Tokenization for language processing

This package contains some generic configurable tools allowing to cut a string in sub-parts (cf. Wikipedia), called ExtractionString. An ExtractionString is a sub-string from a parent string (say the initial complete text), with associated intervals of non-overlaping characters. The number of associated intervals is arbitrary.

ExtractionString class allow basic tokenization of text, such as word splitting, n-gram splitting, char-gram splitting of arbitrary size. In addition, it allows to associate several non-overlapping sub-strings into a given ExtractionString. One can compare two different ExtractionString objects in terms of their intervals. One can also apply basic mathematical operations and logic to them (+, -, *, /) corresponding to the union, difference, intersection and symmetric difference implemented by Python set ; here the sets are the intervals of position from the parent string. Finally, there are some ordering possibilities among the different ExtractionString constructed from the same parent string.

Depositories, and online documentation

The different sources of informations for this packages are :

• the official Python Package Installation (PyPI) repository is on https://pypi.org/project/extractionstring
• the official git repository is on https://framagit.org/nlp/extractionstring
• the official documentation is on https://nlp.frama.io/extractionstring/

Philosophy of this library

In extractionstring, one thinks of a string as a collection of integers: the position of each character in the string. For instance

'Simple string for demonstration and for illustration.' # the parent string
'01234567891123456789212345678931234567894123456789512' # the positions

'       string                       for illustration ' # the ExtractionString es
'       789112                       678 412345678951 ' # the associated positions

'Simple                                               ' # the ExtractionString es2
'012345                                               ' # the associated positions

To define the ExtractionString 'string for illustration' consists in selecting the positions [7,13, 36,39, 40,52] from the parent string, and the ExtractionString 'simple' is defined by the positions [0,6,].

In addition, one can see the above ranges as sets of positins. Then it is quite easy to perform some basic operations on the Span, for instance the addition of two ExtractionString

str(es1 + es2) = 'Simple string for illustration'

is interpreted as the union of their relative sets of positions.

In addition to these logical operations, there are a few utilities, like the possibility to split or slice a ExtractionString object, as long as their are all related to the same parent string.

Basic example

Below we give a simple example of usage of the ExtractionString class.

import re
from extractionstring import ExtractionString

string = 'Simple string for demonstration and for illustration.'
initial_span = ExtractionString(string)

# char-gram generation
chargrams = initial_span.slice(0, len(initial_span), 3)
str(chargrams[2])
# return 'mpl'

# each char-gram conserves a memory of the initial string
chargrams[2].string
# return 'Simple string for demonstration and for illustration.'

cuts = []
for r in re.finditer(r'\W+', string):
    cuts += [r.start(), r.end()]
spans = initial_span.split(cuts)
# this returns a list of ExtractionString objects
# representing the tokens as if string.split() was applied


# an other possibility to keep only the words is to construct it explicitly
cuts = []
for r in re.finditer(r'\w+', string):
    cuts += [r.start(), r.end()]
spans = ExtractionString(string, intervals=cuts).extractions
# extractions attribute contains the list of sub-tokens

# 2-gram construction
ngram = [ExtractionString(string, intervals=cuts[2*i:2*i+4]) 
         for i in range(len(cuts)//2-1)]
ngram[2]
# return ExtractionString('for demonstration', [(14,17),(18,31)])
str(ngram[2])
# return 'for demonstration'
ngram[2].intervals
# return EvenSizedSortedSet[(14,17);(18,31)]
ngram[2].extractions
# return [ExtractionString('for', [(14,17)]), ExtractionString('demonstration', [(18,31)])]

# are the two 'for' Token the same ?
spans[2] == spans[-2]
# return False, because they are not at the same position

# basic operations among Token
for_for = spans[2] + spans[-2]
str(for_for)
# return 'for for'
for_for.intervals
# return EvenSizedSortedSet[(14,17);(36,39)]
for_for.string
# return 'Simple string for demonstration and for illustration.'
# to check the positions of the two 'for' ExtractionString : 
#        '01234567890...456...01234567890.....67890............'

# also available : 
# span1 + span2 : union of the sets of span1.intervals and span2.intervals
# span1 - span2 : difference of span1.intervals and span2.intervals
# span1 * span2 : intersection of span1.intervals and span2.intervals
# span1 / span2 : symmetric difference of span1.intervals and span2.intervals

Other examples can be found in the documentation.

Comparison with other Python libraries

A comparison with some other NLP librairies (nltk, gensim, spaCy, gateNLP, ...) can be found in the documentation

Installation

Simply run

pip install extractionstring

should install the library from Python Package Index (PIP). The official repository is on https://framagit.org/nlp/extractionstring. To install the package from the repository, run the following command lines

git clone https://framagit.org/nlp/extractionstring.git
cd extractionstring/
pip install .

Once installed, one can run some tests using

cd tests/
python3 -m unittest -v

(verbosity -v is an option).

Versions

See CHANGES file in this folder.

About us

Package developped for Natural Language Processing at IAM : Unité d'Informatique et d'Archivistique Médicale, Service d'Informatique Médicale, Pôle de Santé Publique, Centre Hospitalo-Universitaire (CHU) de Bordeaux, France.

You are kindly encouraged to contact the authors by issue on the official repository, and to propose ameliorations and/or suggestions to the authors, via issue or merge requests.

Last version : Jan 3, 2023