title: Token identification using HMM and PPM models

authors: Wen, Yingying

subjects: computer science

abstract: Hidden markov models (HMMs) and prediction by partial matching models (PPM) have been successfully used in language processing tasks including learning-based token identification. Most of the existing systems are domain- and language-dependent. The power of retargetability and applicability of these systems is limited. This paper investigates the effect of the combination of HMMs and PPM on token identification. We implement a system that bridges the two well known methods through words new to the identification model. The system is fully domain- and language-independent. No changes of code are necessary when applying to other domains or languages. The only required input of the system is an annotated corpus. The system has been tested on two corpora and achieved an overall F-measure of 69.02% for TCC, and 76.59% for BIB. Although the performance is not as good as that obtained from a system with language-dependent components, our proposed system has power to deal with large scope of domain- and language-independent problem. Identification of date has the best result, 73% and 92% of correct tokens are identified for two corpora respectively. The system also performs reasonably well on people s name with correct tokens of 68% for TCC, and 76% for BIB.

text: Token identification using HMM and PPM models Hidden markov models (HMMs) and prediction by partial matching models (PPM) have been successfully used in language processing tasks including learning-based token identification. Most of the existing systems are domain- and language-dependent. The power of retargetability and applicability of these systems is limited. This paper investigates the effect of the combination of HMMs and PPM on token identification. We implement a system that bridges the two well known methods through words new to the identification model. The system is fully domain- and language-independent. No changes of code are necessary when applying to other domains or languages. The only required input of the system is an annotated corpus. The system has been tested on two corpora and achieved an overall F-measure of 69.02% for TCC, and 76.59% for BIB. Although the performance is not as good as that obtained from a system with language-dependent components, our proposed system has power to deal with large scope of domain- and language-independent problem. Identification of date has the best result, 73% and 92% of correct tokens are identified for two corpora respectively. The system also performs reasonably well on people s name with correct tokens of 68% for TCC, and 76% for BIB.

year: 2003

title: Radical research honesty in a post-truth society

authors: Twidale, Michael B.

subjects: computer science

abstract: It seems that in much current research, big truths have to be protected by a bodyguard of little white lies, and a phalanx of unreported inconvenient truths. What would happen if we told the truth about how we really do research? Let’s see...

text: Radical research honesty in a post-truth society It seems that in much current research, big truths have to be protected by a bodyguard of little white lies, and a phalanx of unreported inconvenient truths. What would happen if we told the truth about how we really do research? Let’s see...

year: 2019

title: “It is about enabling Tino Rangatiratanga and Mana Motuhake”: An analysis of submissions on the Pae Ora (Healthy Futures) Bill 2021 endorsing a Māori health authority

authors: Edmonds, Moengaroa

subjects: Health Equity

abstract: Indigenous leadership in healthcare is one way for Indigenous peoples to exercise sovereignty over their health. In Aotearoa New Zealand, the establishment of the Māori Health Authority (MHA) was grounded in a decades-long imperative to address Māori health inequities and operationalise te Tiriti o Waitangi within the health system. However, the populist National-led coalition government formed in November 2023 included the abolishment of the MHA in their first 100-day plan and eventually disestablished it in February 2024. This study analysed 155 public group submissions on the Pae Ora (Healthy Futures) Bill made in 2021 and representing health professionals, iwi, hapū, and community groups. Core themes endorsing the MHA as a statutory entity included honouring te Tiriti, advocating for Māori-led solutions to health inequities, decolonising health systems, and affirming Indigenous rights. The political decision to remove the MHA is antithetical to the explicit endorsement statements made by an overwhelming majority of group submitters with expertise in health organisational structures and healthcare provision. Our study has implications for future local and international research in documenting counter-narratives for Indigenous struggles in health systems.

text: “It is about enabling Tino Rangatiratanga and Mana Motuhake”: An analysis of submissions on the Pae Ora (Healthy Futures) Bill 2021 endorsing a Māori health authority Indigenous leadership in healthcare is one way for Indigenous peoples to exercise sovereignty over their health. In Aotearoa New Zealand, the establishment of the Māori Health Authority (MHA) was grounded in a decades-long imperative to address Māori health inequities and operationalise te Tiriti o Waitangi within the health system. However, the populist National-led coalition government formed in November 2023 included the abolishment of the MHA in their first 100-day plan and eventually disestablished it in February 2024. This study analysed 155 public group submissions on the Pae Ora (Healthy Futures) Bill made in 2021 and representing health professionals, iwi, hapū, and community groups. Core themes endorsing the MHA as a statutory entity included honouring te Tiriti, advocating for Māori-led solutions to health inequities, decolonising health systems, and affirming Indigenous rights. The political decision to remove the MHA is antithetical to the explicit endorsement statements made by an overwhelming majority of group submitters with expertise in health organisational structures and healthcare provision. Our study has implications for future local and international research in documenting counter-narratives for Indigenous struggles in health systems.

year: 2025