Abstract

As stated by Bank (2014 :73), “determining the number of signs” in a
corpus is a “non-trivial task” given that “signers may deviate from
citation forms (by articulating one-handed signs as two-handed and
vice versa), use their non-dominant hands as a buoy, or articulate two
one-handed signs simultaneously”. We propose a semi-automatized
method to establish sign frequency that specifically addresses these
difficulties. This method is replicable for any annotation dataset that
draws on the principles of ID-glossing (Johnston 2016), has two
independent annotation tiers for the hands and does not segment the
buoys as separate annotations (Crasborn et al. 2015).
The main steps are (1) the extraction of the “annotation overlaps
information” from ELAN to Excel, (2) the enrichment of the Excel file
with (a) a unique tag for each annotation and with (b) information
about the handedness of the signs (one-handed and two-handed
signs), (3) the classification of the annotations in three articulatory
categories (one-handed articulations, two-handed articulations,
complex articulations), (4) the frequency count based on the crossing
between handedness information and articulatory pattern information.
The table below shows the ten most frequent signs that were
extracted from the annotations of the Corpus LSFB (Meurant 2015)
with this method.
It allows (1) to tackle the difficulties pointed by Bank (2014) and thus
reduce the sources of noise to the minimum; (2) to revise prior
information about handedness based on usage data (Johnston 2016);
(3) to avoid manual annotation of one-handed and two-handed
variants (Johnston 2016).
Original languageEnglish
Publication statusPublished - 30 Jul 2018
EventFirst International Workshop on Cognitive And Functional Explorations in Sign Language Linguistics - University of Birmingham, Birmingham, United Kingdom
Duration: 30 Jul 201831 Jul 2018
https://www.birmingham.ac.uk/schools/edacs/departments/englishlanguage/events/2018/sign-cafe.aspx

Conference

ConferenceFirst International Workshop on Cognitive And Functional Explorations in Sign Language Linguistics
Abbreviated titleSign CAFÉ 1
CountryUnited Kingdom
CityBirmingham
Period30/07/1831/07/18
Internet address

Cite this

Paligot, A., Gobert, M., & Meurant, L. (2018). A method to establish sign frequency based on patterns of articulation. Poster session presented at First International Workshop on Cognitive And Functional Explorations in Sign Language Linguistics, Birmingham, United Kingdom.
Paligot, Aurore ; Gobert, Maxime ; Meurant, Laurence. / A method to establish sign frequency based on patterns of articulation. Poster session presented at First International Workshop on Cognitive And Functional Explorations in Sign Language Linguistics, Birmingham, United Kingdom.
@conference{2d1782c0e0e441bf82f2bca51f61d05d,
title = "A method to establish sign frequency based on patterns of articulation",
abstract = "As stated by Bank (2014 :73), “determining the number of signs” in acorpus is a “non-trivial task” given that “signers may deviate fromcitation forms (by articulating one-handed signs as two-handed andvice versa), use their non-dominant hands as a buoy, or articulate twoone-handed signs simultaneously”. We propose a semi-automatizedmethod to establish sign frequency that specifically addresses thesedifficulties. This method is replicable for any annotation dataset thatdraws on the principles of ID-glossing (Johnston 2016), has twoindependent annotation tiers for the hands and does not segment thebuoys as separate annotations (Crasborn et al. 2015).The main steps are (1) the extraction of the “annotation overlapsinformation” from ELAN to Excel, (2) the enrichment of the Excel filewith (a) a unique tag for each annotation and with (b) informationabout the handedness of the signs (one-handed and two-handedsigns), (3) the classification of the annotations in three articulatorycategories (one-handed articulations, two-handed articulations,complex articulations), (4) the frequency count based on the crossingbetween handedness information and articulatory pattern information.The table below shows the ten most frequent signs that wereextracted from the annotations of the Corpus LSFB (Meurant 2015)with this method.It allows (1) to tackle the difficulties pointed by Bank (2014) and thusreduce the sources of noise to the minimum; (2) to revise priorinformation about handedness based on usage data (Johnston 2016);(3) to avoid manual annotation of one-handed and two-handedvariants (Johnston 2016).",
author = "Aurore Paligot and Maxime Gobert and Laurence Meurant",
year = "2018",
month = "7",
day = "30",
language = "English",
note = "null ; Conference date: 30-07-2018 Through 31-07-2018",
url = "https://www.birmingham.ac.uk/schools/edacs/departments/englishlanguage/events/2018/sign-cafe.aspx",

}

Paligot, A, Gobert, M & Meurant, L 2018, 'A method to establish sign frequency based on patterns of articulation', First International Workshop on Cognitive And Functional Explorations in Sign Language Linguistics, Birmingham, United Kingdom, 30/07/18 - 31/07/18.

A method to establish sign frequency based on patterns of articulation. / Paligot, Aurore; Gobert, Maxime; Meurant, Laurence.

2018. Poster session presented at First International Workshop on Cognitive And Functional Explorations in Sign Language Linguistics, Birmingham, United Kingdom.

Research output: Contribution to conferencePoster

TY - CONF

T1 - A method to establish sign frequency based on patterns of articulation

AU - Paligot, Aurore

AU - Gobert, Maxime

AU - Meurant, Laurence

PY - 2018/7/30

Y1 - 2018/7/30

N2 - As stated by Bank (2014 :73), “determining the number of signs” in acorpus is a “non-trivial task” given that “signers may deviate fromcitation forms (by articulating one-handed signs as two-handed andvice versa), use their non-dominant hands as a buoy, or articulate twoone-handed signs simultaneously”. We propose a semi-automatizedmethod to establish sign frequency that specifically addresses thesedifficulties. This method is replicable for any annotation dataset thatdraws on the principles of ID-glossing (Johnston 2016), has twoindependent annotation tiers for the hands and does not segment thebuoys as separate annotations (Crasborn et al. 2015).The main steps are (1) the extraction of the “annotation overlapsinformation” from ELAN to Excel, (2) the enrichment of the Excel filewith (a) a unique tag for each annotation and with (b) informationabout the handedness of the signs (one-handed and two-handedsigns), (3) the classification of the annotations in three articulatorycategories (one-handed articulations, two-handed articulations,complex articulations), (4) the frequency count based on the crossingbetween handedness information and articulatory pattern information.The table below shows the ten most frequent signs that wereextracted from the annotations of the Corpus LSFB (Meurant 2015)with this method.It allows (1) to tackle the difficulties pointed by Bank (2014) and thusreduce the sources of noise to the minimum; (2) to revise priorinformation about handedness based on usage data (Johnston 2016);(3) to avoid manual annotation of one-handed and two-handedvariants (Johnston 2016).

AB - As stated by Bank (2014 :73), “determining the number of signs” in acorpus is a “non-trivial task” given that “signers may deviate fromcitation forms (by articulating one-handed signs as two-handed andvice versa), use their non-dominant hands as a buoy, or articulate twoone-handed signs simultaneously”. We propose a semi-automatizedmethod to establish sign frequency that specifically addresses thesedifficulties. This method is replicable for any annotation dataset thatdraws on the principles of ID-glossing (Johnston 2016), has twoindependent annotation tiers for the hands and does not segment thebuoys as separate annotations (Crasborn et al. 2015).The main steps are (1) the extraction of the “annotation overlapsinformation” from ELAN to Excel, (2) the enrichment of the Excel filewith (a) a unique tag for each annotation and with (b) informationabout the handedness of the signs (one-handed and two-handedsigns), (3) the classification of the annotations in three articulatorycategories (one-handed articulations, two-handed articulations,complex articulations), (4) the frequency count based on the crossingbetween handedness information and articulatory pattern information.The table below shows the ten most frequent signs that wereextracted from the annotations of the Corpus LSFB (Meurant 2015)with this method.It allows (1) to tackle the difficulties pointed by Bank (2014) and thusreduce the sources of noise to the minimum; (2) to revise priorinformation about handedness based on usage data (Johnston 2016);(3) to avoid manual annotation of one-handed and two-handedvariants (Johnston 2016).

M3 - Poster

ER -

Paligot A, Gobert M, Meurant L. A method to establish sign frequency based on patterns of articulation. 2018. Poster session presented at First International Workshop on Cognitive And Functional Explorations in Sign Language Linguistics, Birmingham, United Kingdom.