grammar-detector 0.2.4

A grammatical feature detector for analyzing sentences, clauses, and phrases.

Grammar-Detector

A tool for detecting grammatical features in sentences, clauses, and phrases in just a few lines of code. This tool is one piece of a larger project to facilitate the creation of reading exercises for language instruction. It is designed to determine if a text contains sentences relevant to the desired grammatical feature. Any language supported by spaCy is theoretically supported.

The patterns for these grammatical features are defined in YAML files called patternsets in lieu of writing code. These YAML files expand the capabilities of the GrammarDetector. The input text to be analyzed is compared against the patterns in the patternsets. In other words, writing more code is unnecessary for supporting new grammatical features. This means that inaccurate results arise from inaccurate patterns (and not from the code itself). To mitigate errors, unittests can be defined in the patternsets.

For the purposes of this tool, a sentence is roughly defined as:

1. An independent clause with sentence-final punctuation and additional clauses, or
2. A dependent clause with sentence-final punctuation which may satisfy the concept of a 'complete thought' in the context of surrounding sentences (e.g. "We tried updating it. Which didn't work. Nor did the reinstall.")

Overview

The core of this tool is the GrammarDetector. After construction, it can be used in two different ways:

1. Using the GrammarDetector.__call__(self, input: str) instance method on the input to run automatically.
2. Looping through the GrammarDetector.detectors: list[Detector] instance property and using the Detector.__call__(self, input: str) instance method on the input to run manually.

Dependencies

Dependencies:

• python (>=3.9) -- frequent use of f-strings and type hints
• pyyaml -- loading patternset YAML files
• spacy -- rule-based grammatical pattern matching
• spacy-lookups-data -- spaCy dependency
• tabulate -- printing token tables to write patterns

Dev dependencies:

• black -- opinionated code formatter
• mypy -- type checking
• python-lsp-server -- IDE integration
• types-pyyaml -- type checking
• types-tabulate -- type checking
• types-setuptools -- type checking

Detector Features

Currently supports the ability to:

• Evaluate a sentence, clause, or phrase for its grammatical features
• Produce results that are reader-friendly and reader-useful
• Use built-in grammatical features with just 3 lines of code (import, construct, and call)
• Create your own grammatical feature by passing the filepath of a simple patternset YAML file with spaCy Tokens
• Convert input into a table of Tokens to aid in visualizing and conceptualizing patterns
• Convert input into list of Tokenlikes to aid in creating and improving patterns
• Define and run tests in patternsets to evaluate the accuracy of the patterns
• Fragment an input into noun chunks automatically before the Detector is run

Future features:

• Add support for validating patternset YAML files (currently validates patterns only)
• Publish the built-in patternset YAML files as a separate package

Grammatical Features

All current patterns are relatively naive, so they do not yet effectively handle recursivity. This problem can be solved by 1) writing recursive patterns or 2) writing alternative patterns and suffixing the rulename property with numbers (e.g. ditransitive-1 and ditransitive-2).

• Determiners:
  • Indefinite
  • Definite
  • Other
  • None
• Persons:
  • 1st
  • 2nd
  • 3rd
• Tense-Aspects:
  • Present simple
  • Present simple passive
  • Past simple
  • Past simple passive
  • Future simple will
  • Future simple will passive
  • Future simple be-going-to
  • Future simple be-going-to passive
  • Present continuous
  • Present continuous passive
  • Past continuous
  • Past continuous passive
  • Future continuous
  • Future continuous passive
  • Present perfect
  • Present perfect passive
  • Past perfect
  • Past perfect passive
  • Future perfect
  • Future perfect passive
  • Present perfect continuous
  • Present perfect continuous passive
  • Past perfect continuous
  • Past perfect continuous passive
  • Future perfect continuous
  • Future perfect continuous passive
• Transitivity and Valency:
  • Impersonal (valency == 0)
  • Intransitive (valency == 1)
  • Transitive (valency == 2)
  • Ditransitive (valency == 3)
• Voices:
  • Active
  • Passive

Installation

The default language model, en_core_web_md (40 MB), can be substituted with another spaCy language model, such as en_core_web_lg (560 MB) or en_core_web_sm (12 MB). Be sure to disable builtins (see below) if using a model from a language other than English.

$ pip install grammar-detector
$ python -m spacy download en_core_web_md

Usage

Usage: 0) Constructing the GrammarDetector

# my_script.py

from grammardetector import GrammarDetector


# Default values
settings = {  
    "builtins": True,
    "language_model": "en_core_web_md",
    "patternset_path": "",  # Custom patternsets
    "verbose": False,
    "very_verbose": False,
}
grammar_detector = GrammarDetector(**settings)  # Optionally, pass in **settings

Usage: 1) Running the GrammarDetector

# my_script.py

from grammardetector import GrammarDetector


grammar_detector = GrammarDetector()
input: str = "The dog was chasing a cat into the house."
results = grammar_detector(input)

Usage: 2) Interpreting the Results

# my_script.py

from grammardetector import GrammarDetector, Match
from typing import Union


ResultsType = dict[str, Union[str, list[Match]]]


grammar_detector = GrammarDetector()
input: str = "The dog was chasing a cat into the house."
results: ResultsType = grammar_detector(input)

print(results)
# {
#     'input': 'The dog was chasing a cat into the house.', 
#     'voices': [<active: was chasing>], 
#     'tense_aspects': [<past continuous: was chasing>], 
#     'persons': [<3rd: dog>, <3rd: cat>, <3rd: house>], 
#     'determiners': [<definite: The dog>, <indefinite: a cat>, <definite: the house>], 
#     'transitivity': [<ditransitive: dog was chasing a cat into the house>]
# }

feature: str = "tense_aspects"
verb_tense: Match = results[feature][0]

print(verb_tense)
# <past continuous: was chasing>

print(verb_tense.rulename)
# "past continuous"

print(verb_tense.span)
# "was chasing"

print(verb_tense.span_features)
# {
#     'span': was chasing, 
#     'phrase': 'was chasing', 
#     'root': 'chasing', 
#     'root_head': 'chasing', 
#     'pos': 'VERB', 
#     'tag': 'VBG', 
#     'dep': 'ROOT', 
#     'phrase_lemma': 'be chase', 
#     'root_lemma': 'chase', 
#     'pos_desc': 'verb', 
#     'tag_desc': 'verb, gerund or present participle', 
#     'dep_desc': 'root'
# }

Usage: Loading Patterns in Custom Patternset YAML Files

from grammardetector import GrammarDetector


grammar_detector = GrammarDetector(
    builtins=False,
    patternset_path="path/to/my/patternset/files/",
)
input: str = "The dog was chasing a cat into the house."
results = grammar_detector(input)

print(results)
# Prints only your custom features

Usage: Running Tests in Custom Patternset YAML Files

# my_script.py

from grammardetector import GrammarDetector


grammar_detector = GrammarDetector(patternset_path="path/to/my/patternset/files/")
grammar_detector.run_tests()

# Run the tests for the built-in patternsets
grammar_detector.run_tests(builtin_tests=True)

Usage: Printing Token Tables

# my_script.py

from grammardetector import GrammarDetector


grammar_detector = GrammarDetector()
input: str = "The dog was chasing a cat into the house."

default_kwargs = {
    "pos": True,
    "tag": True,
    "dependency": True,
    "lemma": True,
}
table: str = grammar_detector.token_table(input, **default_kwargs)
print(table)

---

Word	POS	POS Definition	Tag	Tag Definition	Dep.	Dep. Definition	Lemma.
The	DET	determiner	DT	determiner	det	determiner	the
dog	NOUN	noun	NN	noun, singular or mass	nsubj	nominal subject	dog
was	AUX	auxiliary	VBD	verb, past tense	aux	auxiliary	be
chasing	VERB	verb	VBG	verb, gerund or present participle	ROOT	root	chase
a	DET	determiner	DT	determiner	det	determiner	a
cat	NOUN	noun	NN	noun, singular or mass	dobj	direct object	cat
into	ADP	adposition	IN	conjunction, subordinating or preposition	prep	prepositional modifier	into
the	DET	determiner	DT	determiner	det	determiner	the
house	NOUN	noun	NN	noun, singular or mass	pobj	object of preposition	house
.	PUNCT	punctuation	.	punctuation mark, sentence closer	punct	punctuation	.

---

Usage: Printing Token Data

# my_script.py

from grammardetector import GrammarDetector, Tokenlike


# TokenlikeKeys = Literal["pos", "tag", "dep", "lemma", "word"]
# Tokenlike = dict[TokenlikeKeys, str]

grammar_detector = GrammarDetector()
input: str = "The dog was chasing a cat into the house."

default_kwargs = {
    "pos": True,
    "tag": True,
    "dependency": True,
    "lemma": False,
    "word": False,
}
data: list[Tokenlike] = grammar_detector.token_data(input, **default_kwargs)
for entry in data:
    print(entry)

# {'pos': 'DET', 'tag': 'DT', 'dep': 'det'}
# {'pos': 'NOUN', 'tag': 'NN', 'dep': 'nsubj'}
# {'pos': 'AUX', 'tag': 'VBD', 'dep': 'aux'}
# {'pos': 'VERB', 'tag': 'VBG', 'dep': 'ROOT'}
# {'pos': 'DET', 'tag': 'DT', 'dep': 'det'}
# {'pos': 'NOUN', 'tag': 'NN', 'dep': 'dobj'}
# {'pos': 'ADP', 'tag': 'IN', 'dep': 'prep'}
# {'pos': 'DET', 'tag': 'DT', 'dep': 'det'}
# {'pos': 'NOUN', 'tag': 'NN', 'dep': 'pobj'}
# {'pos': 'PUNCT', 'tag': '.', 'dep': 'punct'}

Usage: Troubleshooting

# my_script.py

from grammardetector import GrammarDetector


grammar_detector = GrammarDetector(verbose=True, very_verbose=False)  # very_verbose prioritized over verbose
# Prints logs for configuring and loading patternsets

input: str = "The dog was chasing a cat into the house."
results = grammar_detector(input)
# Prints logs for running the matcher and interpreting the results

Under the Hood

This section describes the components used to build and run Detectors inside the GrammarDetector. To expand on the built-in features of the GrammarDetector, understanding how patternset YAML files are created, configured, and loaded is critical. To load your own patternset files, pass the file or directory path to the patternset_path keyword argument when constructing the GrammarDetector.

The GrammarDetector class

The GrammarDetector class is the entrypoint for loading in patternset files and evaluating text input. By running the GrammarDetector.__call__(self, input: str) instance method, the text input will be compared against both the built-in patternsets and the provided patternsets via the patternset_path keyword argument. The DetectorRepository is contained under the hood, which in turn contains the Detectors. Extracting the internal Detectors from the GrammarDetector is unnecessary but easy via the GrammarDetector.detectors: list[Detector] instance property.

# my_script.py

from grammardetector import Detector, GrammarDetector


grammar_detector = GrammarDetector(patternset_path="path/to/my/patternset/files/")
input: str = "The dog was chasing a cat into the house."
results = grammar_detector(input)  # Making use of the __call__ method

# Alternatively, extract the detectors
detectors: list[Detector] = grammar_detector.detectors
for detector in detectors:
    print(detector(input))

Component: Token

The smallest piece is the spacy.tokens.Token class. Each Token represents a single word and consists of a single JSON object. A list[Token] represents a chain of words. Lists of Tokens are used in patternset YAML files to define grammatical patterns. Each Token contains a POS (part-of-speech), a TAG (tag), and/or a DEP (dependency). Grammatical categories are denoted with POS and TAG while syntactic categories are denoted with DEP. An OP (operation) may also be included to denote whether a Token is required or optional. A complete list of POSs, TAGs, and DEPs can be found in the spaCy glossary.

Some examples of POSs are "VERB", "AUX", "NOUN", "PROPN", and "SYM" for symbol.

Some examples of TAGs are "VB" for base form verb, "VBD" for past tense verb, "VBG" for gerund/present participle verb, "VBN" for past participle verb, "VBP" for non-3rd person singular present verb, and "VBZ" for 3rd person singular present verb.

Some examples of DEPs are "ROOT" for root verb, "aux", "auxpass", "nsubj", and "dobj".

Examples of Tokens

Token: Present Simple Verb

# my_feature.yaml

patterns:
    # This is a single token (i.e. 1 word)
    - rulename: present simple verb
      tokens:  
        - {TAG: {IN: ["VBP", "VBZ"]},  # IN == one of these

Token: Passive Auxiliary Verb

# my_feature.yaml

patterns:
    # This is also a single token (i.e. 1 word)
    - rulename: passive auxiliary
      tokens:  
        - {
          TAG: {IN: ["VBP", "VBZ"]},
          DEP: "auxpass",
          LEMMA: "be",
          OP: "+"
        }

Token: Future Simple Be-going-to Passive

# my_feature.yaml

patterns:
    # This is a list of 5 tokens (i.e. 5 words)
    - rulename: future simple be-going-to passive
      tokens:
        - {TAG: {IN: ["VBP", "VBZ"]}, DEP: "aux", OP: "+"}
        - {TAG: "VBG", OP: "+", LEMMA: "go"}
        - {TAG: "TO", DEP: "aux", OP: "+"}
        - {TAG: "VB", DEP: "auxpass", LEMMA: "be"}
        - {TAG: "VBN", OP: "+"}

Token: Ditransitive/Trivalency

# my_feature.yaml

patterns:
    # This is a list of 4 tokens minimum with some degree of recursivity
    - rulename: ditransitive
      tokens:
        - {DEP: "nsubj"}
        - {OP: "*"}  # Indicates possible filler words between the tokens
        - {DEP: "ROOT"}
        - {OP: "*"}
        - {DEP: {IN: ["dobj", "iobj", "pobj", "dative"]}}
        - {OP: "*"}
        - {DEP: {IN: ["dobj", "iobj", "pobj", "dative"]}}

Component: Patterns

Each Pattern in patterns has two properties:

1. rulename: str -- the name given to the Pattern with the corresponding list[spacy.tokens.Token]
2. tokens: list[spacy.tokens.Token] -- the grammatical pattern

---

# transitivity.yaml

config:
    how_many_matches: one

patterns:
    - rulename: ditransitive
      tokens:
          - {DEP: "nsubj"}
          - {OP: "*"}
          - {DEP: "ROOT"}
          - {OP: "*"}
          - {DEP: {IN: ["dobj", "iobj", "pobj", "dative"]}}
          - {OP: "*"}
          - {DEP: {IN: ["dobj", "iobj", "pobj", "dative"]}}

    - rulename: transitive
      tokens:
          - {DEP: "nsubj"}
          - {OP: "*"}
          - {DEP: "ROOT"}
          - {OP: "*"}
          - {DEP: "dobj"}

    - rulename: intransitive
      tokens:
          - {DEP: "nsubj", LOWER: {NOT_IN: ["it"]}}
          - {OP: "*"}
          - {DEP: "ROOT"}

    - rulename: impersonal
      tokens:
          - {TAG: "PRP", DEP: "nsubj", LOWER: "it"}
          - {OP: "*"}
          - {DEP: "ROOT"}

Component: Patternset YAML Files

The patternsets expand the capabilities of the GrammarDetector to detect for new features. The patternsets are created by loading YAML files containing these three properties:

1. patterns: list[Pattern] -- an array of named sets of tokens
2. config: dict[str, Union[str, bool]] -- a configuration object to modify input/output
3. tests: list[Test] -- an array of tests to validate the accuracy of the patterns

Internally, this data from the patternset file is converted into a PatternSet.

Patternset Files: Example for Active/Passive Voice

# voices.yaml

config:
    how_many_matches: one

patterns:
    - rulename: active
      tokens:
          - {DEP: "aux", OP: "*"}
          - {DEP: "ROOT"}

    - rulename: passive
      tokens:
          - {DEP: "aux", OP: "*"}
          - {DEP: "auxpass", OP: "+"}
          - {TAG: "VBN", DEP: "ROOT"}

tests:
    - input: The cat was chased by the dog.
      rulenames:
          - passive
      spans:
          - was chased

    - input: The dog chased the cat.
      rulenames:
          - active
      spans:
          - chased

Patternset Files: 1) Defining Rules via patterns

The patterns: list[dict[str, Union[str, list[spacy.tokens.Token]]]] list contains rules and grammatical patterns with the following properties:

• rulename: str -- the name of the grammatical pattern (e.g. "present simple")
• tokens: list[spacy.tokens.Token] -- the tokens of the grammatical pattern

Patternset Files: 2) Configuring via config

The config: dict[str, Union[str, bool]] dict contains several options for modifying the input and/or output:

• extract_noun_chunks: bool -- if true, then fragment the input into nouns before running the detector (default false)
• how_many_matches: str -- if "all", then get all matches; if "one", then get the longest match (default "all")
• skip_tests: bool -- if true, then skip all tests in the file when running the unittests (default false)

Patternset Files: 3) Testing via tests

The tests: list[dict[str, Union[str, bool, list[str]]]] list contains unittests with the following properties:

• input: str -- the sentence, clause, or phrase to be tested
• rulenames: list[str] -- the expected rulenames
• spans: list[str] -- the expected matching text
• skip: bool -- if true, then skip this test (but not the others)

Each test must contain 1) the input and 2) the rulenames and/or the spans. To run the tests in your patternset, call the GrammarDetector.run_tests(self) instance method (see Usage: Running Tests in Custom Patternset YAML Files).

Internal Component: PatternSet and PatternSetRepository

The PatternSetRepository reads a patternset YAML file and converts it into an internal PatternSet. The stored PatternSets can be retrieved individually by referencing its name as the cache key or retrieved collectively as a list[PatternSet]. The PatternSetRepository extends the Repository[Generic[T]] helper class for creating, caching, and querying.

Internal Component: PatternSetMatcher

The PatternSetMatcher is a wrapper class that is composed of an inner spacy.matcher.Matcher and logic to interpret PatternSets. The patterns defined in the PatternSets are automatically loaded into the inner Matcher. The raw matches from the inner Matcher are then converted into a reader-friendly format.

Internal Component: Detector

The Detector is the internal entrypoint by which a sentence, clause, or phrase is analyzed. A Detector contains one PatternSet and one PatternSetMatcher. Each Detector is bound to the specific grammatical feature of the PatternSet. After loading the GrammarDetector, its Detectors can be accessed via the GrammarDetector.detectors: list[Detector] instance property. This permits running them manually and reusing them. The GrammarDetector and Detectors are not bound to the text input.

Internal Component: DetectorRepository

The DetectorRepository is responsible for creating and storing Detectors. It is wrapped by the GrammarDetector class, the main entrypoint. The repository manages the PatternSetRepository and loads its PatternSets into the PatternSetMatchers. The DetectorRepository extends the Repository[Generic[T]] helper class for creating, caching, and querying.

Contributing

This tool is only as good as the patternset YAML files that support it. The primary ways to contribute to this project are:

• Creating new built-in patternsets
• Improving existing patterns in the built-in patternsets
• Adding tests to the built-in patternsets
• Adding new config options and features to the codebase

Cloning the repository:

$ git clone https://github.com/SKCrawford/grammar-detector.git

Preparing the dev environment:

$ pipenv shell
$ pipenv install --dev
$ python -m spacy download en_core_web_md

Running the GrammarDetector from the repository:

$ python -m grammardetector "The dog was chasing a cat into the house."

Running the patternset unittests from the repository:

$ python -m unittest

To add new grammatical features or improve existing features, focus your efforts on the patternsets directory and its YAML files. You may find the token tables generated by the GrammarDetector.token_table(self, input: str, **kwargs) instance method to be helpful for conceptualizing sequences of tokens. You may also find the tokenlike lists generated by the GrammarDetector.token_data(self, input: str, **kwargs) instance method to be helpful when generating new patterns or improving upon existing patterns.

Submissions of patternset files will be rejected if they do not include tests for each pattern.

Authors

Steven Kyle Crawford

Version History

• 0.2.4
  • New feature: Generate lists of tokens, which may be adapted for use in patternset files, via the GrammarDetector.token_data(self, input: str, **kwargs) instance method.
  • Export the Tokenlike return type for the token_data method for type safety.
  • Improve docstrings for the token_data and token_table methods in the GrammarDetector class and utilities package.
• 0.2.3
  • Rename the GrammarDetector constructor keyword argument from dataset to language_model.
  • Change the default language model from en_core_web_lg to en_core_web_md
• 0.2.2
  • Rename the patternset file property from meta to config. Retain usage of meta internally to avoid confusion with the Config class.
  • Rename the run_tests keyword argument from internal_tests to builtin_tests.
  • Bugfix the repository's test suite runner.
• 0.2.1
  • Improve readme readability
• 0.2.0
  • Alpha release
• 0.1.0
  • Pre-alpha release

License

This project is licensed under the GNU General Public License V3. See the LICENSE.txt file for details.

Acknowledgments

• spaCy - Free open-source library for Natural Language Processing in Python (license)