title: Analysing A/O possession in Māori-language tweets

authors: Trye, David

subjects: A/O categories

abstract: This article contributes the first corpus-based study of possession in Māori, the indigenous language of Aotearoa New Zealand. Like most Polynesian languages, Māori has a dual possessive system involving a choice between the so-called A and O categories. While Māori grammars describe these categories in terms of the inherent semantic relationship between the possessum and possessor, there have been no large-scale corpus analyses demonstrating their use in natural contexts. Social media provide invaluable opportunities for such linguistic studies, capturing contemporary language use while alleviating the burden of gathering data through traditional means. We operationalise semantic distinctions to investigate possession in Māori-language tweets, focusing on the [possessum a/o possessor] construction (e.g., te tīmatanga o te wiki ‘the beginning of the week’). In our corpus comprising 2500 tweets produced by more than 200 individuals, we find that users leverage a wide array of noun types encompassing many different semantic relationships. We observe not only the expected predominance of the O category, but also a tendency for examples described by Māori grammars as A-marked to instead be O-marked (59%). Although the A category persists in the corpus, our findings suggest that language change could be underway. Our primary dataset can be explored interactively online.

text: Analysing A/O possession in Māori-language tweets This article contributes the first corpus-based study of possession in Māori, the indigenous language of Aotearoa New Zealand. Like most Polynesian languages, Māori has a dual possessive system involving a choice between the so-called A and O categories. While Māori grammars describe these categories in terms of the inherent semantic relationship between the possessum and possessor, there have been no large-scale corpus analyses demonstrating their use in natural contexts. Social media provide invaluable opportunities for such linguistic studies, capturing contemporary language use while alleviating the burden of gathering data through traditional means. We operationalise semantic distinctions to investigate possession in Māori-language tweets, focusing on the [possessum a/o possessor] construction (e.g., te tīmatanga o te wiki ‘the beginning of the week’). In our corpus comprising 2500 tweets produced by more than 200 individuals, we find that users leverage a wide array of noun types encompassing many different semantic relationships. We observe not only the expected predominance of the O category, but also a tendency for examples described by Māori grammars as A-marked to instead be O-marked (59%). Although the A category persists in the corpus, our findings suggest that language change could be underway. Our primary dataset can be explored interactively online.

year: 2024

title: Ya got ta know when ta hold ‘em: Maori women and gambling

authors: Morrison, Laurie Elena

subjects: Maori

abstract: Gambling among Maori women is under-researched. In this study, I interviewed thirty Maori
women to investigate how they got involved in gambling, what maintained their gambling and
what they thought might help to moderate their gambling. I found that the whanau was central
to understanding these issues. As children, my participants were exposed to gambling within
their whanau. As adults, whanau and other social support relationships were an integral part of
their gambling, which most commonly occurred in the context of card schools and housie. A
sense of reciprocity was important in both forms of gambling. Card schools were reported to be
close-knit groups within which the money circulated, giving all a chance to win. By playing
housie, the women felt that they were contributing to the welfare of their marae. Through the
social bonds of gambling and the acquisition of skills, gambling contributed to these women’s
sense of identity. On the other hand, financial and relationship difficulties were identified as
negative consequences of gambling. The women felt there was a need for Maori-focused
services for problem gambling.

text: Ya got ta know when ta hold ‘em: Maori women and gambling Gambling among Maori women is under-researched. In this study, I interviewed thirty Maori
women to investigate how they got involved in gambling, what maintained their gambling and
what they thought might help to moderate their gambling. I found that the whanau was central
to understanding these issues. As children, my participants were exposed to gambling within
their whanau. As adults, whanau and other social support relationships were an integral part of
their gambling, which most commonly occurred in the context of card schools and housie. A
sense of reciprocity was important in both forms of gambling. Card schools were reported to be
close-knit groups within which the money circulated, giving all a chance to win. By playing
housie, the women felt that they were contributing to the welfare of their marae. Through the
social bonds of gambling and the acquisition of skills, gambling contributed to these women’s
sense of identity. On the other hand, financial and relationship difficulties were identified as
negative consequences of gambling. The women felt there was a need for Maori-focused
services for problem gambling.

year: 1999

title: Vocal Detection: An evaluation between general versus focused models

authors: Tsai, Yi-Na

subjects: vocal detection

abstract: This thesis focuses on presenting a technique on improving current vocal detection methods. One of the most popular methods employs some type of statistical approach where vocal signals can be distinguished automatically by first training a model on both vocal and non-vocal example data, then using this model to classify audio signals into vocals or non-vocals. There is one problem with this method which is that the model that has been trained is typically very general and does its best at classifying various different types of data. Since the audio signals containing vocals that we care about are songs, we propose to improve vocal detection accuracies by creating focused models targeted at predicting vocal segments according to song artist and artist gender. Such useful information like artist name are often overlooked, this restricts opportunities in processing songs more specific to its type and hinders its potential success. Experiment results with several models built according to artist and artist gender reveal improvements of up to 17% when compared to using the general approach. With such improvements, applications such as automatic lyric synchronization to vocal segments in real-time may become more achievable with greater accuracy.

text: Vocal Detection: An evaluation between general versus focused models This thesis focuses on presenting a technique on improving current vocal detection methods. One of the most popular methods employs some type of statistical approach where vocal signals can be distinguished automatically by first training a model on both vocal and non-vocal example data, then using this model to classify audio signals into vocals or non-vocals. There is one problem with this method which is that the model that has been trained is typically very general and does its best at classifying various different types of data. Since the audio signals containing vocals that we care about are songs, we propose to improve vocal detection accuracies by creating focused models targeted at predicting vocal segments according to song artist and artist gender. Such useful information like artist name are often overlooked, this restricts opportunities in processing songs more specific to its type and hinders its potential success. Experiment results with several models built according to artist and artist gender reveal improvements of up to 17% when compared to using the general approach. With such improvements, applications such as automatic lyric synchronization to vocal segments in real-time may become more achievable with greater accuracy.

year: 2011