title: Review: Marine natural products

authors: Blunt, John W.

subjects: South China Sea

abstract: This review covers the literature published in 2011 for marine natural products, with 870 citations (558 for the period January to December 2011) referring to compounds isolated from marine microorganisms and phytoplankton, green, brown and red algae, sponges, cnidarians, bryozoans, molluscs, tunicates, echinoderms, mangroves and other intertidal plants and microorganisms. The emphasis is on new compounds (1152 for 2011), together with the relevant biological activities, source organisms and country of origin. Biosynthetic studies, first syntheses, and syntheses that lead to the revision of structures or stereochemistries, have been included.

text: Review: Marine natural products This review covers the literature published in 2011 for marine natural products, with 870 citations (558 for the period January to December 2011) referring to compounds isolated from marine microorganisms and phytoplankton, green, brown and red algae, sponges, cnidarians, bryozoans, molluscs, tunicates, echinoderms, mangroves and other intertidal plants and microorganisms. The emphasis is on new compounds (1152 for 2011), together with the relevant biological activities, source organisms and country of origin. Biosynthetic studies, first syntheses, and syntheses that lead to the revision of structures or stereochemistries, have been included.

year: 2013

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: District plan implementation under the RMA: Confessions of a resource consent

authors: Day, Maxine

subjects: New Zealand

abstract: This report focuses on results from Phase 2 of PUCM - the quality of plan
implementation in six district councils selected for their range of plan quality and
capacity to plan. Only those results considered to be important for assisting the six
councils (and others) to improve implementation of their plans are included in this
report. The findings and recommendations, both specific and general, ought to be
instructive for other councils, thereby helping to improve their plans and
implementation processes. Since hapu/iwi interests formed a key component of the
research, the outcomes will help enhance their case for better consideration of their
interests when dealing with local government. As well, many of the findings and
recommendations relate to matters of governance and capacity building that require
Government action, which until done will make it difficult for councils to achieve
quality plans and implementation processes.

text: District plan implementation under the RMA: Confessions of a resource consent This report focuses on results from Phase 2 of PUCM - the quality of plan
implementation in six district councils selected for their range of plan quality and
capacity to plan. Only those results considered to be important for assisting the six
councils (and others) to improve implementation of their plans are included in this
report. The findings and recommendations, both specific and general, ought to be
instructive for other councils, thereby helping to improve their plans and
implementation processes. Since hapu/iwi interests formed a key component of the
research, the outcomes will help enhance their case for better consideration of their
interests when dealing with local government. As well, many of the findings and
recommendations relate to matters of governance and capacity building that require
Government action, which until done will make it difficult for councils to achieve
quality plans and implementation processes.

year: 2003-04-15