Comparative Experiments on Disambiguating Word Senses: An Illustration of the Role of Bias in Machine Learning

This paper describes an experimental comparison of seven different learning algorithms on the problem of learning to disambiguate the meaning of a word from context. The algorithms tested include statistical, neural-network, decision-tree, rule-based, and case-based classification techniques. The specific problem tested involves disambiguating six senses of the word ``line'' using the words in the current and proceeding sentence as context. The statistical and neural-network methods perform the best on this particular problem and we discuss a potential reason for this observed difference. We also discuss the role of bias in machine learning and its importance in explaining performance differences observed on specific problems.Comment: 10 page

Content-Based Book Recommending Using Learning for Text Categorization

Recommender systems improve access to relevant products and information by making personalized suggestions based on previous examples of a user's likes and dislikes. Most existing recommender systems use social filtering methods that base recommendations on other users' preferences. By contrast, content-based methods use information about an item itself to make suggestions. This approach has the advantage of being able to recommended previously unrated items to users with unique interests and to provide explanations for its recommendations. We describe a content-based book recommending system that utilizes information extraction and a machine-learning algorithm for text categorization. Initial experimental results demonstrate that this approach can produce accurate recommendations.Comment: 8 pages, 3 figures, Submission to Fourth ACM Conference on Digital Librarie

Learning Parse and Translation Decisions From Examples With Rich Context

We present a knowledge and context-based system for parsing and translating natural language and evaluate it on sentences from the Wall Street Journal. Applying machine learning techniques, the system uses parse action examples acquired under supervision to generate a deterministic shift-reduce parser in the form of a decision structure. It relies heavily on context, as encoded in features which describe the morphological, syntactic, semantic and other aspects of a given parse state.Comment: 8 pages, LaTeX, 3 postscript figures, uses aclap.st

Learning a Policy for Opportunistic Active Learning

Active learning identifies data points to label that are expected to be the most useful in improving a supervised model. Opportunistic active learning incorporates active learning into interactive tasks that constrain possible queries during interactions. Prior work has shown that opportunistic active learning can be used to improve grounding of natural language descriptions in an interactive object retrieval task. In this work, we use reinforcement learning for such an object retrieval task, to learn a policy that effectively trades off task completion with model improvement that would benefit future tasks.Comment: EMNLP 2018 Camera Read

Regenerative Grazing and the Benefits of Livestock on Soils in Northern New South Wales

Conventional cattle grazing has received criticism for environmental degradation in the past. Regenerative grazing and the principles of regenerative agriculture show encouraging signs that proper livestock management and planned grazing can reverse degradation and mitigate climate change. An emphasis on soil health and increasing soil carbon and organic matter levels reveals positive feedback for environmental health, the economic security of farmers, and nutritional health of consumers. In this study I looked to investigate the benefits of regenerative agriculture, reasons why it is being practiced, and the extent it is practiced within the grazing in comparison to traditional methods within Northern New South Wales. In times of climate unpredictability, struggling economic conditions of small farmers, and declining nutrient value in foods, regenerative grazing and agriculture is an alternative strategy to pursue in resolving all of these. In order to gather data to support the claims, I spent 145 hours sending out an electronic questionnaire (gathering 16 responses), consulting background literature, visiting 7 farmers’ properties, conducting both formal (1) and informal interviews (6), and attending one workshop. I found that in the Northern NSW area grazers are implementing a variety of regenerative strategies within their paddocks, that have resulted in improvements in both health and productivity of their grazing enterprises, closer ties to their community, and it is a movement deserving of more converts. Yet, the extent of regenerative grazing in the area is variable, with conventional enterprises still holding dominance in numbers. I argue that with these results, regenerative grazing is a dramatically better strategy and system to employ opposed to the current state of contemporary, conventional grazing. With regenerative grazing: soil health is improved, paddocks are more resilient to climate variability, a more nutrient dense food supply is produced, water retention increases, GHGs are sequestered, livestock received a happy amount of feed, dependence on chemical inputs is reduced, beneficial microbial life is brought back to the rhizosphere, and biodiversity improves in the form of native plants and animals. Results reflect that RAg grazers also feel more in-touch with their local communities and economics. Thus, I contest that RAg grazing is a sustainable enterprise as it meets the triple-bottom lines of sustainability with the economy, environment, and social components