title: Do industry training reforms meet the needs of a medium sized electrical manufacturer? A case study

authors: Piercy, Gemma Louise

subjects: Industry Training Act 1992

abstract: The Industry Training Act 1992 introduced significant changes to vocational education and training in New Zealand. Based on a case study of a Waikato firm, this paper investigates whether the changes resulting from the Industry Training Act 1992 have enabled a medium-sized electrical manufacturing firm to meet its training needs. The paper also examines questions relating to wider issues in regard to vocational education and training in New Zealand. Specifically, it discusses the issue of voluntarism, as it relates to the finding of Industrial Training Organisations (ITOs), the creation of qualifications, and the proposed removal of developmental funding that is presently provided through the Education and Training Support Agency (ETSA).

text: Do industry training reforms meet the needs of a medium sized electrical manufacturer? A case study The Industry Training Act 1992 introduced significant changes to vocational education and training in New Zealand. Based on a case study of a Waikato firm, this paper investigates whether the changes resulting from the Industry Training Act 1992 have enabled a medium-sized electrical manufacturing firm to meet its training needs. The paper also examines questions relating to wider issues in regard to vocational education and training in New Zealand. Specifically, it discusses the issue of voluntarism, as it relates to the finding of Industrial Training Organisations (ITOs), the creation of qualifications, and the proposed removal of developmental funding that is presently provided through the Education and Training Support Agency (ETSA).

year: 1998

title: Contextualised approaches to embedding word senses

authors: Ansell, Alan John

subjects: Natural Language Processing

abstract: Vector representations of text are an essential tool for modern Natural Language Processing (NLP), and there has been much work devoted to finding effective methods for obtaining such representations. Most previously proposed methods derive vector representations for individual words, known as word embeddings. While word embeddings have enabled considerable advances in NLP, they have a significant theoretical drawback: many words have several, often completely unrelated meanings; it seems dubious to conflate these multiple meanings into a single point in semantic space.

This drawback has inspired an alternative, "multi-sense" approach to representing words. In this approach, rather than learning a single vector for each word, multiple vectors, or "sense embeddings," are learned corresponding to the individual meanings of the word. While this approach has not in general surpassed the word embedding approach, it has proved beneficial for a number of tasks such as word similarity estimation and word sense induction.

One of the most significant recent advances in NLP has been the development of "contextualised" word embedding models. Whereas word embeddings model the semantic properties of words in isolation, contextualised models represent the meanings of words in context. This enables them to capture some of the vast array of linguistic phenomena that occur above the word level.

I propose a number of new methods for learning sense embeddings which exploit contextualised techniques, based on the underlying hypothesis that the probability of a word occurring in a given context is equal to the sum of the probabilities of its individual senses occurring in the context. I first validate this hypothesis by using it to derive a simple method for learning sense embeddings inspired by the Skip-gram model. I then present a method for extracting sense embeddings from a contextualised word embedding model. Finally I propose an end-to-end model for learning sense embeddings, and show that it comprehensively outperforms previous sense embedding models on the task of word sense induction, a standard task for evaluation of such models. To demonstrate the model's flexibility I apply it to some other word-sense related tasks with good results.

text: Contextualised approaches to embedding word senses Vector representations of text are an essential tool for modern Natural Language Processing (NLP), and there has been much work devoted to finding effective methods for obtaining such representations. Most previously proposed methods derive vector representations for individual words, known as word embeddings. While word embeddings have enabled considerable advances in NLP, they have a significant theoretical drawback: many words have several, often completely unrelated meanings; it seems dubious to conflate these multiple meanings into a single point in semantic space.

This drawback has inspired an alternative, "multi-sense" approach to representing words. In this approach, rather than learning a single vector for each word, multiple vectors, or "sense embeddings," are learned corresponding to the individual meanings of the word. While this approach has not in general surpassed the word embedding approach, it has proved beneficial for a number of tasks such as word similarity estimation and word sense induction.

One of the most significant recent advances in NLP has been the development of "contextualised" word embedding models. Whereas word embeddings model the semantic properties of words in isolation, contextualised models represent the meanings of words in context. This enables them to capture some of the vast array of linguistic phenomena that occur above the word level.

I propose a number of new methods for learning sense embeddings which exploit contextualised techniques, based on the underlying hypothesis that the probability of a word occurring in a given context is equal to the sum of the probabilities of its individual senses occurring in the context. I first validate this hypothesis by using it to derive a simple method for learning sense embeddings inspired by the Skip-gram model. I then present a method for extracting sense embeddings from a contextualised word embedding model. Finally I propose an end-to-end model for learning sense embeddings, and show that it comprehensively outperforms previous sense embedding models on the task of word sense induction, a standard task for evaluation of such models. To demonstrate the model's flexibility I apply it to some other word-sense related tasks with good results.

year: 2020

title: A study of iwi communication between Te Tau Ihu iwi and intergenerational diasporic whānau

authors: Wright, Karamea Moana

subjects: diaspora

abstract: This study looks at the online communication methods used between Te Tau Ihu iwi and intergenerational uri who live in the diaspora. Preferences and perceptions of methods of communication utilized by both iwi, and tāngata who live in the overseas diaspora were explored. Most important are the implications for uri living away from Aotearoa, and their desire to remain connected to iwi in New Zealand. This is a mixed methods study, comprising of a survey of fifty-eight emigrant respondents living abroad, and interviews with one member of the first, second, and third generations of emigrants to the United States, with affiliation to at least one of the eight tribes who claim tāngata whenua status of Te Tau Ihu.

The implications of the findings from the study provide both iwi and tāngata the opportunity to create a synergistic relationship of communicating. Assisting iwi to develop and implement a strategy for relevant communication, and iwi members engaging in a dialogue with iwi about relevance of communication is critical in strengthening ties between iwi and future generations regardless of location. “He aha te mea nui, he tangata, he tangata.”

text: A study of iwi communication between Te Tau Ihu iwi and intergenerational diasporic whānau This study looks at the online communication methods used between Te Tau Ihu iwi and intergenerational uri who live in the diaspora. Preferences and perceptions of methods of communication utilized by both iwi, and tāngata who live in the overseas diaspora were explored. Most important are the implications for uri living away from Aotearoa, and their desire to remain connected to iwi in New Zealand. This is a mixed methods study, comprising of a survey of fifty-eight emigrant respondents living abroad, and interviews with one member of the first, second, and third generations of emigrants to the United States, with affiliation to at least one of the eight tribes who claim tāngata whenua status of Te Tau Ihu.

The implications of the findings from the study provide both iwi and tāngata the opportunity to create a synergistic relationship of communicating. Assisting iwi to develop and implement a strategy for relevant communication, and iwi members engaging in a dialogue with iwi about relevance of communication is critical in strengthening ties between iwi and future generations regardless of location. “He aha te mea nui, he tangata, he tangata.”

year: 2019