Computational Construction Grammar, or c2xg, is a Python package for learning and working with construction grammars.
Why CxG? Constructions are grammatical entities that support a straight-forward quantification of linguistic structure.
This package currently support 18 languages: English (eng), Arabic (ara), Danish (dan), German (deu), Greek (ell), Farsi (fas), Finnish (fin), French (fra), Hindi (hin), Indonesian (ind), Italian (ita), Dutch (nld), Polish (pol), Portuguese (por), Russian (rus), Spanish (spa), Swedish (swe), Turkish (tur)
More detailed linguistic documentation is available in the draft book Computational Construction Grammar: A Usage-Based Approach available at https://www.jdunn.name/cxg
Usage examples in a working environment are available at https://doi.org/10.24433/CO.9944630.v1
Detailed descriptions of each pre-trained grammar are available at https://doi.org/10.17605/OSF.IO/SA6R3
To install the full package and its dependencies with pip, use:
pip install git+https://github.com/jonathandunn/c2xg.git
A number of pre-trained grammar models are available for download and use with C2xG.
Note: The download_model() function must be used to install pre-trained grammars.
from c2xg import download_model
Models can be downloaded as follows:
download_model(model = False, data_dir = None, out_dir = None)
These parameters are as follows:
model (str) Name of a pre-trained grammar model or its shortcut
data_dir (str) Main data directory, creates 'data' in current directory if none given
out_dir (str) Output data directory, creates 'OUT' in main data directory if none given
For example, to download the model pre-trained on an English blogs corpus, use:
download_model(model = "BL", data_dir = "CxG_data")
Models have been pre-trained for each of the languages above, with additional English models trained on separate corpora. To see the models available, please view the following lists:
View: General Models
"ara": "cxg_multi_v02.ara.1000k_words.model.zip",
"dan": "cxg_multi_v02.dan.1000k_words.model.zip",
"deu": "cxg_multi_v02.deu.1000k_words.model.zip",
"ell": "cxg_multi_v02.ell.1000k_words.model.zip",
"eng": "cxg_multi_v02.eng.1000k_words.model.zip",
"fas": "cxg_multi_v02.fas.1000k_words.model.zip",
"fin": "cxg_multi_v02.fin.1000k_words.model.zip",
"fra": "cxg_multi_v02.fra.1000k_words.model.zip",
"hin": "cxg_multi_v02.hin.1000k_words.model.zip",
"ind": "cxg_multi_v02.ind.1000k_words.model.zip",
"ita": "cxg_multi_v02.ita.1000k_words.model.zip",
"nld": "cxg_multi_v02.nld.1000k_words.model.zip",
"pol": "cxg_multi_v02.pol.1000k_words.model.zip",
"por": "cxg_multi_v02.por.1000k_words.model.zip",
"rus": "cxg_multi_v02.rus.1000k_words.model.zip",
"spa": "cxg_multi_v02.spa.1000k_words.model.zip",
"swe": "cxg_multi_v02.swe.1000k_words.model.zip",
"tur": "cxg_multi_v02.tur.1000k_words.model.zip",
View: English Corpus Models
"BL": "cxg_corpus_blogs_final_v2.eng.1000k_words.model.zip",
"NC": "cxg_corpus_comments_final_v2.eng.1000k_words.model.zip",
"EU": "cxg_corpus_eu_final_v2.eng.1000k_words.model.zip",
"PG": "cxg_corpus_pg_final_v2.eng.1000k_words.model.zip",
"PR": "cxg_corpus_reviews_final_v2.eng.1000k_words.model.zip",
"OS": "cxg_corpus_subs_final_v2.eng.1000k_words.model.zip",
"TW": "cxg_corpus_tw_final_v2.eng.1000k_words.model.zip",
"WK": "cxg_corpus_wiki_final_v2.eng.1000k_words.model.zip",
To use C2xG, it must first be initialised with the following command.
note: This process may take a few minutes depending on your machine.
from c2xg import C2xG
CxG = C2xG(model = "BL")
Initialisation accepts the following parameters:
model (str) Pre-trained grammar file name in the out directory, or corresponding shortcut
See "Download Models" and "Further Documentation" for more
data_dir (str) Working directory, creates 'data' in current directory if none given
in_dir (str) Input directory name, creates 'IN' in 'data_dir' if none given
out_dir (str) Output directory name, creates 'OUT' in 'data_dir' if none given
language (str) Language for file names, default 'N/A'
nickname (str) Nickname for file names, default 'cxg'
max_sentence_length (int) Cutoff length for loading a given sentence, 50 by default
normalization (bool) Normalize frequency by ngram type and frequency strata, True by default
max_words (bool) Limit the number of words when reading input data, False by default
cbow_file (str) Name of cbow file to load or create
sg_file (str) Name of skip-gram file to load or create
For example, to initialise an instance of C2xG with the English Wiki corpus in the folder "CxG_data", use:
CxG_wiki = C2xG(model = "WK", data_dir = "CxG_data")
CxG.parse()
The parse() function takes a text, file name, or list of file names and returns a sparse matrix with construction frequencies for each line in the text.
CxG.parse(self, input, input_type = "files", mode = "syn", third_order = False)
Which accepts the following parameters:
input (str or list of str) A filename or list of filenames to be parsed, sourced from 'in' directory
input_type (str) "files" if input contains filenames or "lines" if input contains data
mode (str, default "syn") Type(s) of constructions to be parsed ("lex", "syn", "full", or "all")
third_order (bool) Whether third-order constructions are used, False by default
For example, to take a text file in the 'in' directory and parse it for lexical constructions, use:
parse_lex = CxG.parse(input = "my_sentences.txt", input_type = "files", mode = "lex")
CxG.parse_types()
The parse_types() function takes a text, file name, or list of file names and returns a sparse matrix with construction type frequencies over all inputs.
CxG.parse_types(self, input, input_type = "files", mode = "syn", third_order = False)
Which takes the following parameters:
input (str or list of str) A filename or list of filenames to be parsed, sourced from 'in' directory
input_type (str) "files" if input contains filenames or "lines" if input contains data
mode (str, default "syn") Type(s) of representations to be parsed ("lex", "syn", "full", or "all")
third_order (bool) Whether third-order constructions are used, False by default
For example, to take a text file in the 'in' directory and parse it for all constructions types, use:
parse_all_types = CxG.parse_types(input = "my_sentences.txt", input_type = "files", mode = "all")
CxG.get_type_token_ratio()
The get_type_token_ratio() method takes a text, file name, or list of file names and returns the following: the type and token counts for all inputs, and the ratio thereof.
get_type_token_ratio(self, input_data, input_type, mode = "syn", third_order = False)
Which takes the following parameters:
input (str or list of str) A filename or list of filenames to be parsed, sourced from 'in' directory
input_type (str) "files" if input contains filenames or "lines" if input contains data
mode (str, default "syn") Type(s) of representations to be parsed ("lex", "syn", "full", or "all")
third_order (bool) Whether third-order constructions are used, False by default
For example, to take a text file in the 'in' directory and obtain the lexical construction type/token counts and type-token ratio, use:
get_ratio = CxG.get_type_token_ratio(input = "my_sentences.txt", input_type = "files", mode = "lex")
CxG.get_association()
The get_association() function returns a dataframe with assocation measures for word pairs in the input data. This dataframe includes the words in the pair, their Delta-P scores in both left and right directions, the difference in scores, the maximum score, and the frequency within the data.
note: For more on these measures, see https://arxiv.org/abs/2104.01297
get_association(self, freq_threshold = 1, normalization = True, grammar_type = "full", lex_only = False, data = False)
Which takes the following parameters:
freq_threshold (int) Only consider bigrams above this frequency threshold, 1 by default
normalization (bool) Normalize frequency by ngram type and frequency strata, True by default
grammar_type (str) Suffix for pickle file name for file containing discounts, default "full"
lex_only (bool) Limit n-grams examined to lexical entries only, False by default
data (str or list of str) A filename or list of filenames to be parsed, sourced from 'in' directory
For example, to examples word pairs that occur at least ten times, use:
delta_association = CxG.get_association(input = "my_sentences.txt", freq_threshold = 10)
CxG.print_constructions()
The print_constructions() function prints, returns, and creates the file "temp.txt" in the 'out' directory containing a list of constructions of the selected type and their IDs from the initialised model.
print_constructions(self, mode="lex")
Which takes the following parameters:
mode (str, default "lex") Type(s) of representations to be examined ("lex", "syn", "full", or "all")
For example, to print all constructions in the model loaded, use
CxG_wiki = C2xG(model = "WK", data_dir = "CxG_data") # initialise model, as above
all_wiki_constructions = CxG.print_constructions(mode = "all)
CxG.print_examples()
The print_examples() function creates the file "temp.txt" with a list of constructions in the 'out' directory containing a list of constructions of the selected type and their IDs from the initialised model (or grammar) with examples from the selected data.
print_examples(self, grammar, input_file, n = 50, output = False, send_back = False)
Which takes the following parameters:
grammar (str or CxG.grammar) Type of grammar to examine ("lex", "syn", "full", "all")
Alternatively, grammars can be specified with:
C2xG.{type}_grammar.loc[:,"Chunk"].values'
input_file (str) A filename or list of filenames to be parsed, sourced from 'in' directory
n (int) Limit examples per construction, 50 by default
output (bool) Print examples in console, False by default
send_back (bool) Return examples as variable, False by default
For example, to print and return 10 examples of each syntactic construction in the chosen data, use:
syn_examples = CxG.print_examples(grammar = "syn", input_file = "my_sentences.txt", n = 10,
output = True, send_back = true)
CxG.learn()
The learn() function creates and returns new grammar models like those obtained using the download_model() function, using a given data input. This function returns three separate dataframes for lexical, syntactic, and full grammars.
note: This function is likely to take some time, especially with more learning/forgetting rounds.
learn(self, input_data, npmi_threshold = 0.75, starting_index = 0, min_count = None,
max_vocab = None, cbow_range = False, sg_range = False, get_examples = True,
increments = 50000, learning_rounds = 20, forgetting_rounds = 40, cluster_only = False)
Which takes the following parameters:
input_data (str or list of str) A filename or list of filenames to be parsed, sourced from 'in' directory
npmi_threshold (int) Normalised pointwise mutual information threshold value, 0.75 by default.
For use with 'gensim.Phrases', for more information see:
https://radimrehurek.com/gensim/models/phrases.html
starting_index (int) Index in input to begin learning, if not the beginning, 0 by default
min_count (int) Minimum ngram token count to maintain. If none, derived from 'max_words' during initialisation
max_vocab (int) Maximum vocabulary size, no maximum by default
cbow_range (int) Maximum cbow clusters, 250 by default
sg_range (int) Maximum skip-gram clusters, 2500 by default
get_examples (bool) If true, also run 'get_examples'. Use 'help(C2xG.get_examples)' for more.
increments (int) Defines both the number of words to discard and where to stop, 50000 by default
learning_rounds (int) Number of learning rounds to build/refine vocabulary, 20 by default
forgetting_rounds (int) Number of forgetting rounds to prune vocabulary, 40 by default
cluster_only (bool) Only use clusters from embedding models, False by default
Each learning fold consists of three tasks: (i) estimating association values from background data; this requires a large amount of data (e.g., 20 files); (ii) extracting candidate constructions; this requires a moderate amount of data (e.g., 5 files); (iii) evaluating potential grammars against a test set; this requires a small amount of data (e.g., 1 file or 10 mil words).
The freq_threshold is used to control the number of potential constructions to consider. It can be set at 20. The turn limit controls how far the search process can go. It can be set at 10.
For example, to create a simple model with only two rounds, eight rounds of forgetting, a cbow range of 50, and a skip-gram range of 500, while also getting a list of examles use:
lex_gram, syn_gram, full_gram = CxG.learn(input_file = "my_sentences.txt", get_examples = True,
learning_rounds = 2, forgetting_rounds = 8,
cbow_range = 50, sg_range = 500)
Example of using CxG.learn()
Below is a simple example that shows the code necessary to learn a new grammar with an existing set of embeddings. In this example, the input corpus is the file "corpus.blogs.gz" and it is contained in DATA > IN. The embedding files are passed using the cbow_file and sg_file parameters. Their expected location is in DATA > OUT. These are the default locations; it is also possible to specify different locations when initializing the C2xG class.
cxg = c2xg.C2xG(data_dir = "DATA",
cbow_file = "training_corpus.01.cbow.bin",
sg_file = "training_corpus.01.sg.bin",
)
cxg.learn(input_data = "corpus.blogs.gz", max_vocab = 10000)
This example will first cluster the sg and cbow embeddings to form the categories needed to formulate slot constraints. It will then proceed to learning the grammar itself.
CxG.learn_embeddings()
The learn_embeddings() function creates new cbow and skip-gram embeddings using input data. The learn() function will do this automatically by default, but this function generates them in isolation.
For quality assurance, it is best when possible to learn and examine the embeddings directly, for instance using Gensim.
note: Embeddings are stored in the class as 'self.cbow_model' and 'self.sg_model'.
learn_embeddings(self, input_data, name="embeddings")
Which takes the following parameters:
input_data (str or list of str) A filename or list of filenames to be parsed, sourced from 'in' directory
name (str) The nickname to use when saving models, 'embeddings' by default.
For example, learn embeddings and save them with the nickname "new_embeddings":
CxG.learn_embeddings(input_file = "my_sentences.txt", name = "new_embeddings)