## Creating a Document Term Matrix

Often we want to represent documents as a matrix of word counts so that we can apply linear algebra operations and statistical techniques. Before we do this, we need to update the lexicon:

julia> crps = Corpus([StringDocument("To be or not to be"),
StringDocument("To become or not to become")])

julia> update_lexicon!(crps)

julia> m = DocumentTermMatrix(crps)
A 2 X 6 DocumentTermMatrix

A DocumentTermMatrix object is a special type. If you would like to use a simple sparse matrix, call dtm() on this object:

julia> dtm(m)
2×6 SparseArrays.SparseMatrixCSC{Int64,Int64} with 10 stored entries:
[1, 1]  =  1
[2, 1]  =  1
[1, 2]  =  2
[2, 3]  =  2
[1, 4]  =  1
[2, 4]  =  1
[1, 5]  =  1
[2, 5]  =  1
[1, 6]  =  1
[2, 6]  =  1

If you would like to use a dense matrix instead, you can pass this as an argument to the dtm function:

julia> dtm(m, :dense)
2×6 Array{Int64,2}:
1  2  0  1  1  1
1  0  2  1  1  1

## Creating Individual Rows of a Document Term Matrix

In many cases, we don't need the entire document term matrix at once: we can make do with just a single row. You can get this using the dtv function. Because individual's document do not have a lexicon associated with them, we have to pass in a lexicon as an additional argument:

julia> dtv(crps[1], lexicon(crps))
1×6 Array{Int64,2}:
1  2  0  1  1  1

## The Hash Trick

The need to create a lexicon before we can construct a document term matrix is often prohibitive. We can often employ a trick that has come to be called the "Hash Trick" in which we replace terms with their hashed valued using a hash function that outputs integers from 1 to N. To construct such a hash function, you can use the TextHashFunction(N) constructor:

julia> h = TextHashFunction(10)
TextHashFunction(hash, 10)

You can see how this function maps strings to numbers by calling the index_hash function:

julia> index_hash("a", h)
8

julia> index_hash("b", h)
7

Using a text hash function, we can represent a document as a vector with N entries by calling the hash_dtv function:

julia> hash_dtv(crps[1], h)
1×10 Array{Int64,2}:
0  2  0  0  1  3  0  0  0  0

This can be done for a corpus as a whole to construct a DTM without defining a lexicon in advance:

julia> hash_dtm(crps, h)
2×10 Array{Int64,2}:
0  2  0  0  1  3  0  0  0  0
0  2  0  0  1  1  0  0  2  0

Every corpus has a hash function built-in, so this function can be called using just one argument:

julia> hash_dtm(crps)
2×100 Array{Int64,2}:
0  0  0  0  0  0  0  0  0  0  0  0  0  …  0  0  0  0  0  0  0  0  0  0  0  0
0  0  0  0  0  0  0  0  2  0  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0

Moreover, if you do not specify a hash function for just one row of the hash DTM, a default hash function will be constructed for you:

julia> hash_dtv(crps[1])
1×100 Array{Int64,2}:
0  0  0  0  0  0  0  0  0  0  0  0  0  …  0  0  0  0  0  0  0  0  0  0  0  0

## TF (Term Frequency)

Often we need to find out the proportion of a document is contributed by each term. This can be done by finding the term frequency function

tf(dtm)

The parameter, dtm can be of the types - DocumentTermMatrix , SparseMatrixCSC or Matrix

julia> crps = Corpus([StringDocument("To be or not to be"),
StringDocument("To become or not to become")])

julia> update_lexicon!(crps)

julia> m = DocumentTermMatrix(crps)

julia> tf(m)
2×6 SparseArrays.SparseMatrixCSC{Float64,Int64} with 10 stored entries:
[1, 1]  =  0.166667
[2, 1]  =  0.166667
[1, 2]  =  0.333333
[2, 3]  =  0.333333
[1, 4]  =  0.166667
[2, 4]  =  0.166667
[1, 5]  =  0.166667
[2, 5]  =  0.166667
[1, 6]  =  0.166667
[2, 6]  =  0.166667

## TF-IDF (Term Frequency - Inverse Document Frequency)

tf_idf(dtm)

In many cases, raw word counts are not appropriate for use because:

• (A) Some documents are longer than other documents
• (B) Some words are more frequent than other words

You can work around this by performing TF-IDF on a DocumentTermMatrix:

julia> crps = Corpus([StringDocument("To be or not to be"),
StringDocument("To become or not to become")])

julia> update_lexicon!(crps)

julia> m = DocumentTermMatrix(crps)
DocumentTermMatrix(
[1, 1]  =  1
[2, 1]  =  1
[1, 2]  =  2
[2, 3]  =  2
[1, 4]  =  1
[2, 4]  =  1
[1, 5]  =  1
[2, 5]  =  1
[1, 6]  =  1
[2, 6]  =  1, ["To", "be", "become", "not", "or", "to"], Dict("or"=>5,"not"=>4,"to"=>6,"To"=>1,"be"=>2,"become"=>3))

julia> tf_idf(m)
2×6 SparseArrays.SparseMatrixCSC{Float64,Int64} with 10 stored entries:
[1, 1]  =  0.0
[2, 1]  =  0.0
[1, 2]  =  0.231049
[2, 3]  =  0.231049
[1, 4]  =  0.0
[2, 4]  =  0.0
[1, 5]  =  0.0
[2, 5]  =  0.0
[1, 6]  =  0.0
[2, 6]  =  0.0

As you can see, TF-IDF has the effect of inserting 0's into the columns of words that occur in all documents. This is a useful way to avoid having to remove those words during preprocessing.

## Okapi BM-25

From the document term matparamterix, Okapi BM25 document-word statistic can be created.

bm_25(dtm::AbstractMatrix; κ, β)
bm_25(dtm::DocumentTermMatrixm, κ, β)

It can also be used via the following methods Overwrite the bm25 with calculated weights.

bm_25!(dtm, bm25, κ, β)

The inputs matrices can also be a Sparse Matrix. The parameters κ and β default to 2 and 0.75 respectively.

Here is an example usage -

julia> crps = Corpus([StringDocument("a a a sample text text"), StringDocument("another example example text text"), StringDocument(""), StringDocument("another another text text text text")])

julia> update_lexicon!(crps)

julia> m = DocumentTermMatrix(crps)

julia> bm_25(m)
4×5 SparseArrays.SparseMatrixCSC{Float64,Int64} with 8 stored entries:
[1, 1]  =  1.29959
[2, 2]  =  0.882404
[4, 2]  =  1.40179
[2, 3]  =  1.54025
[1, 4]  =  1.89031
[1, 5]  =  0.405067
[2, 5]  =  0.405067
[4, 5]  =  0.676646

## Co occurrence matrix (COOM)

The elements of the Co occurrence matrix indicate how many times two words co-occur in a (sliding) word window of a given size. The COOM can be calculated for objects of type Corpus, AbstractDocument (with the exception of NGramDocument).

CooMatrix(crps; window, normalize)
CooMatrix(doc; window, normalize)

It takes following keyword arguments:

• window::Integer -length of the Window size, defaults to 5. The actual size of the sliding window is 2 * window + 1, with the keyword argument window specifying how many words to consider to the left and right of the center one
• normalize::Bool -normalizes counts to distance between words, defaults to true

It returns the CooMatrix structure from which the matrix can be extracted using coom(::CooMatrix). The terms can also be extracted from this. Here is an example usage -


julia> crps = Corpus([StringDocument("this is a string document"),

julia> C = CooMatrix(crps, window=1, normalize=false)
CooMatrix{Float64}(
[2, 1]  =  2.0
[6, 1]  =  2.0
[1, 2]  =  2.0
[3, 2]  =  2.0
[2, 3]  =  2.0
[6, 3]  =  2.0
[5, 4]  =  4.0
[4, 5]  =  4.0
[6, 5]  =  4.0
[1, 6]  =  2.0
[3, 6]  =  2.0
[5, 6]  =  4.0, ["string", "document", "token", "this", "is", "a"], OrderedDict("string"=>1,"document"=>2,"token"=>3,"this"=>4,"is"=>5,"a"=>6))

julia> coom(C)
6×6 SparseArrays.SparseMatrixCSC{Float64,Int64} with 12 stored entries:
[2, 1]  =  2.0
[6, 1]  =  2.0
[1, 2]  =  2.0
[3, 2]  =  2.0
[2, 3]  =  2.0
[6, 3]  =  2.0
[5, 4]  =  4.0
[4, 5]  =  4.0
[6, 5]  =  4.0
[1, 6]  =  2.0
[3, 6]  =  2.0
[5, 6]  =  4.0

julia> C.terms
6-element Array{String,1}:
"string"
"document"
"token"
"this"
"is"
"a"


It can also be called to calculate the terms for a specific list of words / terms in the document. In other cases it calculates the the co occurrence elements for all the terms.

CooMatrix(crps, terms; window, normalize)
CooMatrix(doc, terms; window, normalize)
julia> C = CooMatrix(crps, ["this", "is", "a"], window=1, normalize=false)
CooMatrix{Float64}(
[2, 1]  =  4.0
[1, 2]  =  4.0
[3, 2]  =  4.0
[2, 3]  =  4.0, ["this", "is", "a"], OrderedCollections.OrderedDict("this"=>1,"is"=>2,"a"=>3))


The type can also be specified for CooMatrix with the weights of type T. T defaults to Float64.

CooMatrix{T}(crps; window, normalize) where T <: AbstractFloat
CooMatrix{T}(doc; window, normalize) where T <: AbstractFloat
CooMatrix{T}(crps, terms; window, normalize) where T <: AbstractFloat
CooMatrix{T}(doc, terms; window, normalize) where T <: AbstractFloat

Remarks:

• The sliding window used to count co-occurrences does not take into consideration sentence stops however, it does with documents i.e. does not span across documents
• The co-occurrence matrices of the documents in a corpus are summed up when calculating the matrix for an entire corpus
Note

The Co occurrence matrix does not work for NGramDocument, or a Corpus containing an NGramDocument.

julia> C = CooMatrix(NGramDocument("A document"), window=1, normalize=false) # fails, documents are NGramDocument
ERROR: The tokens of an NGramDocument cannot be reconstructed

## Sentiment Analyzer

It can be used to find the sentiment score (between 0 and 1) of a word, sentence or a Document. A trained model (using Flux) on IMDB word corpus with weights saved are used to calculate the sentiments.

model = SentimentAnalyzer()
model(doc)
model(doc, handle_unknown)
• doc = Input Document for calculating document (AbstractDocument type)
• handle_unknown = A function for handling unknown words. Should return an array (default (x)->[])

## Summarizer

TextAnalysis offers a simple text-rank based summarizer for its various document types.

summarize(d, ns)

It takes 2 arguments:

• d : A document of type StringDocument, FileDocument or TokenDocument
• ns : (Optional) Mention the number of sentences in the Summary, defaults to 5 sentences.
julia> s = StringDocument("Assume this Short Document as an example. Assume this as an example summarizer. This has too foo sentences.")

julia> summarize(s, ns=2)
2-element Array{SubString{String},1}:
"Assume this Short Document as an example."
"This has too foo sentences."

## Tagging_schemes

There are many tagging schemes used for sequence labelling. TextAnalysis currently offers functions for conversion between these tagging format.

• BIO1
• BIO2
• BIOES
julia> tags = ["I-LOC", "O", "I-PER", "B-MISC", "I-MISC", "B-PER", "I-PER", "I-PER"]

julia> tag_scheme!(tags, "BIO1", "BIOES")

julia> tags
8-element Array{String,1}:
"S-LOC"
"O"
"S-PER"
"B-MISC"
"E-MISC"
"B-PER"
"I-PER"
"E-PER"

## Parts of Speech Tagging

This package provides with two different Part of Speech Tagger.

## Average Perceptron Part of Speech Tagger

This tagger can be used to find the POS tag of a word or token in a given sentence. It is a based on Average Perceptron Algorithm. The model can be trained from scratch and weights are saved in specified location. The pretrained model can also be loaded and can be used directly to predict tags.

### To train model:

julia> tagger = PerceptronTagger(false) #we can use tagger = PerceptronTagger()
julia> fit!(tagger, [[("today","NN"),("is","VBZ"),("good","JJ"),("day","NN")]])
iteration : 1
iteration : 2
iteration : 3
iteration : 4
iteration : 5

julia> tagger = PerceptronTagger(true)
PerceptronTagger(AveragePerceptron(Set(Any["JJS", "NNP_VBZ", "NN_NNS", "CC", "NNP_NNS", "EX", "NNP_TO", "VBD_DT", "LS", ("Council", "NNP")  …  "NNPS", "NNP_LS", "VB", "NNS_NN", "NNP_SYM", "VBZ", "VBZ_JJ", "UH", "SYM", "NNP_NN", "CD"]), Dict{Any,Any}("i+2 word wetlands"=>Dict{Any,Any}("NNS"=>0.0,"JJ"=>0.0,"NN"=>0.0),"i-1 tag+i word NNP basic"=>Dict{Any,Any}("JJ"=>0.0,"IN"=>0.0),"i-1 tag+i word DT chloride"=>Dict{Any,Any}("JJ"=>0.0,"NN"=>0.0),"i-1 tag+i word NN choo"=>Dict{Any,Any}("NNP"=>0.0,"NN"=>0.0),"i+1 word antarctica"=>Dict{Any,Any}("FW"=>0.0,"NN"=>0.0),"i-1 tag+i word -START- appendix"=>Dict{Any,Any}("NNP"=>0.0,"NNPS"=>0.0,"NN"=>0.0),"i-1 word wahoo"=>Dict{Any,Any}("JJ"=>0.0,"VBD"=>0.0),"i-1 tag+i word DT children's"=>Dict{Any,Any}("NNS"=>0.0,"NN"=>0.0),"i word dnipropetrovsk"=>Dict{Any,Any}("NNP"=>0.003,"NN"=>-0.003),"i suffix hla"=>Dict{Any,Any}("JJ"=>0.0,"NN"=>0.0)…), DefaultDict{Any,Any,Int64}(), DefaultDict{Any,Any,Int64}(), 1, ["-START-", "-START2-"]), Dict{Any,Any}("is"=>"VBZ","at"=>"IN","a"=>"DT","and"=>"CC","for"=>"IN","by"=>"IN","Retrieved"=>"VBN","was"=>"VBD","He"=>"PRP","in"=>"IN"…), Set(Any["JJS", "NNP_VBZ", "NN_NNS", "CC", "NNP_NNS", "EX", "NNP_TO", "VBD_DT", "LS", ("Council", "NNP")  …  "NNPS", "NNP_LS", "VB", "NNS_NN", "NNP_SYM", "VBZ", "VBZ_JJ", "UH", "SYM", "NNP_NN", "CD"]), ["-START-", "-START2-"], ["-END-", "-END2-"], Any[])

### To predict tags:

The perceptron tagger can predict tags over various document types-

predict(tagger, sentence::String)
predict(tagger, Tokens::Array{String, 1})
predict(tagger, sd::StringDocument)
predict(tagger, fd::FileDocument)
predict(tagger, td::TokenDocument)

This can also be done by - tagger(input)

julia> predict(tagger, ["today", "is"])
2-element Array{Any,1}:
("today", "NN")
("is", "VBZ")

julia> tagger(["today", "is"])
2-element Array{Any,1}:
("today", "NN")
("is", "VBZ")

PerceptronTagger(load::Bool)

• load = Boolean argument if true then pretrained model is loaded

fit!(self::PerceptronTagger, sentences::Vector{Vector{Tuple{String, String}}}, save_loc::String, nr_iter::Integer)

• self = PerceptronTagger object
• sentences = Vector of Vector of Tuple of pair of word or token and its POS tag [see above example]
• save_loc = location of file to save the trained weights
• nr_iter = Number of iterations to pass the sentences to train the model ( default 5)

predict(self::PerceptronTagger, tokens)

• self = PerceptronTagger
• tokens = Vector of words or tokens for which to predict tags

## Neural Model for Part of Speech tagging using LSTMs, CNN and CRF

The API provided is a pretrained model for tagging Part of Speech. The current model tags all the POS Tagging is done based on convention used in Penn Treebank, with 36 different Part of Speech tags excludes punctuation.

To use the API, we first load the model weights into an instance of tagger. The function also accepts the path of modelweights and modeldicts (for character and word embeddings)

PoSTagger()
PoSTagger(dicts_path, weights_path)
julia> pos = PoSTagger()

Note

When you call PoSTagger() for the first time, the package will request permission for download the Model_dicts and Model_weights. Upon downloading, these are store locally and managed by DataDeps. So, on subsequent uses the weights will not need to be downloaded again.

Once we create an instance, we can call it to tag a String (sentence), sequence of tokens, AbstractDocument or Corpus.

(pos::PoSTagger)(sentence::String)
(pos::PoSTagger)(tokens::Array{String, 1})
(pos::PoSTagger)(sd::StringDocument)
(pos::PoSTagger)(fd::FileDocument)
(pos::PoSTagger)(td::TokenDocument)
(pos::PoSTagger)(crps::Corpus)

julia> sentence = "This package is maintained by John Doe."
"This package is maintained by John Doe."

julia> tags = pos(sentence)
8-element Array{String,1}:
"DT"
"NN"
"VBZ"
"VBN"
"IN"
"NNP"
"NNP"
"."


The API tokenizes the input sentences via the default tokenizer provided by WordTokenizers, this currently being set to the multilingual TokTok Tokenizer.


julia> using WordTokenizers

julia> collect(zip(WordTokenizers.tokenize(sentence), tags))
8-element Array{Tuple{String,String},1}:
("This", "DT")
("package", "NN")
("is", "VBZ")
("maintained", "VBN")
("by", "IN")
("John", "NNP")
("Doe", "NNP")
(".", ".")


For tagging a multisentence text or document, once can use split_sentences from WordTokenizers.jl package and run the pos model on each.

julia> sentences = "Rabinov is winding up his term as ambassador. He will be replaced by Eliahu Ben-Elissar, a former Israeli envoy to Egypt and right-wing Likud party politiian." # Sentence taken from CoNLL 2003 Dataset

julia> splitted_sents = WordTokenizers.split_sentences(sentences)

julia> tag_sequences = pos.(splitted_sents)
2-element Array{Array{String,1},1}:
["NNP", "VBZ", "VBG", "RP", "PRP\$", "NN", "IN", "NN", "."] ["PRP", "MD", "VB", "VBN", "IN", "NNP", "NNP", ",", "DT", "JJ", "JJ", "NN", "TO", "NNP", "CC", "JJ", "NNP", "NNP", "NNP", "."] julia> zipped = [collect(zip(tag_sequences[i], WordTokenizers.tokenize(splitted_sents[i]))) for i in eachindex(splitted_sents)] julia> zipped[1] 9-element Array{Tuple{String,String},1}: ("NNP", "Rabinov") ("VBZ", "is") ("VBG", "winding") ("RP", "up") ("PRP\$", "his")
("NN", "term")
("IN", "as")
(".", ".")

julia> zipped[2]
20-element Array{Tuple{String,String},1}:
("PRP", "He")
("MD", "will")
("VB", "be")
("VBN", "replaced")
("IN", "by")
("NNP", "Eliahu")
("NNP", "Ben-Elissar")
(",", ",")
("DT", "a")
("JJ", "former")
("JJ", "Israeli")
("NN", "envoy")
("TO", "to")
("NNP", "Egypt")
("CC", "and")
("JJ", "right-wing")
("NNP", "Likud")
("NNP", "party")
("NNP", "politiian")
(".", ".")


Since the tagging the Part of Speech is done on sentence level, the text of AbstractDocument is sentence_tokenized and then labelled for over sentence. However is not possible for NGramDocument as text cannot be recreated. For TokenDocument, text is approximated for splitting into sentences, hence the following throws a warning when tagging the Corpus.


julia> crps = Corpus([StringDocument("We aRE vErY ClOSE tO ThE HEaDQuarTeRS."), TokenDocument("this is Bangalore.")])
A Corpus with 2 documents:
* 1 StringDocument's
* 0 FileDocument's
* 1 TokenDocument's
* 0 NGramDocument's

Corpus's lexicon contains 0 tokens
Corpus's index contains 0 tokens

julia> pos(crps)
┌ Warning: TokenDocument's can only approximate the original text
└ @ TextAnalysis ~/.julia/dev/TextAnalysis/src/document.jl:220
2-element Array{Array{Array{String,1},1},1}:
[["PRP", "VBP", "RB", "JJ", "TO", "DT", "NN", "."]]
[["DT", "VBZ", "NNP", "."]]