235 lines
8.4 KiB
Plaintext
235 lines
8.4 KiB
Plaintext
.. Copyright (C) 2001-2019 NLTK Project
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.. For license information, see LICENSE.TXT
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=======
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Chat-80
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=======
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Chat-80 was a natural language system which allowed the user to
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interrogate a Prolog knowledge base in the domain of world
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geography. It was developed in the early '80s by Warren and Pereira; see
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`<http://acl.ldc.upenn.edu/J/J82/J82-3002.pdf>`_ for a description and
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`<http://www.cis.upenn.edu/~pereira/oldies.html>`_ for the source
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files.
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The ``chat80`` module contains functions to extract data from the Chat-80
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relation files ('the world database'), and convert then into a format
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that can be incorporated in the FOL models of
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``nltk.sem.evaluate``. The code assumes that the Prolog
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input files are available in the NLTK corpora directory.
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The Chat-80 World Database consists of the following files::
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world0.pl
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rivers.pl
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cities.pl
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countries.pl
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contain.pl
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borders.pl
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This module uses a slightly modified version of ``world0.pl``, in which
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a set of Prolog rules have been omitted. The modified file is named
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``world1.pl``. Currently, the file ``rivers.pl`` is not read in, since
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it uses a list rather than a string in the second field.
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Reading Chat-80 Files
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=====================
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Chat-80 relations are like tables in a relational database. The
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relation acts as the name of the table; the first argument acts as the
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'primary key'; and subsequent arguments are further fields in the
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table. In general, the name of the table provides a label for a unary
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predicate whose extension is all the primary keys. For example,
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relations in ``cities.pl`` are of the following form::
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'city(athens,greece,1368).'
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Here, ``'athens'`` is the key, and will be mapped to a member of the
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unary predicate *city*.
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By analogy with NLTK corpora, ``chat80`` defines a number of 'items'
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which correspond to the relations.
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>>> from nltk.sem import chat80
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>>> print(chat80.items) # doctest: +ELLIPSIS
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('borders', 'circle_of_lat', 'circle_of_long', 'city', ...)
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The fields in the table are mapped to binary predicates. The first
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argument of the predicate is the primary key, while the second
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argument is the data in the relevant field. Thus, in the above
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example, the third field is mapped to the binary predicate
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*population_of*, whose extension is a set of pairs such as
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``'(athens, 1368)'``.
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An exception to this general framework is required by the relations in
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the files ``borders.pl`` and ``contains.pl``. These contain facts of the
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following form::
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'borders(albania,greece).'
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'contains0(africa,central_africa).'
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We do not want to form a unary concept out the element in
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the first field of these records, and we want the label of the binary
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relation just to be ``'border'``/``'contain'`` respectively.
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In order to drive the extraction process, we use 'relation metadata bundles'
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which are Python dictionaries such as the following::
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city = {'label': 'city',
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'closures': [],
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'schema': ['city', 'country', 'population'],
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'filename': 'cities.pl'}
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According to this, the file ``city['filename']`` contains a list of
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relational tuples (or more accurately, the corresponding strings in
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Prolog form) whose predicate symbol is ``city['label']`` and whose
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relational schema is ``city['schema']``. The notion of a ``closure`` is
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discussed in the next section.
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Concepts
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========
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In order to encapsulate the results of the extraction, a class of
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``Concept``\ s is introduced. A ``Concept`` object has a number of
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attributes, in particular a ``prefLabel``, an arity and ``extension``.
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>>> c1 = chat80.Concept('dog', arity=1, extension=set(['d1', 'd2']))
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>>> print(c1)
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Label = 'dog'
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Arity = 1
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Extension = ['d1', 'd2']
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The ``extension`` attribute makes it easier to inspect the output of
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the extraction.
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>>> schema = ['city', 'country', 'population']
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>>> concepts = chat80.clause2concepts('cities.pl', 'city', schema)
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>>> concepts
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[Concept('city'), Concept('country_of'), Concept('population_of')]
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>>> for c in concepts: # doctest: +NORMALIZE_WHITESPACE
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... print("%s:\n\t%s" % (c.prefLabel, c.extension[:4]))
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city:
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['athens', 'bangkok', 'barcelona', 'berlin']
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country_of:
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[('athens', 'greece'), ('bangkok', 'thailand'), ('barcelona', 'spain'), ('berlin', 'east_germany')]
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population_of:
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[('athens', '1368'), ('bangkok', '1178'), ('barcelona', '1280'), ('berlin', '3481')]
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In addition, the ``extension`` can be further
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processed: in the case of the ``'border'`` relation, we check that the
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relation is **symmetric**, and in the case of the ``'contain'``
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relation, we carry out the **transitive closure**. The closure
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properties associated with a concept is indicated in the relation
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metadata, as indicated earlier.
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>>> borders = set([('a1', 'a2'), ('a2', 'a3')])
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>>> c2 = chat80.Concept('borders', arity=2, extension=borders)
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>>> print(c2)
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Label = 'borders'
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Arity = 2
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Extension = [('a1', 'a2'), ('a2', 'a3')]
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>>> c3 = chat80.Concept('borders', arity=2, closures=['symmetric'], extension=borders)
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>>> c3.close()
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>>> print(c3)
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Label = 'borders'
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Arity = 2
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Extension = [('a1', 'a2'), ('a2', 'a1'), ('a2', 'a3'), ('a3', 'a2')]
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The ``extension`` of a ``Concept`` object is then incorporated into a
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``Valuation`` object.
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Persistence
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===========
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The functions ``val_dump`` and ``val_load`` are provided to allow a
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valuation to be stored in a persistent database and re-loaded, rather
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than having to be re-computed each time.
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Individuals and Lexical Items
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=============================
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As well as deriving relations from the Chat-80 data, we also create a
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set of individual constants, one for each entity in the domain. The
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individual constants are string-identical to the entities. For
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example, given a data item such as ``'zloty'``, we add to the valuation
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a pair ``('zloty', 'zloty')``. In order to parse English sentences that
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refer to these entities, we also create a lexical item such as the
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following for each individual constant::
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PropN[num=sg, sem=<\P.(P zloty)>] -> 'Zloty'
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The set of rules is written to the file ``chat_pnames.fcfg`` in the
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current directory.
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SQL Query
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=========
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The ``city`` relation is also available in RDB form and can be queried
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using SQL statements.
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>>> import nltk
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>>> q = "SELECT City, Population FROM city_table WHERE Country = 'china' and Population > 1000"
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>>> for answer in chat80.sql_query('corpora/city_database/city.db', q):
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... print("%-10s %4s" % answer)
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canton 1496
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chungking 1100
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mukden 1551
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peking 2031
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shanghai 5407
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tientsin 1795
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The (deliberately naive) grammar ``sql.fcfg`` translates from English
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to SQL:
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>>> nltk.data.show_cfg('grammars/book_grammars/sql0.fcfg')
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% start S
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S[SEM=(?np + WHERE + ?vp)] -> NP[SEM=?np] VP[SEM=?vp]
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VP[SEM=(?v + ?pp)] -> IV[SEM=?v] PP[SEM=?pp]
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VP[SEM=(?v + ?ap)] -> IV[SEM=?v] AP[SEM=?ap]
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NP[SEM=(?det + ?n)] -> Det[SEM=?det] N[SEM=?n]
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PP[SEM=(?p + ?np)] -> P[SEM=?p] NP[SEM=?np]
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AP[SEM=?pp] -> A[SEM=?a] PP[SEM=?pp]
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NP[SEM='Country="greece"'] -> 'Greece'
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NP[SEM='Country="china"'] -> 'China'
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Det[SEM='SELECT'] -> 'Which' | 'What'
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N[SEM='City FROM city_table'] -> 'cities'
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IV[SEM=''] -> 'are'
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A[SEM=''] -> 'located'
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P[SEM=''] -> 'in'
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Given this grammar, we can express, and then execute, queries in English.
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>>> cp = nltk.parse.load_parser('grammars/book_grammars/sql0.fcfg')
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>>> query = 'What cities are in China'
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>>> for tree in cp.parse(query.split()):
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... answer = tree.label()['SEM']
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... q = " ".join(answer)
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... print(q)
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...
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SELECT City FROM city_table WHERE Country="china"
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>>> rows = chat80.sql_query('corpora/city_database/city.db', q)
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>>> for r in rows: print("%s" % r, end=' ')
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canton chungking dairen harbin kowloon mukden peking shanghai sian tientsin
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Using Valuations
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-----------------
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In order to convert such an extension into a valuation, we use the
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``make_valuation()`` method; setting ``read=True`` creates and returns
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a new ``Valuation`` object which contains the results.
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>>> val = chat80.make_valuation(concepts, read=True)
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>>> 'calcutta' in val['city']
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True
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>>> [town for (town, country) in val['country_of'] if country == 'india']
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['bombay', 'calcutta', 'delhi', 'hyderabad', 'madras']
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>>> dom = val.domain
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>>> g = nltk.sem.Assignment(dom)
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>>> m = nltk.sem.Model(dom, val)
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>>> m.evaluate(r'population_of(jakarta, 533)', g)
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True
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