title: Evolving practice - The environment court of New Zealand

authors: Daya-Winterbottom, Trevor

abstract: Practice before the Environment Court is evolving. Recent innovations have included enhanced support for Judges through the appointment of case managers and hearings managers, digital recording of evidence, provision for Commissioner only hearings, the introduction of case management, increased use of alternative dispute resolution, and the introduction of a code of conduct for expert witnesses. Discussions about the circumstances in which evidence may be taken as read continue, and the recent debate on the review of the Resource Management Act 1991 ("RMA") posed difficult questions about judicial deference to Local Authority decisions. Most recently the first judgment of the Supreme Court on the RMA has posed serious questions about the independence of expert witness in certain situations. How these issues are resolved will have an impact on the role of expert witnesses before the Environment Court. They will be examined against the background of the reforms enacted by Parliament in the Resource Management Amendment Act 2005 ("RMAA 2005").

text: Evolving practice - The environment court of New Zealand Practice before the Environment Court is evolving. Recent innovations have included enhanced support for Judges through the appointment of case managers and hearings managers, digital recording of evidence, provision for Commissioner only hearings, the introduction of case management, increased use of alternative dispute resolution, and the introduction of a code of conduct for expert witnesses. Discussions about the circumstances in which evidence may be taken as read continue, and the recent debate on the review of the Resource Management Act 1991 ("RMA") posed difficult questions about judicial deference to Local Authority decisions. Most recently the first judgment of the Supreme Court on the RMA has posed serious questions about the independence of expert witness in certain situations. How these issues are resolved will have an impact on the role of expert witnesses before the Environment Court. They will be examined against the background of the reforms enacted by Parliament in the Resource Management Amendment Act 2005 ("RMAA 2005").

year: 2005

title: Building a Twitter opinion lexicon from automatically-annotated tweets

authors: Bravo-Marquez, Felipe

subjects: Science & Technology

abstract: Opinion lexicons, which are lists of terms labelled by sentiment, are widely used resources to support automatic sentiment analysis of textual passages. However, existing resources of this type exhibit some limitations when applied to social media messages such as tweets (posts in Twitter), because they are unable to capture the diversity of informal expressions commonly found in this type of media. 
In this article, we present a method that combines information from automatically annotated tweets and existing hand-made opinion lexicons to expand an opinion lexicon in a supervised fashion. The expanded lexicon contains part-of-speech (POS) disambiguated entries with a probability distribution for positive, negative, and neutral polarity classes, similarly to SentiWordNet. 
To obtain this distribution using machine learning, we propose word-level attributes based on (a) the morphological information conveyed by POS tags and (b) associations between words and the sentiment expressed in the tweets that contain them. We consider tweets with both hard and soft sentiment labels. The sentiment associations are modelled in two different ways: using point-wise-mutual-information semantic orientation (PMI-SO), and using stochastic gradient descent semantic orientation (SGD-SO), which learns a linear relationship between words and sentiment. The training dataset is labelled by a seed lexicon formed by combining multiple hand-annotated lexicons. 
Our experimental results show that our method outperforms the three-dimensional word-level polarity classification performance obtained by using PMI-SO alone. This is significant because PMI-SO is a state-of-the-art measure for establishing world-level sentiment. Additionally, we show that lexicons created with our method achieve signifi- cant improvements over SentiWordNet for classifying tweets into polarity classes, and also outperform SentiStrength in the majority of the experiments.

text: Building a Twitter opinion lexicon from automatically-annotated tweets Opinion lexicons, which are lists of terms labelled by sentiment, are widely used resources to support automatic sentiment analysis of textual passages. However, existing resources of this type exhibit some limitations when applied to social media messages such as tweets (posts in Twitter), because they are unable to capture the diversity of informal expressions commonly found in this type of media. 
In this article, we present a method that combines information from automatically annotated tweets and existing hand-made opinion lexicons to expand an opinion lexicon in a supervised fashion. The expanded lexicon contains part-of-speech (POS) disambiguated entries with a probability distribution for positive, negative, and neutral polarity classes, similarly to SentiWordNet. 
To obtain this distribution using machine learning, we propose word-level attributes based on (a) the morphological information conveyed by POS tags and (b) associations between words and the sentiment expressed in the tweets that contain them. We consider tweets with both hard and soft sentiment labels. The sentiment associations are modelled in two different ways: using point-wise-mutual-information semantic orientation (PMI-SO), and using stochastic gradient descent semantic orientation (SGD-SO), which learns a linear relationship between words and sentiment. The training dataset is labelled by a seed lexicon formed by combining multiple hand-annotated lexicons. 
Our experimental results show that our method outperforms the three-dimensional word-level polarity classification performance obtained by using PMI-SO alone. This is significant because PMI-SO is a state-of-the-art measure for establishing world-level sentiment. Additionally, we show that lexicons created with our method achieve signifi- cant improvements over SentiWordNet for classifying tweets into polarity classes, and also outperform SentiStrength in the majority of the experiments.

year: 2016-09-15

title: E Tama Hikairo

authors: Maxwell, Te Kahautu

abstract: E Tama Hikairo was composed to welcome Kīngi Tūeheitia's the Māori Kings first grandchild that was born in September 2014. Kīngi Tūheitia had Te Haahi Ringatū tūā (Christen) Hikairo. This composition talks about the royal genealogy of Hikairo that links him to all the iwi of the land and his special place in Te Haahi Ringatū. This item placed first overall in the choral section. This item was performed by Ōpōtiki Mai Tawhiti at Te Matatini the World Championship Kapa Haka Championship.

text: E Tama Hikairo E Tama Hikairo was composed to welcome Kīngi Tūeheitia's the Māori Kings first grandchild that was born in September 2014. Kīngi Tūheitia had Te Haahi Ringatū tūā (Christen) Hikairo. This composition talks about the royal genealogy of Hikairo that links him to all the iwi of the land and his special place in Te Haahi Ringatū. This item placed first overall in the choral section. This item was performed by Ōpōtiki Mai Tawhiti at Te Matatini the World Championship Kapa Haka Championship.

year: 2015-01-22