Disambiguating Nouns, Verbs, and Adjectives Using Automatically Acquired Selectional Preferences

Selectional preferences have been used by word sense disambiguation (WSD) systems as one source of disambiguating information. We evaluate WSD using selectional preferences acquired for English adjective—noun, subject, and direct object grammatical relationships with respect to a standard test corpus. The selectional preferences are specific to verb or adjective classes, rather than individual word forms, so they can be used to disambiguate the co-occurring adjectives and verbs, rather than just the nominal argument heads. We also investigate use of the one-senseper-discourse heuristic to propagate a sense tag for a word to other occurrences of the same word within the current document in order to increase coverage. Although the preferences perform well in comparison with other unsupervised WSD systems on the same corpus, the results show that for many applications, further knowledge sources would be required to achieve an adequate level of accuracy and coverage. In addition to quantifying performance, we analyze the results to investigate the situations in which the selectional preferences achieve the best precision and in which the one-sense-per-discourse heuristic increases performance

Finding predominant word senses in untagged text

In word sense disambiguation (WSD), the heuristic of choosing the most common sense is extremely powerful because the distribution of the senses of a word is often skewed. The problem with using the predominant, or first sense heuristic, aside from the fact that it does not take surrounding context into account, is that it assumes some quantity of handtagged data. Whilst there are a few hand-tagged corpora available for some languages, one would expect the frequency distribution of the senses of words, particularly topical words, to depend on the genre and domain of the text under consideration. We present work on the use of a thesaurus acquired from raw textual corpora and the WordNet similarity package to find predominant noun senses automatically. The acquired predominant senses give a precision of 64% on the nouns of the SENSEVAL- 2 English all-words task. This is a very promising result given that our method does not require any hand-tagged text, such as SemCor. Furthermore, we demonstrate that our method discovers appropriate predominant senses for words from two domainspecific corpora

Evaluating Automatic Approaches for Word Meaning Discovery and Disambiguation using Lexical Substitution. Abstract

Proceedings of the 16th Nordic Conference of Computational Linguistics NODALIDA-2007. Editors: Joakim Nivre, Heiki-Jaan Kaalep, Kadri Muischnek and Mare Koit. University of Tartu, Tartu, 2007. ISBN 978-9985-4-0513-0 (online) ISBN 978-9985-4-0514-7 (CD-ROM) p. 2

Customary land tenure issues in Australia

"The debate concerning wealth creation on communally owned indigenous land is gaining momentum in Australia. This Australian debate has implications for Papua New Guinea, given the level of influence that Australia has on Papua New Guinea through its aid program and the long-standing colonial relationship between the two countries. Given the possibility that Australian commentaries on Australian indigenous land dealings will have some bearing on Papua New Guinea land issues, this chapter aims to describe for Papua New Guineans the context in which the Australian debate occurs, the legal framework defining indigenous lands, and the ways in which indigenous land is being developed." - page 8

NAU is abuzz about career

In 2013, NAU Career Development implemented a robust 3 year assessment strategy. Year One, showcased at the 2015 Assessment Fair, focused on assessing the Reach of our program. In Year Two, we continued to assess Reach and added measures of Perception, including satisfaction surveys targeted towards students, employers, and campus partners, as well as interviews with faculty. Assessing outcomes of both quantitative and qualitative data analyses, our results demonstrated something exciting….NAU is Abuzz about Career! The busy bees at NAU Career Development are having an impact, resulting in prepared students, engaged partners, and satisfied employers. Our data is reviewed throughout the year, and continues to inform future priorities and next steps. Come see what the buzz is about

Measuring Context-Word Biases in Lexical Semantic Datasets

State-of-the-art contextualized models eg. BERT use tasks such as WiC and WSD to evaluate their word-in-context representations. This inherently assumes that performance in these tasks reflect how well a model represents the coupled word and context semantics. We question this assumption by presenting the first quantitative analysis on the context-word interaction required and being tested in major contextual lexical semantic tasks, taking into account that tasks can be inherently biased and models can learn spurious correlations from datasets. To achieve this, we run probing baselines on masked input, based on which we then propose measures to calculate the degree of context or word biases in a dataset, and plot existing datasets on a continuum. The analysis were performed on both models and humans to decouple biases inherent to the tasks and biases learned from the datasets. We found that, (1) to models, most existing datasets fall into the extreme ends of the continuum: the retrieval-based tasks and especially the ones in the medical domain (eg. COMETA) exhibit strong target word bias while WiC-style tasks and WSD show strong context bias; (2) AM2iCo and Sense Retrieval show less extreme model biases and challenge a model more to represent both the context and target words. (3) A similar trend of biases exists in humans but humans are much less biased compared with models as humans found semantic judgments more difficult with the masked input, indicating models are learning spurious correlations. This study demonstrates that with heavy context or target word biases, models are usually not being tested for word-in-context representations as such in these tasks and results are therefore open to misinterpretation. We recommend our framework as a sanity check for context and target word biases in future task design and model interpretation in lexical semantics

Noise minimisation in gene expression switches

Gene expression is subject to stochastic variation which leads to fluctuations in the rate of protein production. Recently, a study in yeast at a genomic scale showed that, in some cases, gene expression variability alters phenotypes while, in other cases, these remain unchanged despite fluctuations in the expression of other genes. These studies suggested that noise in gene expression is a physiologically relevant trait and, to prevent harmful stochastic variation in the expression levels of some genes, it can be subject to minimisation. However, the mechanisms for noise minimisation are still unclear. In the present work, we analysed how noise expression depends on the architecture of the cis-regulatory system, in particular on the number of regulatory binding sites. Using analytical calculations and stochastic simulations, we found that the fluctuation level in noise expression decreased with the number of regulatory sites when regulatory transcription factors interacted with only one other bound transcription factor. In contrast, we observed that there was an optimal number of binding sites when transcription factors interacted with many bound transcription factors. This finding suggested a new mechanism for preventing large fluctuations in the expression of genes which are sensitive to the concentration of regulators.Instituto de Física La PlataFacultad de Ciencias Exacta

Ibuprofen Ameliorates Fatigue- And Depressive-Like Behavior in Tumor-Bearing Mice

Aims: Cancer-related fatigue (CRF) is often accompanied by depressed mood, both of which reduce functional status and quality of life. Research suggests that increased expression of pro-inflammatory cytokines is associated with skeletal muscle wasting and depressive- and fatigue-like behaviors in rodents and cancer patients. We have previously shown that treatment with ibuprofen, a nonsteroidal anti-inflammatory drug, preserved muscle mass in tumor-bearing mice. Therefore, the purpose of the present study was to determine the behavioral effects of ibuprofen in a mouse model of CRF. Main methods: Mice were injected with colon-26 adenocarcinoma cells and treated with ibuprofen (10 mg/kg) in the drinking water. Depressive-like behavior was determined using the forced swim test (FST). Fatigue-like behaviors were determined using voluntary wheel running activity (VWRA) and grip strength. The hippocampus, gastrocnemius muscle, and serum were collected for cytokine analysis. Key findings: Tumor-bearing mice showed depressive-like behavior in the FST, which was not observed in mice treated with ibuprofen. VWRA and grip strength declined in tumor-bearing mice, and ibuprofen attenuated this decline. Tumor-bearing mice had decreased gastrocnemius muscle mass and increased expression of IL-6, MAFBx and MuRF mRNA, biomarkers of protein degradation, in the muscle. Expression of IL-1β and IL-6 was also increased in the hippocampus. Treatment with ibuprofen improved muscle mass and reduced cytokine expression in both the muscle and hippocampus of tumor-bearing mice. Significance: Ibuprofen treatment reduced skeletal muscle wasting, inflammation in the brain, and fatigue- and depressive-like behavior in tumor-bearing mice. Therefore, ibuprofen warrants evaluation as an adjuvant treatment for CRF

Fluoxetine Prevents the Development of Depressive-like Behavior in a Mouse Model of Cancer Related Fatigue

Cancer patients frequently suffer from fatigue, a complex syndrome associated with tiredness and depressed mood. Cancer-related fatigue (CRF) can be present at the time of diagnosis, escalates during treatment, and can persist for years after treatment. CRF negatively influences quality of life, limits functional independence, and is associated with decreased survival in patients with incurable disease. We have previously shown that increased pro-inflammatory cytokine expression in the brain contributes to depressive- and fatigue-like behaviors in a mouse model of CRF. Inflammatory cytokines increase the activity of indoleamine 2,3-dioxygenase (IDO) and kynurenine 3-monooxygenase (KMO), which competitively reduce serotonin synthesis. Reduced serotonin availability in the brain and increased production of alternative neuroactive metabolites of tryptophan are thought to contribute to the development of depression and fatigue. The purpose of this study was to determine the effects of fluoxetine, a selective serotonin reuptake inhibitor (SSRI), on brain cytokines and behavioral measures of fatigue and depression in tumor-bearing mice. Here we show that tumor growth increased brain expression of pro-inflammatory cytokines and KMO. Treatment with fluoxetine had no effect on tumor growth, muscle wasting, fatigue behavior, or cytokine expression in the brain. Fluoxetine, however, reduced depressive-like behaviors in tumor bearing mice. In conclusion, our data confirm that increased brain expression of pro-inflammatory cytokines is associated with tumor-induced fatigue- and depressive-like behaviors. However, it is possible to separate the effects of tumor growth on mood and fatigue-like behaviors using SSRIs such as fluoxetine