wordkit

This is the repository of the wordkit package, a Python 3.X package for the featurization of words into orthographic and phonological vectors.

Overview

wordkit is a package for working with words. The package contains a variety of functions that allow you to:

• Extract words from lexical databases in a structured format.
• Normalize phonological strings across languages and databases.
• Featurize words for usage in computational psycholinguistic models using the following features:
  • Open ngrams
  • Character ngrams
  • Holographic features
  • Consonant Vowel mapping (patpho)
  • Onset Nucleus Coda mapping
• Find synonyms, homographs, and homophones across languages.
• Fuse lexical databases, also crosslingually.
• Sample from (subsets of) corpora by frequency of occurrence.

and much more.

Installation

wordkit is on pip.

pip install wordkit

Examples

See the examples for some ways in which you can use wordkit. All examples assume you have wordkit installed (see above.)

More

If, after working through the examples, you want to dive deeper into wordkit, check out the following documentation.

wordkit is a modular system, and contains two broad families of components. The subpackages are documented using separate README.MD files. Feel free to click ahead to find descriptions of the contents of subpackages.

• corpora
• features

In general, a wordkit pipeline consists of one or more readers, which extract structured information from corpora. This information is then sent to one or more transformers, which are either assigned pre-defined features or a feature extractor.

Paper

A paper that describes wordkit was accepted at LREC 2018. If you use wordkit in your research, please cite the following paper:

@InProceedings{TULKENS18.249,
  author = {Tulkens, Stéphan and Sandra, Dominiek and Daelemans, Walter},
  title = {WordKit: a Python Package for Orthographic and Phonological Featurization},
  booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},
  year = {2018},
  month = {may},
  date = {7-12},
  location = {Miyazaki, Japan},
  editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga},
  publisher = {European Language Resources Association (ELRA)},
  address = {Paris, France},
  isbn = {979-10-95546-00-9},
  language = {english}
  }

Additionally, if you use any of the corpus readers in wordkit, you MUST cite the accompanying corpora and transformers. All of these references can be found in the docstrings of the applicable classes.

Example

This example shows one big wordkit pipeline.

import pandas as pd

from wordkit.corpora import celex_english, celex_dutch
from wordkit.features import LinearTransformer, NGramTransformer, fourteen
from string import ascii_lowercase

# The fields we want to extract from our corpora.
fields = ('orthography', 'frequency', 'phonology', 'syllables')

# Link to epl.cd
english = celex_english("epw.cd",
                        fields=fields)
# Link to dpl.cd
dutch = celex_dutch("dpw.cd",
                    fields=fields)

# Merge both corpora.
words = pd.concat([english, dutch], sort=False).reindex()

# We filter both corpora to only contain monosyllables and words
# with only alphabetical characters
words = words[[len(x) == 1 for x in words["syllables"]]]
words = words[[not set(x) - set(ascii_lowercase)
              for x in words["orthography"]]]

# words.iloc[0] =>
# orthography                      a
# phonology                   (e, ɪ)
# syllables                ((e, ɪ),)
# frequency                   844672
# log_frequency              5.92669
# frequency_per_million        21363
# zipf_score                 4.32966
# length                           1

# You can also query specific words
wind = words[words['orthography'] == "wind"]

# This gives
# wind =>
#        orthography        phonology  ... zipf_score  length
# 146523        wind  (w, a, ɪ, n, d)  ...   0.015757       4
# 146524        wind     (w, ɪ, n, d)  ...   1.683096       4
# 313527        wind     (w, ɪ, n, t)  ...   2.042675       4

# Now, let's transform into features
# Orthography is a linear transformer with the fourteen segment feature set.
o = LinearTransformer(fourteen, field='orthography')
# For phonology we use ngrams.
p = NGramTransformer(n=3, field='phonology')

X_o = o.fit_transform(words)
X_p = p.fit_transform(words)

# Get the feature vector length for each featurizer
o.vec_len # 126
p.vec_len # 5415

Corpora

wordkit currently offers readers for the following corpora. Note that, while we offer predefined fields for all these corpora, any fields present in these data can be retrieved by wordkit in addition to the fields we define. The Lexicon Projects, for example, also contain lexicality information, accuracy information, and so on. These can be retrieved by passing the appropriate fields as argument to fields.

BPAL

Download

You have to extract the nwphono.txt file from the .exe file. The corpus is not for download in a more practical fashion.

Publication

Fields:     Orthography, Phonology, Frequency
Languages:  Spanish

Celex

Currently not freely available.

Fields:     Orthography, Phonology, Syllables, Frequency
Languages:  Dutch, German, English

WARNING: the Celex frequency norms are no longer thought to be correct. Please use the SUBTLEX frequencies instead. You can use the Celex corpus with SUBTLEX frequency norms by using a pandas merge. If you use CELEX frequency norms at a psycholinguistic conference, you will get yelled at.

CMUDICT

Download

We can read the cmudict.dict file from the above repository.

Fields:     Orthography, Syllables  
Languages:  American English

Deri

Download

Download the pron_data.tar.gz file, and unzip it. We use the gold_data_train file.

Publication

Fields:     Orthography, Phonology  
Languages:  lots

WARNING: we manually checked the Dutch, Spanish and German phonologies in this corpus, and a lot of them seem to be incorrectly transcribed or extracted. Only use this corpus if you don't have another resource for your language.

Lexique

Download

Download the zip file, we use the lexique382.txt file.

Publication

Note that this is the publication for Lexique version 2. Lexique 3 does not seem to have an associated publication in English.

Fields:     Orthography, Phonology, Frequency, Syllables  
Languages:  French

NOTE: the currently implemented reader is for version 3.82 (the most recent version as of May 2018) of Lexique.

SUBTLEX

Check the link below for the various SUBTLEX corpora and their associated publications. We support all of the formats from the link below.

Link

Fields:     Orthography, Frequency  
Languages:  Dutch, American English, Greek
            British English, Polish, Chinese,
            Spanish

Wordnet

We support all the tab-separated formats of the open multilingual WordNet. If you use any of these WordNets, please cite the appropriate source, as well as the official WordNet reference.

Link

Fields: Orthography, Semantics
Languages: lots

Lexicon projects

We support all lexicon projects. These contain RT data with which you can validate models.

Link

Fields: Orthography, rt
Languages: Dutch, British English, American English, French

Experiments

The code for replicating the experiments in the wordkit paper can be found here

Requirements

• ipapy
• numpy
• pandas
• reach (for the semantics)
• nltk (for wordnet-related semantics)

Contributors

Stéphan Tulkens

License

GPL v3