Topic Modeling
Another application of Natural Language Processing is the Topic Modeling, which aims to extract the topics from a given document. In this section, we are going to apply this to Chapter 18 (the Cave) of the Qur'an. To do this, we are going to use the TextAnalysis.jl library. The model for this task will be the Latent Dirichlet Allocation (LDA). To start with, load the data as follows:
julia> using QuranTreejulia> using TextAnalysisjulia> using Yunirjulia> crps, tnzl = QuranData() |> load;julia> crpsdata = table(crps)Quranic Arabic Corpus (morphology) (C) 2011 Kais Dukes 128219×7 DataFrame Row │ chapter verse word part form tag features ⋯ │ Int64 Int64 Int64 Int64 String String String ⋯ ────────┼─────────────────────────────────────────────────────────────────────── 1 │ 1 1 1 1 bi P PREFIX|bi+ ⋯ 2 │ 1 1 1 2 somi N STEM|POS:N|LEM:{so 3 │ 1 1 2 1 {ll~ahi PN STEM|POS:PN|LEM:{l 4 │ 1 1 3 1 {l DET PREFIX|Al+ 5 │ 1 1 3 2 r~aHoma`ni ADJ STEM|POS:ADJ|LEM:r ⋯ 6 │ 1 1 4 1 {l DET PREFIX|Al+ 7 │ 1 1 4 2 r~aHiymi ADJ STEM|POS:ADJ|LEM:r 8 │ 1 2 1 1 {lo DET PREFIX|Al+ ⋮ │ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋱ 128213 │ 114 5 5 2 n~aAsi N STEM|POS:N|LEM:n~a ⋯ 128214 │ 114 6 1 1 mina P STEM|POS:P|LEM:min 128215 │ 114 6 2 1 {lo DET PREFIX|Al+ 128216 │ 114 6 2 2 jin~api N STEM|POS:N|LEM:jin 128217 │ 114 6 3 1 wa CONJ PREFIX|w:CONJ+ ⋯ 128218 │ 114 6 3 2 {l DET PREFIX|Al+ 128219 │ 114 6 3 3 n~aAsi N STEM|POS:N|LEM:n~a 1 column and 128204 rows omitted
You need to install Yunir.jl to successfully run the code.
using Pkg
Pkg.add("Yunir")
Pkg.add("TextAnalysis")Data Preprocessing
The first data processing will be the removal of all Disconnected Letters (like الٓمٓ ,الٓمٓصٓ, among others), Prepositions, Particles, Conjunctions, Pronouns, and Adverbs. This is done as follows:
julia> function preprocess(s::String) feat = parse(QuranFeatures, s) disletters = isfeat(feat, AbstractDisLetters) prepositions = isfeat(feat, AbstractPreposition) particles = isfeat(feat, AbstractParticle) conjunctions = isfeat(feat, AbstractConjunction) pronouns = isfeat(feat, AbstractPronoun) adverbs = isfeat(feat, AbstractAdverb) return !disletters && !prepositions && !particles && !conjunctions && !pronouns && !adverbs endpreprocess (generic function with 1 method)julia> crpstbl = filter(t -> preprocess(t.features), crpsdata[18].data)827×7 DataFrame Row │ chapter verse word part form tag features ⋯ │ Int64 Int64 Int64 Int64 String String String ⋯ ─────┼────────────────────────────────────────────────────────────────────────── 1 │ 18 1 1 2 Hamodu N STEM|POS:N|LEM:Hamod|R ⋯ 2 │ 18 1 2 2 l~ahi PN STEM|POS:PN|LEM:{ll~ah 3 │ 18 1 4 1 >anzala V STEM|POS:V|PERF|(IV)|L 4 │ 18 1 6 1 Eabodi N STEM|POS:N|LEM:Eabod|R 5 │ 18 1 9 1 yajoEal V STEM|POS:V|IMPF|LEM:ja ⋯ 6 │ 18 1 11 1 EiwajaA N STEM|POS:N|LEM:Eiwaj|R 7 │ 18 2 2 1 l~i PRP PREFIX|l:PRP+ 8 │ 18 2 2 2 yun*ira V STEM|POS:V|IMPF|(IV)|L ⋮ │ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋱ 821 │ 18 110 14 1 yarojuwA@ V STEM|POS:V|IMPF|LEM:ya ⋯ 822 │ 18 110 16 1 rab~i N STEM|POS:N|LEM:rab~|RO 823 │ 18 110 17 2 lo IMPV PREFIX|l:IMPV+ 824 │ 18 110 17 3 yaEomalo V STEM|POS:V|IMPF|LEM:Ea 825 │ 18 110 21 1 yu$oriko V STEM|POS:V|IMPF|(IV)|L ⋯ 826 │ 18 110 22 2 EibaAdapi N STEM|POS:N|LEM:EibaAda 827 │ 18 110 23 1 rab~i N STEM|POS:N|LEM:rab~|RO 1 column and 812 rows omitted
Next, we create a copy of the above data so we have the original state, and use the copy to do further data processing.
julia> crpsnew = deepcopy(crpstbl)827×7 DataFrame Row │ chapter verse word part form tag features ⋯ │ Int64 Int64 Int64 Int64 String String String ⋯ ─────┼────────────────────────────────────────────────────────────────────────── 1 │ 18 1 1 2 Hamodu N STEM|POS:N|LEM:Hamod|R ⋯ 2 │ 18 1 2 2 l~ahi PN STEM|POS:PN|LEM:{ll~ah 3 │ 18 1 4 1 >anzala V STEM|POS:V|PERF|(IV)|L 4 │ 18 1 6 1 Eabodi N STEM|POS:N|LEM:Eabod|R 5 │ 18 1 9 1 yajoEal V STEM|POS:V|IMPF|LEM:ja ⋯ 6 │ 18 1 11 1 EiwajaA N STEM|POS:N|LEM:Eiwaj|R 7 │ 18 2 2 1 l~i PRP PREFIX|l:PRP+ 8 │ 18 2 2 2 yun*ira V STEM|POS:V|IMPF|(IV)|L ⋮ │ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋱ 821 │ 18 110 14 1 yarojuwA@ V STEM|POS:V|IMPF|LEM:ya ⋯ 822 │ 18 110 16 1 rab~i N STEM|POS:N|LEM:rab~|RO 823 │ 18 110 17 2 lo IMPV PREFIX|l:IMPV+ 824 │ 18 110 17 3 yaEomalo V STEM|POS:V|IMPF|LEM:Ea 825 │ 18 110 21 1 yu$oriko V STEM|POS:V|IMPF|(IV)|L ⋯ 826 │ 18 110 22 2 EibaAdapi N STEM|POS:N|LEM:EibaAda 827 │ 18 110 23 1 rab~i N STEM|POS:N|LEM:rab~|RO 1 column and 812 rows omittedjulia> feats = crpsnew[!, :features]827-element Vector{String}: "STEM|POS:N|LEM:Hamod|ROOT:Hmd|M|NOM" "STEM|POS:PN|LEM:{ll~ah|ROOT:Alh|GEN" "STEM|POS:V|PERF|(IV)|LEM:>anzala|ROOT:nzl|3MS" "STEM|POS:N|LEM:Eabod|ROOT:Ebd|M|GEN" "STEM|POS:V|IMPF|LEM:jaEala|ROOT:jEl|3MS|MOOD:JUS" "STEM|POS:N|LEM:Eiwaj|ROOT:Ewj|M|NOM" "PREFIX|l:PRP+" "STEM|POS:V|IMPF|(IV)|LEM:>an*ara|ROOT:n*r|3MS|MOOD:SUBJ" "STEM|POS:N|LEM:l~adun|ROOT:ldn|GEN" "STEM|POS:V|IMPF|(II)|LEM:bu\$~ira|ROOT:b\$r|3MS|MOOD:SUBJ" ⋮ "STEM|POS:ADJ|LEM:wa`Hid|ROOT:wHd|MS|INDEF|NOM" "STEM|POS:V|PERF|LEM:kaAna|ROOT:kwn|SP:kaAn|3MS" "STEM|POS:V|IMPF|LEM:yarojuwA@|ROOT:rjw|3MS" "STEM|POS:N|LEM:rab~|ROOT:rbb|M|GEN" "PREFIX|l:IMPV+" "STEM|POS:V|IMPF|LEM:Eamila|ROOT:Eml|3MS|MOOD:JUS" "STEM|POS:V|IMPF|(IV)|LEM:>a\$oraka|ROOT:\$rk|3MS|MOOD:JUS" "STEM|POS:N|LEM:EibaAdat|ROOT:Ebd|F|GEN" "STEM|POS:N|LEM:rab~|ROOT:rbb|M|GEN"julia> feats = parse.(QuranFeatures, feats)827-element Vector{AbstractQuranFeature}: Stem(:N, N, AbstractQuranFeature[Lemma("Hamod"), Root("Hmd"), M, NOM]) Stem(:PN, PN, AbstractQuranFeature[Lemma("{ll~ah"), Root("Alh"), GEN]) Stem(:V, V, AbstractQuranFeature[Lemma(">anzala"), Root("nzl"), PERF, IV, 3, M, S, IND, ACT]) Stem(:N, N, AbstractQuranFeature[Lemma("Eabod"), Root("Ebd"), M, GEN]) Stem(:V, V, AbstractQuranFeature[Lemma("jaEala"), Root("jEl"), JUS, IMPF, 3, M, S, ACT, I]) Stem(:N, N, AbstractQuranFeature[Lemma("Eiwaj"), Root("Ewj"), M, NOM]) Prefix(Symbol("l:PRP+"), PRP) Stem(:V, V, AbstractQuranFeature[Lemma(">an*ara"), Root("n*r"), SUBJ, IMPF, IV, 3, M, S, ACT]) Stem(:N, N, AbstractQuranFeature[Lemma("l~adun"), Root("ldn"), GEN]) Stem(:V, V, AbstractQuranFeature[Lemma("bu\$~ira"), Root("b\$r"), SUBJ, IMPF, II, 3, M, S, ACT]) ⋮ Stem(:ADJ, ADJ, AbstractQuranFeature[Lemma("wa`Hid"), Root("wHd"), M, S, INDEF, NOM]) Stem(:V, V, AbstractQuranFeature[Lemma("kaAna"), Root("kwn"), Special("kaAn"), PERF, 3, M, S, IND, ACT, I]) Stem(:V, V, AbstractQuranFeature[Lemma("yarojuwA@"), Root("rjw"), IMPF, 3, M, S, IND, ACT, I]) Stem(:N, N, AbstractQuranFeature[Lemma("rab~"), Root("rbb"), M, GEN]) Prefix(Symbol("l:IMPV+"), IMPV) Stem(:V, V, AbstractQuranFeature[Lemma("Eamila"), Root("Eml"), JUS, IMPF, 3, M, S, ACT, I]) Stem(:V, V, AbstractQuranFeature[Lemma(">a\$oraka"), Root("\$rk"), JUS, IMPF, IV, 3, M, S, ACT]) Stem(:N, N, AbstractQuranFeature[Lemma("EibaAdat"), Root("Ebd"), F, GEN]) Stem(:N, N, AbstractQuranFeature[Lemma("rab~"), Root("rbb"), M, GEN])
Lemmatization
Using the above parsed features, we then convert the form of the tokens into its lemma. This is useful for addressing inflections.
julia> lemmas = lemma.(feats)827-element Vector{Union{Missing, String}}: "Hamod" "{ll~ah" ">anzala" "Eabod" "jaEala" "Eiwaj" missing ">an*ara" "l~adun" "bu\$~ira" ⋮ "wa`Hid" "kaAna" "yarojuwA@" "rab~" missing "Eamila" ">a\$oraka" "EibaAdat" "rab~"julia> forms1 = crpsnew[!, :form]827-element Vector{String}: "Hamodu" "l~ahi" ">anzala" "Eabodi" "yajoEal" "EiwajaA" "l~i" "yun*ira" "l~aduno" "yuba\$~ira" ⋮ "wa`HidN" "kaAna" "yarojuwA@" "rab~i" "lo" "yaEomalo" "yu\$oriko" "EibaAdapi" "rab~i"julia> forms1[.!ismissing.(lemmas)] = lemmas[.!ismissing.(lemmas)]795-element Vector{Union{Missing, String}}: "Hamod" "{ll~ah" ">anzala" "Eabod" "jaEala" "Eiwaj" ">an*ara" "l~adun" "bu\$~ira" "Eamila" ⋮ "<ila`h" "wa`Hid" "kaAna" "yarojuwA@" "rab~" "Eamila" ">a\$oraka" "EibaAdat" "rab~"
We can also use the Root features instead, which is done by simply replacing lemma.(feats) with root.(feats).
We now put back the new form to the corpus:
julia> crpsnew[!, :form] = forms1827-element Vector{String}: "Hamod" "{ll~ah" ">anzala" "Eabod" "jaEala" "Eiwaj" "l~i" ">an*ara" "l~adun" "bu\$~ira" ⋮ "wa`Hid" "kaAna" "yarojuwA@" "rab~" "lo" "Eamila" ">a\$oraka" "EibaAdat" "rab~"julia> crpsnew = CorpusData(crpsnew)Quranic Arabic Corpus (morphology) (C) 2011 Kais Dukes 827×7 DataFrame Row │ chapter verse word part form tag features ⋯ │ Int64 Int64 Int64 Int64 String String String ⋯ ─────┼────────────────────────────────────────────────────────────────────────── 1 │ 18 1 1 2 Hamod N STEM|POS:N|LEM:Hamod|R ⋯ 2 │ 18 1 2 2 {ll~ah PN STEM|POS:PN|LEM:{ll~ah 3 │ 18 1 4 1 >anzala V STEM|POS:V|PERF|(IV)|L 4 │ 18 1 6 1 Eabod N STEM|POS:N|LEM:Eabod|R 5 │ 18 1 9 1 jaEala V STEM|POS:V|IMPF|LEM:ja ⋯ 6 │ 18 1 11 1 Eiwaj N STEM|POS:N|LEM:Eiwaj|R 7 │ 18 2 2 1 l~i PRP PREFIX|l:PRP+ 8 │ 18 2 2 2 >an*ara V STEM|POS:V|IMPF|(IV)|L ⋮ │ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋱ 821 │ 18 110 14 1 yarojuwA@ V STEM|POS:V|IMPF|LEM:ya ⋯ 822 │ 18 110 16 1 rab~ N STEM|POS:N|LEM:rab~|RO 823 │ 18 110 17 2 lo IMPV PREFIX|l:IMPV+ 824 │ 18 110 17 3 Eamila V STEM|POS:V|IMPF|LEM:Ea 825 │ 18 110 21 1 >a$oraka V STEM|POS:V|IMPF|(IV)|L ⋯ 826 │ 18 110 22 2 EibaAdat N STEM|POS:N|LEM:EibaAda 827 │ 18 110 23 1 rab~ N STEM|POS:N|LEM:rab~|RO 1 column and 812 rows omitted
Tokenization
We want to summarize the Qur'an at the verse level. Thus, the token would be the verses of the corpus. From these verses, we further clean it by dediacritization and normalization of the characters:
julia> lem_vrs = verses(crpsnew)109-element Vector{String}: "Hamod {ll~ah >anzala Eabod jaEala Eiwaj" "l~i>an*ara l~adun bu\$~ira Eamila" ">an*ara qaAla {t~axa*a {ll~ah" "Eilom A^baA' kabura xaraja >afowa`h qaAla" "ba`xiE >avar 'aAmana Hadiyv" "jaEala >aroD libalawo >aHosan" "lajaAEil" "Hasiba kahof r~aqiym kaAna 'aAyap" ">awaY fitoyap kahof qaAla A^taY l~adun yuhay~i}o >amor" "Daraba >u*unN kahof" ⋮ "Hasiba kafara {t~axa*a Eabod duwn >aEotadato jahan~am ka`firuwn" "qaAla nab~a>a >axosariyn" "Dal~a saEoy Hayaw`p d~unoyaA Hasiba >aHosana" "kafara 'aAyap rab~ liqaA^' HabiTa Eamal >aqaAma qiya`map" "jazaA^' jahan~am kafara {t~axa*a 'aAyap rasuwl" "'aAmana Eamila S~a`liHa`t kaAna jan~ap firodawos" "bagaY`" "qaAla kaAna baHor kalima`t rab~ lanafida baHor nafida kalima`t rab~ jaA^'a mivol" "qaAla ba\$ar mivol >awoHaY`^ <ila`h <ila`h wa`Hid kaAna yarojuwA@ rab~ loEamila >a\$oraka EibaAdat rab~"julia> vrs = normalize.(dediac.(lem_vrs))109-element Vector{String}: "Hmd Allh Anzl Ebd jEl Ewj" "lAn*r ldn b\$r Eml" "An*r qAl Atx* Allh" "Elm AbA' kbr xrj Afwh qAl" "bxE Avr 'Amn Hdyv" "jEl ArD lblw AHsn" "ljAEl" "Hsb khf rqym kAn 'Ayh" "Awy ftyh khf qAl Aty ldn yhyy Amr" "Drb A*n khf" ⋮ "Hsb kfr Atx* Ebd dwn AEtdt jhnm kfrwn" "qAl nbA Axsryn" "Dl sEy Hywh dnyA Hsb AHsn" "kfr 'Ayh rb lqA' HbT Eml AqAm qymh" "jzA' jhnm kfr Atx* 'Ayh rswl" "'Amn Eml SlHt kAn jnh frdws" "bgy" "qAl kAn bHr klmt rb lnfd bHr nfd klmt rb jA' mvl" "qAl b\$r mvl AwHy Alh Alh wHd kAn yrjwA@ rb lEml A\$rk EbAdt rb"
Creating a TextAnalysis Corpus
To make use of the TextAnalysis.jl's APIs, we need to encode the processed Quranic Corpus to TextAnalysis.jl's Corpus. In this case, we will create a StringDocument of the verses.
julia> crps1 = Corpus(StringDocument.(vrs))A Corpus with 109 documents: * 109 StringDocument's * 0 FileDocument's * 0 TokenDocument's * 0 NGramDocument's Corpus's lexicon contains 0 tokens Corpus's index contains 0 tokens
We then update the lexicon and inverse index for efficient indexing of the corpus.
julia> update_lexicon!(crps1)julia> update_inverse_index!(crps1)
Next, we create a Document Term Matrix, which will have rows of verses and columns of words describing the verses.
julia> m1 = DocumentTermMatrix(crps1)A 109 X 360 DocumentTermMatrix
Latent Dirichlet Allocation
Finally, run LDA as follows:
julia> k = 3 # number of topics3julia> iter = 1000 # number of gibbs sampling iterations1000julia> alpha = 0.1 # hyperparameter0.1julia> beta = 0.1 # hyperparameter0.1julia> ϕ, θ = lda(m1, k, iter, alpha, beta)( ⠛⠛⠓⠛⠛⠛⠛⠛⠛⠛⠛⠛⠚⠒⠛⠛⠛⠛⠛⠛⠛⠛⠛⠛⠓⠛⠛⠒⠛⠛⠛⠛⠛⠓⠛⠛⠓⠛⠛⠓, [0.5 1.0 … 0.0 0.06666666666666667; 0.5 0.0 … 0.0 0.9333333333333333; 0.0 0.0 … 1.0 0.0])
Extract the topic for first cluster:
julia> ntopics = 1010julia> cluster_topics = Matrix(undef, ntopics, k);julia> for i = 1:k topics_idcs = sortperm(ϕ[i, :], rev=true) cluster_topics[:, i] = arabic.(m1.terms[topics_idcs][1:ntopics]) endjulia> cluster_topics10×3 Matrix{Any}: "ذ" "ء" "قال" "ء" "قال" "رب" "قال" "كان" "ء" "وجد" "رب" "جعل" "اتخ" "الله" "اتي" "ر" "استطاع" "جا" "ا" "شي" "ل" "رب" "ارض" "امن" "الله" "امر" "علم" "بلغ" "اتبع" "جنه"
Tabulating this properly would give us the following
Pkg.add("DataFrames")
Pkg.add("Latexify")
using DataFrames: DataFrame
using Latexify
mdtable(DataFrame(
topic1 = cluster_topics[:, 1],
topic2 = cluster_topics[:, 2],
topic3 = cluster_topics[:, 3]
), latex=false)| topic1 | topic2 | topic3 |
|---|---|---|
| ذ | ء | قال |
| ء | قال | رب |
| قال | كان | ء |
| وجد | رب | جعل |
| اتخ | الله | اتي |
| ر | استطاع | جا |
| ا | شي | ل |
| رب | ارض | امن |
| الله | امر | علم |
| بلغ | اتبع | جنه |
As you may have noticed, the result is not good and this is mainly due to data processing. Readers are encourage to improve this for their use case. This section simply demonstrated how TextAnalysis.jl's LDA can be used for Topic Modeling of the QuranTree.jl's corpus.
Finally, the following will extract the topic for each verse:
julia> vrs_topics = []Any[]julia> for i = 1:dtm(m1).m push!(vrs_topics, sortperm(θ[:, i], rev=true)[1]) endjulia> vrs_topics109-element Vector{Any}: 1 1 1 3 3 2 2 1 1 1 ⋮ 1 3 2 1 1 3 3 3 2