technical_drawings_extraction/venv/lib/python3.7/site-packages/nltk/sem/relextract.py

# Natural Language Toolkit: Relation Extraction
#
# Copyright (C) 2001-2019 NLTK Project
# Author: Ewan Klein <ewan@inf.ed.ac.uk>
# URL: <http://nltk.org/>
# For license information, see LICENSE.TXT

"""
Code for extracting relational triples from the ieer and conll2002 corpora.

Relations are stored internally as dictionaries ('reldicts').

The two serialization outputs are "rtuple" and "clause".

- An rtuple is a tuple of the form ``(subj, filler, obj)``,
  where ``subj`` and ``obj`` are pairs of Named Entity mentions, and ``filler`` is the string of words
  occurring between ``sub`` and ``obj`` (with no intervening NEs). Strings are printed via ``repr()`` to
  circumvent locale variations in rendering utf-8 encoded strings.
- A clause is an atom of the form ``relsym(subjsym, objsym)``,
  where the relation, subject and object have been canonicalized to single strings.
"""
from __future__ import print_function

# todo: get a more general solution to canonicalized symbols for clauses -- maybe use xmlcharrefs?

from collections import defaultdict
import re

from six.moves import html_entities

# Dictionary that associates corpora with NE classes
NE_CLASSES = {
    'ieer': [
        'LOCATION',
        'ORGANIZATION',
        'PERSON',
        'DURATION',
        'DATE',
        'CARDINAL',
        'PERCENT',
        'MONEY',
        'MEASURE',
    ],
    'conll2002': ['LOC', 'PER', 'ORG'],
    'ace': [
        'LOCATION',
        'ORGANIZATION',
        'PERSON',
        'DURATION',
        'DATE',
        'CARDINAL',
        'PERCENT',
        'MONEY',
        'MEASURE',
        'FACILITY',
        'GPE',
    ],
}

# Allow abbreviated class labels
short2long = dict(LOC='LOCATION', ORG='ORGANIZATION', PER='PERSON')
long2short = dict(LOCATION='LOC', ORGANIZATION='ORG', PERSON='PER')


def _expand(type):
    """
    Expand an NE class name.
    :type type: str
    :rtype: str
    """
    try:
        return short2long[type]
    except KeyError:
        return type


def class_abbrev(type):
    """
    Abbreviate an NE class name.
    :type type: str
    :rtype: str
    """
    try:
        return long2short[type]
    except KeyError:
        return type


def _join(lst, sep=' ', untag=False):
    """
    Join a list into a string, turning tags tuples into tag strings or just words.
    :param untag: if ``True``, omit the tag from tagged input strings.
    :type lst: list
    :rtype: str
    """
    try:
        return sep.join(lst)
    except TypeError:
        if untag:
            return sep.join(tup[0] for tup in lst)
        from nltk.tag import tuple2str

        return sep.join(tuple2str(tup) for tup in lst)


def descape_entity(m, defs=html_entities.entitydefs):
    """
    Translate one entity to its ISO Latin value.
    Inspired by example from effbot.org


    """
    # s = 'mcglashan_&amp;_sarrail'
    # l = ['mcglashan', '&amp;', 'sarrail']
    # pattern = re.compile("&(\w+?);")
    # new = list2sym(l)
    # s = pattern.sub(descape_entity, s)
    # print s, new
    try:
        return defs[m.group(1)]

    except KeyError:
        return m.group(0)  # use as is


def list2sym(lst):
    """
    Convert a list of strings into a canonical symbol.
    :type lst: list
    :return: a Unicode string without whitespace
    :rtype: unicode
    """
    sym = _join(lst, '_', untag=True)
    sym = sym.lower()
    ENT = re.compile("&(\w+?);")
    sym = ENT.sub(descape_entity, sym)
    sym = sym.replace('.', '')
    return sym


def tree2semi_rel(tree):
    """
    Group a chunk structure into a list of 'semi-relations' of the form (list(str), ``Tree``).

    In order to facilitate the construction of (``Tree``, string, ``Tree``) triples, this
    identifies pairs whose first member is a list (possibly empty) of terminal
    strings, and whose second member is a ``Tree`` of the form (NE_label, terminals).

    :param tree: a chunk tree
    :return: a list of pairs (list(str), ``Tree``)
    :rtype: list of tuple
    """

    from nltk.tree import Tree

    semi_rels = []
    semi_rel = [[], None]

    for dtr in tree:
        if not isinstance(dtr, Tree):
            semi_rel[0].append(dtr)
        else:
            # dtr is a Tree
            semi_rel[1] = dtr
            semi_rels.append(semi_rel)
            semi_rel = [[], None]
    return semi_rels


def semi_rel2reldict(pairs, window=5, trace=False):
    """
    Converts the pairs generated by ``tree2semi_rel`` into a 'reldict': a dictionary which
    stores information about the subject and object NEs plus the filler between them.
    Additionally, a left and right context of length =< window are captured (within
    a given input sentence).

    :param pairs: a pair of list(str) and ``Tree``, as generated by
    :param window: a threshold for the number of items to include in the left and right context
    :type window: int
    :return: 'relation' dictionaries whose keys are 'lcon', 'subjclass', 'subjtext', 'subjsym', 'filler', objclass', objtext', 'objsym' and 'rcon'
    :rtype: list(defaultdict)
    """
    result = []
    while len(pairs) > 2:
        reldict = defaultdict(str)
        reldict['lcon'] = _join(pairs[0][0][-window:])
        reldict['subjclass'] = pairs[0][1].label()
        reldict['subjtext'] = _join(pairs[0][1].leaves())
        reldict['subjsym'] = list2sym(pairs[0][1].leaves())
        reldict['filler'] = _join(pairs[1][0])
        reldict['untagged_filler'] = _join(pairs[1][0], untag=True)
        reldict['objclass'] = pairs[1][1].label()
        reldict['objtext'] = _join(pairs[1][1].leaves())
        reldict['objsym'] = list2sym(pairs[1][1].leaves())
        reldict['rcon'] = _join(pairs[2][0][:window])
        if trace:
            print(
                "(%s(%s, %s)"
                % (
                    reldict['untagged_filler'],
                    reldict['subjclass'],
                    reldict['objclass'],
                )
            )
        result.append(reldict)
        pairs = pairs[1:]
    return result


def extract_rels(subjclass, objclass, doc, corpus='ace', pattern=None, window=10):
    """
    Filter the output of ``semi_rel2reldict`` according to specified NE classes and a filler pattern.

    The parameters ``subjclass`` and ``objclass`` can be used to restrict the
    Named Entities to particular types (any of 'LOCATION', 'ORGANIZATION',
    'PERSON', 'DURATION', 'DATE', 'CARDINAL', 'PERCENT', 'MONEY', 'MEASURE').

    :param subjclass: the class of the subject Named Entity.
    :type subjclass: str
    :param objclass: the class of the object Named Entity.
    :type objclass: str
    :param doc: input document
    :type doc: ieer document or a list of chunk trees
    :param corpus: name of the corpus to take as input; possible values are
        'ieer' and 'conll2002'
    :type corpus: str
    :param pattern: a regular expression for filtering the fillers of
        retrieved triples.
    :type pattern: SRE_Pattern
    :param window: filters out fillers which exceed this threshold
    :type window: int
    :return: see ``mk_reldicts``
    :rtype: list(defaultdict)
    """

    if subjclass and subjclass not in NE_CLASSES[corpus]:
        if _expand(subjclass) in NE_CLASSES[corpus]:
            subjclass = _expand(subjclass)
        else:
            raise ValueError(
                "your value for the subject type has not been recognized: %s"
                % subjclass
            )
    if objclass and objclass not in NE_CLASSES[corpus]:
        if _expand(objclass) in NE_CLASSES[corpus]:
            objclass = _expand(objclass)
        else:
            raise ValueError(
                "your value for the object type has not been recognized: %s" % objclass
            )

    if corpus == 'ace' or corpus == 'conll2002':
        pairs = tree2semi_rel(doc)
    elif corpus == 'ieer':
        pairs = tree2semi_rel(doc.text) + tree2semi_rel(doc.headline)
    else:
        raise ValueError("corpus type not recognized")

    reldicts = semi_rel2reldict(pairs)

    relfilter = lambda x: (
        x['subjclass'] == subjclass
        and len(x['filler'].split()) <= window
        and pattern.match(x['filler'])
        and x['objclass'] == objclass
    )

    return list(filter(relfilter, reldicts))


def rtuple(reldict, lcon=False, rcon=False):
    """
    Pretty print the reldict as an rtuple.
    :param reldict: a relation dictionary
    :type reldict: defaultdict
    """
    items = [
        class_abbrev(reldict['subjclass']),
        reldict['subjtext'],
        reldict['filler'],
        class_abbrev(reldict['objclass']),
        reldict['objtext'],
    ]
    format = '[%s: %r] %r [%s: %r]'
    if lcon:
        items = [reldict['lcon']] + items
        format = '...%r)' + format
    if rcon:
        items.append(reldict['rcon'])
        format = format + '(%r...'
    printargs = tuple(items)
    return format % printargs


def clause(reldict, relsym):
    """
    Print the relation in clausal form.
    :param reldict: a relation dictionary
    :type reldict: defaultdict
    :param relsym: a label for the relation
    :type relsym: str
    """
    items = (relsym, reldict['subjsym'], reldict['objsym'])
    return "%s(%r, %r)" % items


#######################################################
# Demos of relation extraction with regular expressions
#######################################################

############################################
# Example of in(ORG, LOC)
############################################
def in_demo(trace=0, sql=True):
    """
    Select pairs of organizations and locations whose mentions occur with an
    intervening occurrence of the preposition "in".

    If the sql parameter is set to True, then the entity pairs are loaded into
    an in-memory database, and subsequently pulled out using an SQL "SELECT"
    query.
    """
    from nltk.corpus import ieer

    if sql:
        try:
            import sqlite3

            connection = sqlite3.connect(":memory:")
            connection.text_factory = sqlite3.OptimizedUnicode
            cur = connection.cursor()
            cur.execute(
                """create table Locations
            (OrgName text, LocationName text, DocID text)"""
            )
        except ImportError:
            import warnings

            warnings.warn("Cannot import sqlite; sql flag will be ignored.")

    IN = re.compile(r'.*\bin\b(?!\b.+ing)')

    print()
    print("IEER: in(ORG, LOC) -- just the clauses:")
    print("=" * 45)

    for file in ieer.fileids():
        for doc in ieer.parsed_docs(file):
            if trace:
                print(doc.docno)
                print("=" * 15)
            for rel in extract_rels('ORG', 'LOC', doc, corpus='ieer', pattern=IN):
                print(clause(rel, relsym='IN'))
                if sql:
                    try:
                        rtuple = (rel['subjtext'], rel['objtext'], doc.docno)
                        cur.execute(
                            """insert into Locations
                                    values (?, ?, ?)""",
                            rtuple,
                        )
                        connection.commit()
                    except NameError:
                        pass

    if sql:
        try:
            cur.execute(
                """select OrgName from Locations
                        where LocationName = 'Atlanta'"""
            )
            print()
            print("Extract data from SQL table: ORGs in Atlanta")
            print("-" * 15)
            for row in cur:
                print(row)
        except NameError:
            pass


############################################
# Example of has_role(PER, LOC)
############################################


def roles_demo(trace=0):
    from nltk.corpus import ieer

    roles = """
    (.*(                   # assorted roles
    analyst|
    chair(wo)?man|
    commissioner|
    counsel|
    director|
    economist|
    editor|
    executive|
    foreman|
    governor|
    head|
    lawyer|
    leader|
    librarian).*)|
    manager|
    partner|
    president|
    producer|
    professor|
    researcher|
    spokes(wo)?man|
    writer|
    ,\sof\sthe?\s*  # "X, of (the) Y"
    """
    ROLES = re.compile(roles, re.VERBOSE)

    print()
    print("IEER: has_role(PER, ORG) -- raw rtuples:")
    print("=" * 45)

    for file in ieer.fileids():
        for doc in ieer.parsed_docs(file):
            lcon = rcon = False
            if trace:
                print(doc.docno)
                print("=" * 15)
                lcon = rcon = True
            for rel in extract_rels('PER', 'ORG', doc, corpus='ieer', pattern=ROLES):
                print(rtuple(rel, lcon=lcon, rcon=rcon))


##############################################
### Show what's in the IEER Headlines
##############################################


def ieer_headlines():

    from nltk.corpus import ieer
    from nltk.tree import Tree

    print("IEER: First 20 Headlines")
    print("=" * 45)

    trees = [
        (doc.docno, doc.headline)
        for file in ieer.fileids()
        for doc in ieer.parsed_docs(file)
    ]
    for tree in trees[:20]:
        print()
        print("%s:\n%s" % tree)


#############################################
## Dutch CONLL2002: take_on_role(PER, ORG
#############################################


def conllned(trace=1):
    """
    Find the copula+'van' relation ('of') in the Dutch tagged training corpus
    from CoNLL 2002.
    """

    from nltk.corpus import conll2002

    vnv = """
    (
    is/V|    # 3rd sing present and
    was/V|   # past forms of the verb zijn ('be')
    werd/V|  # and also present
    wordt/V  # past of worden ('become)
    )
    .*       # followed by anything
    van/Prep # followed by van ('of')
    """
    VAN = re.compile(vnv, re.VERBOSE)

    print()
    print("Dutch CoNLL2002: van(PER, ORG) -- raw rtuples with context:")
    print("=" * 45)

    for doc in conll2002.chunked_sents('ned.train'):
        lcon = rcon = False
        if trace:
            lcon = rcon = True
        for rel in extract_rels(
            'PER', 'ORG', doc, corpus='conll2002', pattern=VAN, window=10
        ):
            print(rtuple(rel, lcon=lcon, rcon=rcon))


#############################################
## Spanish CONLL2002: (PER, ORG)
#############################################


def conllesp():
    from nltk.corpus import conll2002

    de = """
    .*
    (
    de/SP|
    del/SP
    )
    """
    DE = re.compile(de, re.VERBOSE)

    print()
    print("Spanish CoNLL2002: de(ORG, LOC) -- just the first 10 clauses:")
    print("=" * 45)
    rels = [
        rel
        for doc in conll2002.chunked_sents('esp.train')
        for rel in extract_rels('ORG', 'LOC', doc, corpus='conll2002', pattern=DE)
    ]
    for r in rels[:10]:
        print(clause(r, relsym='DE'))
    print()


def ne_chunked():
    print()
    print("1500 Sentences from Penn Treebank, as processed by NLTK NE Chunker")
    print("=" * 45)
    ROLE = re.compile(
        r'.*(chairman|president|trader|scientist|economist|analyst|partner).*'
    )
    rels = []
    for i, sent in enumerate(nltk.corpus.treebank.tagged_sents()[:1500]):
        sent = nltk.ne_chunk(sent)
        rels = extract_rels('PER', 'ORG', sent, corpus='ace', pattern=ROLE, window=7)
        for rel in rels:
            print('{0:<5}{1}'.format(i, rtuple(rel)))


if __name__ == '__main__':
    import nltk
    from nltk.sem import relextract

    in_demo(trace=0)
    roles_demo(trace=0)
    conllned()
    conllesp()
    ieer_headlines()
    ne_chunked()