdf) - * [Wikipedia entry] (#https://en.wikipedia.org/wiki/Latent_semantic_analysis) + * Research paper: [Indexing by Latent Semantic Analysis](http://citeseer.ist.psu.edu/viewdoc/download?doi=10.1.1.108.8490&rep=rep1&type=pdf) + * [Wikipedia entry] (https://en.wikipedia.org/wiki/Latent_semantic_analysis) PLSA: - * [Original paper: Unsupervised Learning by Probabilistic Latent Semantic Analysis](http://www.cs.bham.ac.uk/~pxt/IDA/plsa.pdf) - * [Wikipedia entry] (#https://en.wikipedia.org/wiki/Probabilistic_latent_semantic_analysis) + * Research aper: [Unsupervised Learning by Probabilistic Latent Semantic Analysis](http://www.cs.bham.ac.uk/~pxt/IDA/plsa.pdf) + * [Wikipedia entry] (https://en.wikipedia.org/wiki/Probabilistic_latent_semantic_analysis) LDA: - * [Original paper: Latent Dirichlet Allocation](http://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf) - * [Wikipedia entry] (#https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation) + * Research paper: [Latent Dirichlet Allocation](http://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf) + * [Wikipedia entry] (https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation) LDA2vec: - * [Original paper: Mixing Dirichlet Topic Models and Word Embeddings to Make lda2vec](https://arxiv.org/abs/1605.02019) \ No newline at end of file + * Research paper: [Mixing Dirichlet Topic Models and Word Embeddings to Make lda2vec](https://arxiv.org/abs/1605.02019) \ No newline at end of file From d3b0fcfcbbe888ab241685fd27cd351c02634ef2 Mon Sep 17 00:00:00 2001 From: Eyob Yirdaw Date: Mon, 28 Jan 2019 17:36:52 +0300 Subject: [PATCH 4/6] update readme 4 --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 2189418..25c29f8 100644 --- a/README.md +++ b/README.md @@ -20,15 +20,15 @@ consists of a collection of words that represent a given topic. LSA: * Research paper: [Indexing by Latent Semantic Analysis](http://citeseer.ist.psu.edu/viewdoc/download?doi=10.1.1.108.8490&rep=rep1&type=pdf) - * [Wikipedia entry] (https://en.wikipedia.org/wiki/Latent_semantic_analysis) + * [Wikipedia entry](https://en.wikipedia.org/wiki/Latent_semantic_analysis) PLSA: * Research aper: [Unsupervised Learning by Probabilistic Latent Semantic Analysis](http://www.cs.bham.ac.uk/~pxt/IDA/plsa.pdf) - * [Wikipedia entry] (https://en.wikipedia.org/wiki/Probabilistic_latent_semantic_analysis) + * [Wikipedia entry](https://en.wikipedia.org/wiki/Probabilistic_latent_semantic_analysis) LDA: * Research paper: [Latent Dirichlet Allocation](http://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf) - * [Wikipedia entry] (https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation) + * [Wikipedia entry](https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation) LDA2vec: * Research paper: [Mixing Dirichlet Topic Models and Word Embeddings to Make lda2vec](https://arxiv.org/abs/1605.02019) \ No newline at end of file From 4c22633b500b57ade3ce5ccce0f27e529cbe6624 Mon Sep 17 00:00:00 2001 From: Eyob Yirdaw Date: Mon, 28 Jan 2019 17:37:47 +0300 Subject: [PATCH 5/6] update readme 5 --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 25c29f8..cc94aed 100644 --- a/README.md +++ b/README.md @@ -12,7 +12,7 @@ This repository contains various topic analysis services for SingularityNET. The The services are wrapped using gRPC. -The user provides a collection of documents for [topic analysis](#https://en.wikipedia.org/wiki/Topic_model) and the service would return discoverd topics. Each topic +The user provides a collection of documents for [topic analysis](https://en.wikipedia.org/wiki/Topic_model) and the service would return discoverd topics. Each topic consists of a collection of words that represent a given topic. From 72a0f04c0b8ba3269ad63a1ecd14039eb5678281 Mon Sep 17 00:00:00 2001 From: Eyob Yirdaw Date: Mon, 28 Jan 2019 17:38:54 +0300 Subject: [PATCH 6/6] update readme 6 --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index cc94aed..316af29 100644 --- a/README.md +++ b/README.md @@ -3,7 +3,7 @@ # Topic Analysis Services -This repository contains various topic analysis services for SingularityNET. The topic analysis methods would include: +This repository contains various [topic analysis](https://en.wikipedia.org/wiki/Topic_model) services for SingularityNET. The topic analysis methods would include: * Latent semantic analysis (LSA) * Probabilistic latent semantic analysis (PLSA) @@ -12,7 +12,7 @@ This repository contains various topic analysis services for SingularityNET. The The services are wrapped using gRPC. -The user provides a collection of documents for [topic analysis](https://en.wikipedia.org/wiki/Topic_model) and the service would return discoverd topics. Each topic +The user provides a collection of documents for topic analysis and the service would return discoverd topics. Each topic consists of a collection of words that represent a given topic.