pilsner 0.1.0

Description:

pilsner 0.1.0

pilsner
Python implemented library servicing named entity recognition

1. Purpose
This library is Python implementation of toolkit for dictionary based named
entity recognition. It is intended to store any thesaurus in a trie-like
structure and identify any of stored synonyms in a string.
2. Installation and dependencies
pip install pilsner

pilsner is tested in Python 3.6, 3.7, and 3.8.
The only dependency is sic package. While it can be automatically installed
at the time of pilsner installation, manual installation of sic beforehand
might also be considered (see benchmark of cythonized vs pure Python
implementation in sic docimentation,
https://pypi.org/project/sic/).
3. Diagram
pilsner consists of two major components: Model and Utility. Model
class provides storage for the dictionary and string normalization rules, as
well as low-level methods for populating this storage. Utility class provides
high-level methods for storing and retrieving data to/from Model instance.

4. Usage
import pilsner

4.1. Initialize model

To initialize empty model:

m = pilsner.Model()


To specify path to temporary database for empty model:

m = pilsner.Model(storage_location='path/to/database.file')


To create empty model that uses database created in memory rather than on
disk:

m = pilsner.Model(storage_location=':memory:')


To create empty model that does not store any attributes in a database at all:

m = pilsner.Model(simple=True)


If database is created in memory, the model cannot be later saved on disk
(can only be used instantly).


To load model from disk:

m = pilsner.Model(filename='path/to/model')


More on how model is saved to and loaded from disk - see
4.6. Save model and 4.7. Load model.

4.2. Add string normalization units

Depending on the dictionary and nature of the text supposed to be parsed,
string normalization might not be required at all, and nothing specific is to
be done here in such case.
Without string normalization, synonyms from the dictionary will be stored as
they are and looked up by recognizer case-sensitively.
To add a single normalization unit:

# Assuming m is pilsner.Model instance:
m.add_normalizer(
normalizer_name='normalizer_tag',
filename='path/to/normalizer_config.xml'
)


String normalization is technically done by sic component. See
documentation for sic at
https://pypi.org/project/sic/ to learn how
to design normalizer config.


Model can embed more than one normalization unit.
Default normalization unit for the model is the one added first or the last
one added with parameter default set to True.
Having multiple normalization units in one model makes perfect sense when the
stored dictionary contains synonyms of different nature that should be
normalized in different ways (for example, abbreviations probably should not
get normalized at all, while other synonyms might include tokens or punctuation
marks that should not affect entity recognition). For that purpose, Model class
includes normalizer_map dict that is supposed to map names of added
normalization units to values in specific field in a dictionary designating the
way a synonym should be normalized (tokenizer field, or tokenizer column):

# Assuming m is pilsner.Model instance:
m.normalizer_map = {
'synonym_type_1': 'normalizer_1',
'synonym_type_2': 'normalizer_2'
}


The snippet above instructs pilsner to normalize synonyms that have
synonym_type_1 value in tokenizer column with normalizer_1
normalization unit, and normalize synonyms that have synonym_type_2 value
in tokenizer column with normalizer_2 normalization unit. For more about
fields in a dictionary, see 4.4. Define dictionary.

4.3. Initialize utility

To load dictionary into Model instance, as well as to parse text, the
Utility instance is required:

r = pilsner.Utility()

4.4. Define dictionary

Source dictionary for pilsner must be delimited text file.
Along with the source dictionary, specifications of the columns (fields) must
be provided as list where each item corresponds to a column (from left to
right). Each item in this list must be a dict object with string keys name,
include, delimiter, id_flag, normalizer_flag, and value_flag, so
that:

field['name'] is a string for column title;
field['include'] is a boolean that must be set to True for the column
to be included in the model, otherwise False;
field['delimiter'] is a string that is supposed to split single cell into
list of values if the column holds concatenated lists rather than individual
values;
field['id_flag] is a boolean that must be set to True if the column is
supposed to be used for grouping synonyms (generally, entity ID is such
column), otherwise False;
field['normalizer_flag'] is a boolean that must be set to True if the
column holds indication on what normalization unit must be applied to this
particular synonym, otherwise False;
field['value_flag'] is a boolean that must be set to True if the column
holds synonyms that are supposed to be looked up when parsing a text,
otherwise False.




If dictionary has a column flagged with normalizer_flag, synonym in each
row will be normalized with string normalization unit which name is mapped on
value in this column using pilsner.Model.normalizer_map dict. If value is
not among pilsner.Model.normalizer_map keys, default normalization unit
will be used.

4.5. Compile model

To store dictionary in Model instance, method compile_model of Utility
instance must be called with the following required parameters:

model: pointer to initilized Model instance;
filename: string with path and filename of source dictionary;
fields: dict object with definitions of columns (see
4.4. Define dictionary);
word_separator: string defining what is to be considered word separator
(generally, it should be whitespace);
column_separator: string defining what is to be considered column
separator (e.g. \t for tab-delimited file);
column_enclosure: string defining what is to be stripped away from cell
after row has been split into columns (typically, it should be \n for new
line character to be trimmed from the rightmost column).



# Assuming m is pilsner.Model instance and r is pilsner.Utility instance:
r.compile_model(
model=m,
filename='path/to/dictionary_in_a_text_file.txt',
fields=fields,
word_separator=' ',
column_separator='\t',
column_enclosure='\n'
)


To review optional parameters, see comments in the code.

4.6. Save model

If Model instance has compiled dictionary, and if database location for the
Model instance is not explicitly set to ':memory:', the data such instance
is holding can be saved to disk:

# Assuming m is pilsner.Model instance
m.save('path/to/model_name')


The snippet above will write the following files:

path/to/model_name.attributes: database with attributes (fields from the
dictionary that are not synonyms) - will only be written if Model instance
is not created with simple=True parameter;
path/to/model_name.keywords: keywords used for disambiguation;
path/to/model_name.normalizers: string normalization units;
path/to/model_name.0.dictionary: trie with synonyms;
path/to/model_name.<N>.dictionary: additional tries with synonyms (<N>
being integer number of a trie) in case more than one trie was created (see
comments in the code - pilsner.Utility.compile_model method, item_limit
parameter).



4.7. Load model

To initialize new Model instance using previously saved data:

m = pilsner.Model(filename='path/to/model_name')


Alternatively, data can be loaded to previously initialized Model instance:

m = pilsner.Model()
m.load('path/to/model_name')


In both cases, the program will look for the following files:

path/to/model_name.attributes: database with attributes (fields from the
dictionary that are not synonyms) - if not found, Model instance will work
as if it is initialized with simple=True parameter, meaning no attributes
other than primary IDs could be processed;
path/to/model_name.keywords: keywords used for disambiguation;
path/to/model_name.normalizers: string normalization units;
path/to/model_name.<N>.dictionary: tries with synonyms (<N> being
integer).



4.8. Parse string

To parse a string without filtering out any synonyms and output all
attributes of spotted entities:

# Assuming m is pilsner.Model instance, r is pilsner.Utility instance,
# and text_to_parse is string to parse
parsed = r.parse(
model=m,
source_string=text_to_parse
)


The output will be dict object where keys are tuples for location of spotted
entity in a string (begin, end) and values are dicts for attributes that are
associated with identified entity ({'attribute_name': {attribute_values}}).
To ignore entity by its label rather than some of its attributes, compiled
model can be adjusted using pilsnet.Utility.ignore_node() method:

# Assuming m is pilsner.Model instance, r is pilsner.Utility instance
r.ignore_node(
model=m,
label='irrelevant substring'
)
# substring 'irrelevant substring' will not be found by pilsner.Utility.parse()
# even if it is present in the model


For details about optional parameters, see comments in the code -
pilsner.Utility.parse() function.

5. Example
Everything written above is put together in example code,
see /misc/example/ directory in the project's repository.