Unsupervised Learning of Semantic Orientation from a Hundred-Billion-Word Corpus

The evaluative character of a word is called its semantic orientation. A positive semantic orientation implies desirability (e.g., "honest", "intrepid") and a negative semantic orientation implies undesirability (e.g., "disturbing", "superfluous"). This paper introduces a simple algorithm for unsupervised learning of semantic orientation from extremely large corpora. The method involves issuing queries to a Web search engine and using pointwise mutual information to analyse the results. The algorithm is empirically evaluated using a training corpus of approximately one hundred billion words — the subset of the Web that is indexed by the chosen search engine. Tested with 3,596 words (1,614 positive and 1,982 negative), the algorithm attains an accuracy of 80%. The 3,596 test words include adjectives, adverbs, nouns, and verbs. The accuracy is comparable with the results achieved by Hatzivassiloglou and McKeown (1997), using a complex four-stage supervised learning algorithm that is restricted to determining the semantic orientation of adjectives

Directional Decision Lists

In this paper we introduce a novel family of decision lists consisting of highly interpretable models which can be learned efficiently in a greedy manner. The defining property is that all rules are oriented in the same direction. Particular examples of this family are decision lists with monotonically decreasing (or increasing) probabilities. On simulated data we empirically confirm that the proposed model family is easier to train than general decision lists. We exemplify the practical usability of our approach by identifying problem symptoms in a manufacturing process.Comment: IEEE Big Data for Advanced Manufacturin

Supplementary skills guides for built environment researchers

Deepening specialised knowledge-base and wider skills of researchers in a wider variety of disciplines are prerequisite for developing successful leadership in higher education, the public sector and industry. In response to this repeated calls for enhancing supplementary skills of the built environment researchers, TG53 (Postgraduate Research Training in Building and Construction) initiated steps to develop and nurture understanding of supplementary skills and providing a common frame of reference for use and further discourse and has developed 6 good practice examples highlighting skills for researchers within the built environment. Accordingly, this TG53 publication is in response to the repeated calls for enhancing supplementary skills of the built environment researchers

Modeling Dynamic Swarms

This paper proposes the problem of modeling video sequences of dynamic swarms (DS). We define DS as a large layout of stochastically repetitive spatial configurations of dynamic objects (swarm elements) whose motions exhibit local spatiotemporal interdependency and stationarity, i.e., the motions are similar in any small spatiotemporal neighborhood. Examples of DS abound in nature, e.g., herds of animals and flocks of birds. To capture the local spatiotemporal properties of the DS, we present a probabilistic model that learns both the spatial layout of swarm elements and their joint dynamics that are modeled as linear transformations. To this end, a spatiotemporal neighborhood is associated with each swarm element, in which local stationarity is enforced both spatially and temporally. We assume that the prior on the swarm dynamics is distributed according to an MRF in both space and time. Embedding this model in a MAP framework, we iterate between learning the spatial layout of the swarm and its dynamics. We learn the swarm transformations using ICM, which iterates between estimating these transformations and updating their distribution in the spatiotemporal neighborhoods. We demonstrate the validity of our method by conducting experiments on real video sequences. Real sequences of birds, geese, robot swarms, and pedestrians evaluate the applicability of our model to real world data.Comment: 11 pages, 17 figures, conference paper, computer visio

Changing police culture: raising awareness of the importance of mental health

Master's Project (M.A.) University of Alaska Fairbanks, 2017The suicide rate involving police officers has produced alarming statistics for decades. Until recently, however, little has been done to prevent suicide in law enforcement and even fewer efforts have been made to change the root of the problem. This paper reviews why a law enforcement officer may choose to take their life, looks at preexisting programs and resources that departments can choose to embrace, and supplies departments with a new approach to destigmatizing suicide within the police culture starting at the academy level