Lexical Variants


ClarityNLP uses the term lexical variants to mean either plurals, verb inflections, or both. Pluralization is a familiar concept and is assumed to be self-explanatory. English verbs have four inflected forms (i.e. a different ending depending on use), which are as follows, using the verb ‘walk’ as an example:

Description Inflected Form
bare infinitive (base form) walk
3rd person singular present walks
present participle walking
past tense (preterite) walked
past participle walked

Regular English verbs have inflected forms that can be computed from relatively straightforward rules (but there are many exceptions). Irregular verbs have inflected forms for the past tense and/or past participle that violate the rules.

ClarityNLP includes a pluralizer and a verb inflector that attempt to compute the plurals and inflected forms of English words. The verb inflector ignores archaic forms and focuses primarily on contemporary American English.


The ClarityNLP pluralizer generates plural forms of words and phrases. Several functions are offered depending on whether the part of speech of the term to be pluralized is known. The source code for the pluralizer can be found in nlp/algorithms/vocabulary/pluralize.py. The pluralizer is mainly a wrapper around the Python port of Damian Conway’s well-known inflect module [1]. An error-correction mechanism has also been incorporated to improve the module’s performance on medical text.


A single string, representing the word or phrase to be pluralized.


A list of strings containing all known plural forms for the input.


The functions provided by the pluralize module are (all arguments are strings):


Use the more specific functions if the part of speech of the input text is known. Use plural if nothing is known about the text.

Verb Inflections

The verb inflector module computes verb inflections from a given verb in base form. The base form of a verb is also known as “plain form”, “dictionary form”, “bare infinitive form”, or as the “principal part” of the verb. Here is a list of some common verbs and their base forms:

Verb Base Form
running run
walks walk
eaten eat
were be

It is not possible to unambiguously compute the base form of a verb from an arbitrary inflected form. Observe:

Verb Possible Base Forms
clad clad (to cover with material), clothe (to cover with clothes)
cleft cleave (to split), cleft (to separate important parts of a clause)
fell fell (to make something fall), fall (to take a tumble)
lay lay (to set down), lie (to rest on a surface)

The only way to unambiguously recover the base form from an arbitrary inflection is to supply additional information such as meaning, pronounciation, or usage.

Lemmatizers attempt to solve this problem, but with decidedly mixed results. Neither the NLTK WordNet lemmatizer nor the Spacy lemmatizer worked reliably enough on this module’s test data to allow users to input verbs in arbitrary inflections. Lemmatization is still an area of active NLP research, so these results are not necessarily surprising.

Therefore, for all of these reasons, the ClarityNLP verb inflector requires the input verb to be provided in base form.

Source Code

The source code for the verb inflector is located in nlp/algorithms/vocabulary/verb_inflector.py. Supporting files in the same directory are inflection_truth_data.txt, irregular_verbs.py, and the files in the verb_scraper directory. The purpose of the supporting files and software will be described below.


The entry point to the verb inflector is the get_inflections function, which takes a single string as input. The string is a verb in base form as described above.


The get_inflections function returns all inflections for the verb whose base form is given. The inflections are returned as a five-element list, interpreted as follows:

Element Interpretation
0 [string] the base form of the verb
1 [list] third-person singular present forms
2 [list] present participle forms
3 [list] simple past tense (preterite) forms
4 [list] past participle forms

The lists returned in components 1-4 are all lists of strings. Even if only a single variant exists for one of these components, it is still returned as a single-element list, for consistency.


inflections = verb_inflector.get_inflections('outdo')
# returns ['outdo',['outdoes'],['outdoing'],['outdid'],['outdone']]

inflections = verb_inflector.get_inflections('be')
# returns ['be',['is'],['being'],['was','were'],['been']]


The verb inflector uses different algorithms for the various inflections. A high-level overview of each algorithm will be presented next. The verb inflector uses a list of 558 irregular verb preterite and past participle forms scraped from Wikipedia and Wiktionary to support its operations.

It should be stated that the rules below have been gleaned from various grammar sources scattered about the Internet. Some grammar sites present subsets of these rules; others present some rules without mentioning any exceptions; and other sites simply present incorrect information. We developed these algorithms iteratively, over a period of time, adjusting for exceptions and violations as we found them. This is still a work in progress.

Algorithm for the Third-Person Singular Present

The third-person singular present can be formed for most verbs, either regular or irregular, by simply adding an s character to the end. Some highly irregular verbs such as be and a few others are stored in a list of exceptions. If the base form of the verb appears in the exception list, the verb inflector performs a simple lookup and returns the result.

If the base form is not in the exception list, the verb inflector checks to see if it ends in a consonant followed by y. If so, the terminating y is changed to an i and an es is added, such as for the verb try, which has the third-person singular present form tries.

If the base form instead ends in a consonant followed by o, an es is appended to form the result. An example of such a verb would be echo, for which the desired inflection is echoes.

If the base form has neither of these endings, the verb inflector checks to see if it ends in a sibilant sound. The sibilant sounds affect the spelling of the third-person singular inflection in the presence of a silent-e ending [2]. The CMU pronouncing dictionary [3] is used to detect the presence of sibilant sounds. The phonemes for these sounds are based on the ARPAbet [4] phonetic transcription codes and appear in the next table:

Sibilant Sound Phoneme
voiceless alveolar sibilant S
voiced alveolar sibilant Z
voiceless postalveolar fricative SH
voiced postalveolar fricitave ZH
voiceless postalveolar affricate CH
voiced postalveolar affricate JH

If the base form ends in a sibilant sound and has no silent-e ending, an es is appended to form the desired inflection. Otherwise, an s is appended to of the base form and returned as the result.

Algorithm for the Present Participle

The verb inflector keeps a dictionary of known exceptions to the rules for forming the present participle. Most of these exceptional verbs are either not found in the CMU pronouncing dictionary, or are modal verbs, auxiliaries, or other irregular forms. Some verbs also have multiple accepted spellings for the present participle, so the verb inflector keeps a list of these as well. If the base form of the given verb appears as an exception, a simple lookup is performed to generate the result.

If the base form of the verb is not a known exception, the verb inflector determines whether the base form ends in ie. If it does, the ie is changed to ying and appended to the base form to generate the result. An example of such a verb is tie, which has the form tying as the present participle.

Next the verb inflector checks the base form for an ee, oe, or ye ending. If one of these endings is present, the final e is retained, and ing is appended to the base form and returned as the result.

If the base form ends in vowel-l, British spelling tends to double the final l before appending ing, but American spelling does not. For many verbs both the British and American spellings are common, so the verb inflector generates both forms and returns them as the result. There appears to be one exception to this rule, though. If the vowel preceding the final l is an i, the rule does not seem to apply (such as for the verb sail, whose present participle form is sailing, not sailling).

If none of these tests succeed, the verb inflector checks for pronounciation- dependent spellings using the CMU pronouncing dictionary. If the base form has a silent-e ending, the final e is dropped and ing is appended to the base verb to form the result, unless the base form is a known exception to this rule, in which case the final e is retained.

The verb inflector next checks for a pronunciation-dependent spelling caused by consonant doubling. The rules for consonant doubling are presented in the next section. The verb inflector doubles the final consonant if necessary, appends ing, and returns that as the result.

If none of the tests succeeds, the verb inflector appends ing to the base form and returns that as the result.

Algorithm for Consonant Doubling

If the base form of the verb ends in c, a k should generally be appended prior to the inflection ending. There are a few exceptions to this rule that the verb inflector checks for.

If the base form of the verb ends in two vowels followed by a consonant, the rule is generally to not double the final consonant. One exception to this rule is if the first vowel is a u preceded by q. In this case the u is pronounced like a w, so the qu acts as if it were actually qw. This gives the word an effective consonant-vowel-consonant ending, in which case the final consonant is doubled. An example of this would be the verb equip, which requires a doubled p for inflection (equipping, equipped, etc.).

If the base form of the verb has a vowel-consonant ending, and if the consonant is not a silent-t, then the final consonant is doubled for single syllable verbs. If the final syllable is stressed, the final consonant is also doubled. Otherwise the final consonant is not doubled prior to inflection.

Algorithm for the Simple Past Tense

If the verb is irregular, its past tense inflection cannot be predicted, so the verb inflector simply looks up the past tense form in a dict and returns the result. A lookup is also performed for a small list of regular verbs that are either known exceptions to the rules, or which have multiple accepted spellings for the past tense forms.

If the verb is regular and not in the list of exceptions, the verb inflector checks the base form for an e ending. If the verb ends in e, a d is appended and returned as the result.

If the base form instead ends in a consonant followed by y, the y is changed to i and ed is appended and returned as the result.

If the base form ends in a vowel followed by l, both the American and British spellings are returned, as described above for the present participle. The British spelling appends led to the base form, while the American spelling only appends ed.

If the final consonant requires doubling, the verb inflector appends the proper consonant followed by ed and returns that as the result.

Otherwise, ed is appended to the base form and returned as the result.

Algorithm for the Past Participle

The past participle for irregular verbs is obtained by simple lookup. The past participle for a small number of regular verbs with multiple accepted spellings is also obained via lookup. Otherwise, the past participle for regular verbs is equivalent to the simple past tense form.

Testing the Verb Inflector

The file verb_inflector.py includes 114 test cases that can be run via the --selftest command line option. A more extensive set of 1364 verbs and all inflected forms can be found in the file inflection_truth_data.txt. This list consists of the unique verbs found in two sets: the set of irregular English verbs scraped from Wikipedia [5], and the set of the 1000 most common English verbs scraped from poetrysoup.com [6]. The verb_inflector will read the file, compute all inflections for each verb, and compare with the data taken from the file using this command:

python3 ./verb_inflector.py -f inflection_truth_data.txt

The code for scraping the verbs and generating the truth data file can be found in the verb_scraper folder.

To generate the truth data file, change directories to the verb_scraper folder and run this command:

python3 ./scrape_verbs.py

Two output files will be generated:

  • verb_list.txt, a list of the unique verbs found
  • irregular_verbs.py, data structures imported by the verb inflector

In addition to scraping verb data, this code also corrects for some inconsistencies found between Wikipedia and the Wiktionary entries for each verb.

Copy irregular_verbs.py to the folder that contains verb_inflector.py, which should be the parent of the verb_scraper folder.

Next, scrape the inflection truth data from Wiktionary for each verb in verb_list.txt:

python3 ./scrape_inflection_data.py

This code loads the verb list, constructs the Wiktionary URL for each verb in the list, scrapes the inflection data, corrects further inconsistencies, and writes the output file raw_inflection_data.txt. Progress updates appear on the screen as the run progresses.

Finally, generate the truth data file with this command:

python3 ./process_scraped_inflection_data.py