LexFindR: Find Related Items and Lexical Dimensions in a Lexicon

Description

Implements code to identify lexical competitors in a given list of words. We include many of the standard competitor types used in spoken word recognition research, such as functions to find cohorts, neighbors, and rhymes, amongst many others. The package includes documentation for using a variety of lexicon files, including those with form codes made up of multiple letters (i.e., phoneme codes) and also basic orthographies. Importantly, the code makes use of multiple CPU cores and vectorization when possible, making it extremely fast and able to handle large lexicons. Additionally, the package contains documentation for users to easily write new functions, allowing researchers to examine other relationships within a lexicon. Preprint: https://osf.io/preprints/psyarxiv/8dyru/. Open access: doi:10.3758/s13428-021-01667-6. Citation: Li, Z., Crinnion, A.M. & Magnuson, J.S. (2021). doi:10.3758/s13428-021-01667-6.

Author(s)

Maintainer: ZhaoBin Li li_zhaobin@icloud.com

Authors:

• Anne Marie Crinnion anne.crinnion@uconn.edu

• James S. Magnuson james.magnuson@uconn.edu [copyright holder]

See Also

Useful links:

• https://github.com/maglab-uconn/LexFindR

• Report bugs at https://github.com/maglab-uconn/LexFindR/issues

Escape sep and target in regex

Description

Escape sep and target in regex

Usage

add_escape(target, sep, split = TRUE)

Arguments

Value

escaped sep and target

Check input and raise warnings as needed

Description

Check input and raise warnings as needed

Usage

check_input(target, lexicon, sep)

Arguments

Value

the indexes of the competitors in the lexical database

Get cohort competitors

Description

Cohorts overlap in onset phoneme(s).

Usage

get_cohorts(
  target,
  lexicon,
  sep = " ",
  form = FALSE,
  count = FALSE,
  overlap = 2
)

Arguments

Value

the indexes of the competitors in the lexical database

Examples

get_cohorts("AA R K", c("AA R K", "AA R T", "B AA B"))

Get CohortsPrime

Description

Cohorts that are not neighbors

Usage

get_cohortsP(
  target,
  lexicon,
  neighbors = "das",
  sep = " ",
  form = FALSE,
  count = FALSE
)

Arguments

Value

the indexes of the competitors in the lexical database

Examples

get_cohortsP("AA R K", c("AA R K", "AA R", "B AA B"), neighbors = "das")

Get embedding competitors

Description

Embedding competitors are items embedded in target

Usage

get_embeds_in_target(target, lexicon, sep = " ", form = FALSE, count = FALSE)

Arguments

Value

the indexes of the competitors in the lexical database

Examples

get_embeds_in_target("AA R K", c("AA R K", "AA R", "B AA B"))

Get embeds-in-target PRIME

Description

Items embedded in the target which are not cohorts or neighbors

Usage

get_embeds_in_targetP(
  target,
  lexicon,
  neighbors = "das",
  sep = " ",
  form = FALSE,
  count = FALSE
)

Arguments

Value

the indexes of the competitors in the lexical database

Examples

get_embeds_in_targetP("B AA R K IY", c("AA R K", "AA R", "AA R K IY", "B AA R"))

Get the log Frequency Weight (FW) of a competitor set

Description

Get the log Frequency Weight (FW) of a competitor set

Usage

get_fw(competitors_freq, pad = 0)

Arguments

Value

FW

Examples

get_fw(c(10, 50), pad = 1)

Get the log Frequency Weighted Competitor Probability (FWCP)

Description

Get the log Frequency Weighted Competitor Probability (FWCP)

Usage

get_fwcp(target_freq, competitors_freq, pad = 0, add_target = FALSE)

Arguments

Value

log FWCP

Examples

get_fwcp(100, c(10, 50), pad = 1)

Get competitors in spoken word recognition

Description

Get competitors in spoken word recognition

Usage

get_generic(target, lexicon, neighbors, sep, form, count, overlap, mismatch)

Arguments

Value

the indexes of the competitors in the lexical database

Get homophones

Description

Homophones are items which sound similar to the target

Usage

get_homoforms(target, lexicon, sep = " ", form = FALSE, count = FALSE)

Arguments

Value

the indexes of the competitors in the lexical database

Examples

get_homoforms("AA R K", c("AA R K", "AA R", "B AA B"))

Get phonological neighbors

Description

Phonological neighbors are items which can be converted to the target by one add, delete and substitute operation

Usage

get_neighbors(
  target,
  lexicon,
  neighbors = "das",
  sep = " ",
  form = FALSE,
  count = FALSE
)

Arguments

Value

the indexes of the competitors in the lexical database

Examples

get_neighbors("AA R K", c("AA R K", "AA R", "B AA B"), "d")
get_neighbors("AA R K", c("AA R K", "AA R", "B AA B"), "da")
get_neighbors("AA R K", c("AA R K", "AA R", "B AA B"), "das")

Get NeighborssPrime

Description

Neighbors which are not cohorts or rhymes

Usage

get_neighborsP(
  target,
  lexicon,
  neighbors = "das",
  sep = " ",
  form = FALSE,
  count = FALSE
)

Arguments

Value

the indexes of the competitors in the lexical database

Examples

get_neighborsP("AA R K", c("AA R K", "AA R", "B AA B"), neighbors = "das")

Get nohorts

Description

Items which are both cohorts and neighbors

Usage

get_nohorts(
  target,
  lexicon,
  neighbors = "das",
  sep = " ",
  form = FALSE,
  count = FALSE
)

Arguments

Value

the indexes of the competitors in the lexical database

Examples

get_nohorts("AA R K", c("AA R K", "AA R", "B AA B"), neighbors = "das")

Return lexical items the appropriate type

Description

Return lexical items the appropriate type

Usage

get_return(idx, lexicon, form, count)

Arguments

Value

the appropriate type

Get rhyme competitors

Description

Rhymes overlap in all except onset phoneme(s)

Usage

get_rhymes(
  target,
  lexicon,
  sep = " ",
  form = FALSE,
  count = FALSE,
  mismatch = 1
)

Arguments

Value

the indexes of the competitors in the lexical database

Examples

get_rhymes("AA R K", c("AA R K", "B AA R K", "B AA B"))

Get embedded competitors

Description

Embedded competitors are items which the target embedded in.

Usage

get_target_embeds_in(target, lexicon, sep = " ", form = FALSE, count = FALSE)

Arguments

Value

the indexes of the competitors in the lexical database

Examples

get_target_embeds_in("AA R K", c("AA R K", "B AA R K", "B AA B"))

Get target-embeds-in PRIME

Description

Items the target embeds into which are not cohorts or neighbors

Usage

get_target_embeds_inP(
  target,
  lexicon,
  neighbors = "das",
  sep = " ",
  form = FALSE,
  count = FALSE
)

Arguments

Value

the indexes of the competitors in the lexical database

Examples

get_target_embeds_inP("B AA R K", c("AA R K", "AA R", "B AA R K IY", "B AA R"))

Get phonological uniqueness point

Description

Phonological uniqueness point is the index at which the target becomes unique in the lexicon

Usage

get_uniqpt(target, lexicon, sep = " ")

Arguments

Value

Target is not unique: length + 1, else index where target becomes unique in lexicon

Examples

get_uniqpt("AA R K", c("AA R", "B AA B", "B AA R K"))

Lemmalex dictionary

Description

Lemmalex is primarily based on the SUBTLEXus subtitle corpus (based on American subtitles with 51 million items in total) reduced to lemma using a copyrighted database (Francis and Kučera, 1982). The pronunciation is given by CMU Pronouncing Dictionary

Usage

lemmalex

Format

An object of class tbl_df (inherits from tbl, data.frame) with 17750 rows and 3 columns.

Details

Reference: Brysbaert, M., & New, B. (2009). Moving beyond Kučera and Francis: A critical evaluation of current word frequency norms and the introduction of a new and improved word frequency measure for American English. Behavior research methods, 41(4), 977-990.

Kučera, H., & Francis, W. N. (1967). Computational analysis of present-day American English. Brown university press.

CMU Pronouncing Dictionary: http://www.speech.cs.cmu.edu/cgi-bin/cmudict

@format A table with 20,293 rows and 3 variables:

Item

SUBTLEXus dictionary reduced to lemmas

Frequency

Number of times the item appeared in the SUBTLEXus corpus

Pronunciation

ARPAbet transcription according to CMU

...

Source

https://www.ugent.be/pp/experimentele-psychologie/en/research/documents/subtlexus

slex ARPAbet

Description

TRACE slex lexicon translated by Nenadić and Tucker into ARPAbet pronunciation

Usage

slex

Format

An object of class data.table (inherits from data.frame) with 212 rows and 3 columns.

Details

TRACE slex lexicon with Frequencies: McClelland, J. L., & Elman, J. L. (1986). The TRACE model of speech perception. Cognitive psychology, 18(1), 1-86.

APRAbet transcription: Nenadić, F., & Tucker, B. V. (2020). Computational modelling of an auditory lexical decision experiment using jTRACE and TISK. Language, Cognition and Neuroscience, 1-29.

@format A table with 212 rows and 2 variables:

Item

TRACE slex transcription

Pronunciation

ARPAbet transcription

...

Source

https://era.library.ualberta.ca/items/61319cc6-436a-428c-b960-545bdc9bd5d3