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bibliography.bib
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@proceedings {23,
title = {Gender and Dialect Bias in YouTube{\textquoteright}s Automatic Captions },
journal = {Ethics in Natural Language Processing},
year = {2017},
publisher = {European Chapter of the Association for Computational Linguistics },
address = {Valencia, Spain},
author = {Tatman, R.}
}
@conference {25,
title = {{\textquotedblleft}He maybe did{\textquotedblright} or {\textquotedblleft}He may be dead{\textquotedblright}? The use of acoustic and social cues in applying perceptual learning of a new dialect},
journal = {173rd Meeting of the Acoustical Society of America},
year = {2017},
month = {06/2017},
address = {Boston, MA},
author = {R Tatman}
}
@proceedings {27,
title = {$\#$MAGA or $\#$TheResistance: Classifying Twitter users{\textquoteright} political affiliation without looking at their words or friends},
journal = {Women and Underrepresented Minorities in Natural Language Processing },
year = {2017},
author = {Rachael Tatman}
}
@proceedings {26,
title = {Non-lexical Features Encode Political Affiliation on Twitter},
journal = {Workshop on Natural Language Processing and Computational Social Science at ACL},
year = {2017},
abstract = {Previous work on classifying Twitter users{\textquoteright} political alignment has mainly focused on lexical and social network fea
tures. This study provides evidence that political affiliation is also reflected in features which have been previously over-looked: users{\textquoteright} discourse patterns (proportion of Tweets that are retweets or replies) and their rate of use of capitalization and punctuation. We find robust differences between politically left- and right-leaning communities with respect to these discourse and sub-lexical features, although they are not enough to train a high-accuracy classifier.},
author = {Rachael Tatman and Leo Stewart and Amandalynne Paullada and Emma Spiro}
}
@proceedings {22,
title = {{\textquotedblleft}Oh, I{\textquoteright}ve Heard That Before{\textquotedblright}: Modelling Own-Dialect Bias After Perceptual Learning by Weighting Training Data},
journal = {Workshop on Cognitive Modeling and Computational Linguistics},
year = {2017},
publisher = {European Chapter of the Association for Computational Linguistics },
address = {Valencia, Spain},
author = {Tatman, R.}
}
@conference {24,
title = {Social Identity and Punctuation Variation in the $\#$BlueLivesMatter and $\#$BlackLivesMatter Twitter Communities},
journal = {33rd Northwest Linguistics Conference},
year = {2017},
month = {05/2017},
keywords = {capitalization, microblogging, NLP, punctuation, social media, text, Twitter},
author = {Rachael Tatman and Amandalynne Paullada}
}
@article {21,
title = {{\textquoteleft}I{\textquoteright}m a spawts guay{\textquoteright}: Comparing the Use of Sociophonetic Variables in Speech andTwitter},
journal = {Selected Papers from NWAV 44},
year = {2016},
keywords = {microblogging, phonetics, Twitter},
author = {Tatman, R.}
}
@conference {20,
title = {Listening with American Ears: Using Social Information in Perceptual Learning},
journal = {3rd Conference on Experimental Approaches to Perception and Production of Language Variation},
year = {2016},
month = {09/2016},
keywords = {perceptual learning, sociophonetics},
author = {Rachael Tatman}
}
@conference {19,
title = {$\#$PronouncingThingsIncorrectly: Initial phonological generalizations of a novel Internet wordgame},
journal = {NorthWest Phonetics \& Phonology Conference},
year = {2016},
month = {05/2016},
abstract = {$\#$PronouncingThingsIncorrectly is a language game popularized by Chaz Smith on Vine, a micro video-blogging platform. These mis-pronunciations show a number of interesting phonological processes. While these processes are not categorical{\textemdash}they are often disregarded in favor of humorous homophony, such as the pronunciation of "pop secret" as "poop secrete"{\textemdash} they are robust, productive and are an intriguing new source of phonological data.},
keywords = {microblogging},
doi = {10.13140/RG.2.1.5012.8245},
author = {Rachael Tatman}
}
@article {17,
title = {Speaker {Dialect} is a {Necessary} {Feature} to {Model} {Perceptual} {Accent} {Adaptation} in {Humans}},
journal = {4th Pacific Northwest Regional NLP Workshop: NW-NLP 2016},
year = {2016},
abstract = {In accent adaptation{\textendash}adjusting existing ASR to recognize novel accents{\textendash}systems commonly make use of dialect labels. This project models parallel experimental behavioral data, where human listeners were trained to categorize speech sounds from a novel dialect. Explicitly including dialect information in the model allowed the classifier to better simulate the behavioral results.},
author = {Rachael Tatman}
}
@article {18,
title = {We Who Tweet: Pronominal Relative Clauses on Twitter},
journal = {Corpus Linguistics Fest 2016},
year = {2016},
abstract = {Pronominal relative clauses were previously reported to be unproductive in English, appearing only in Bible verses and proverbs. This corpus study of Twitter data shows that pronominal relative clauses are a productive part of contemporary English, and can be used in both literary and nonliterary registers.
},
keywords = {microblogging},
author = {Kirby Conrod and Rachael Tatman and Rik Koncel-Kedziorski}
}
@conference {15,
title = {Comparing the Use of Sociophonetic Variables in Speech and Twitter},
journal = {New Ways of Analyzing Variation (NWAV) 44},
year = {2015},
month = {10/2015},
address = {Toronto, Ontario},
keywords = {linguistics, microblogging, phonetics, sociolinguistics, Twitter},
author = {Rachael Tatman}
}
@article {16,
title = {The cross-linguistic distribution of sign language parameters},
journal = {Proceedings of theForty-first Annual Meeting of The Berkeley Linguistics Society},
volume = {41},
year = {2015},
chapter = {503},
keywords = {markedness, parameters, sign language},
author = {Rachael Tatman}
}
@conference {Tatman2015Berkeley,
title = {The cross-linguistic distribution of sign language parameters},
journal = {Berkeley Linguistics Society},
number = {41},
year = {2015},
month = {February},
keywords = {phonology, sign language, typology},
author = {Rachael Tatman}
}
@article {14,
title = {$\#$go awn: Sociophonetic Variation in Variant Spellings on Twitter},
journal = {Working Papers of the Linguistics Circle of the University of Victoria },
volume = {25},
year = {2015},
month = {09/2015},
chapter = {98},
abstract = {While there is a long history of investigating sociophonetic
variation in speech, it has been less studied in computer mediated
communication contexts such as Twitter. The most obvious reason
for this is that interactions in Twitter are text-based and therefore
do not include acoustic information. Twitter users are, however,
encoding sociophonetic information through their use of variant
spellings, such as {\textquotedblleft}awn{\textquotedblright} for {\textquotedblleft}on{\textquotedblright}. This study provides evidence
that Twitter users in multiple dialect regions are using variant
spellings to encode sociophonetic variation in a systematic way
and that these variant spelling are sensitive to style shifting. The
methodology used here may be used in future studies to determine
the salience of sociophonetic variables.
Keywords: sociolinguistics; phonetics; social media; Twitter},
keywords = {microblogging, phonetics, social media, sociolinguistics, Twitter},
author = {Rachael Tatman}
}
@conference {Tatman2015,
title = {go awn: Sociophonetic Variation in Variant Spellings on Twitter},
journal = {Northwest Linguistic Conference},
year = {2015},
abstract = {Background: Variation in speech and variant spellings in writing (e.g. {\textquotedblleft}becawse{\textquotedblright}, {\textquotedblleft}go awn{\textquotedblright}) play similar social roles (Sebba 2003, Paolillo 2001). This study extends that parallel by showing that variant spellings are used a systematic way that mirrors variation in speech. Methodology: Tweets were selected that showed a distinction between /ɑ/and /ɔ/{\textendash} which is found in African American English and the Southern American English (Labov, Ash \& Boburg 2005). The Twitter public API was used to extract recent tweets that used an {\textquotedblleft}aw{\textquotedblright} spelling of one of the six most frequent /ɔ/ words in English. The tweets were then hand-sorted to remove tweets where the target word occurred as an acronym, name, non-English term, typo or URL. The resulting 74 tweets were hand-coded for the number and type of variant spellings found. Results: Half of the tweets (37/74) included more than one variant spelling. The most commonly encoded variables were th-stopping (19 instances), g-dropping (12 instances), r-lessness (10 instances), cluster reduction (8 instances), and /ai/ monophthongization (4 instances). An example is shown in (1). (1) hype hayed foah dat becawse it was 8 bucks foah 2 yeahs and w da jets i like readin about da prospects ogay (JPG 2015) {\textquotedblleft}I paid for that because it was eight bucks for two years and with the Jets I like reading about the prospects, okay?{\textquotedblright} While Twitter does not collect ethnographic information and geographic data was only available for one tweet (which was from Louisiana), this collection of features is consistent with those observed in African American English (Cutler 1999, Rickford \& Labov 1999) and{\textemdash}with some exceptions{\textemdash}Southern American English (Labov, Ash \& Boburg 2005). This suggests that Twitter users who use variant spellings do so in a systematic way that reflects patterns observed in speech.},
keywords = {corpus linguistics, microblogging, phonetics, phonology, social media, sociolinguistics},
author = {Rachael Tatman}
}
@conference {13,
title = {Hand Choice Lateralization as Phonologization of Sign Language Pronouns},
journal = {Workshop on Computational Phonology \& Morphology },
year = {2015},
month = {07/2015},
author = {Rachael Tatman}
}
@article {Souza2015jslhr,
title = {Individual sensitivity to spectral and temporal cues in listeners with hearing impairment},
journal = {Journal of Speech, Language, and Hearing Research},
year = {2015},
abstract = {Purpose: The present study was designed to evaluate use of spectral and temporal cues, under conditions where both types of cues were available. Method: Participants included adults with normal hearing and hearing loss. We focused on three categories of speech cues: static spectral (spectral shape); dynamic spectral (formant change); and temporal (amplitude envelope). Spectral and/or temporal dimensions of synthetic speech were systematically manipulated along a continuum and recognition was measured using the manipulated stimuli. Level was controlled to ensure cue audibility. Discriminant function analysis was used to determine to what degree spectral and temporal information contributed to the identification of each stimulus. Results: Listeners with normal hearing were influenced to a greater extent by spectral cues for all stimuli. Listeners with hearing impairment generally utilized spectral cues when the information was static (spectral shape) and but used temporal cues when the information was dynamic (formant transition). The relative use of spectral and temporal dimensions varied among individuals, especially among listeners with hearing loss. Conclusions: Information about spectral and temporal cue use may aid in identifying listeners who rely to a greater extent on particular acoustic cues, and to apply that information toward therapeutic interventions.},
keywords = {cues, phonetics, statistical modelling},
doi = {10.1044/2015_JSLHR-H-14-0138},
author = {Souza, P and Wright, R and Blackburn, M and Tatman, R. and Gallun, F.}
}
@article {TatmansubmittedSLAY,
title = {The Sign Language Analyses (SLAY) Database},
journal = {University of Washington Working Papers in Linguistics},
year = {2015},
keywords = {database, sign language},
author = {Rachael Tatman}
}
@conference {12,
title = {The State of the Stats: Current Use of Statistical Methods Across Linguistics Subfields},
journal = {Linguistics Summer Institute},
year = {2015},
author = {Rachael Tatman}
}
@conference {Tatman2014slay,
title = {The SLAY Database: A Meta-Analytic Database of Sign Language Grammars},
journal = {Workshop on Databases and Corpora in Linguistics},
year = {2014},
month = {October},
keywords = {database, sign language, typology},
author = {Rachael Tatman}
}