Knowledge and regularity in planning

The field of planning has focused on several methods of using domain-specific knowledge. The three most common methods, use of search control, use of macro-operators, and analogy, are part of a continuum of techniques differing in the amount of reused plan information. This paper describes TALUS, a planner that exploits this continuum, and is used for comparing the relative utility of these methods. We present results showing how search control, macro-operators, and analogy are affected by domain regularity and the amount of stored knowledge

Unsupervised Named-Entity Recognition: Generating Gazetteers and Resolving Ambiguity

In this paper, we propose a named-entity recognition (NER) system that addresses two major limitations frequently discussed in the field. First, the system requires no human intervention such as manually labeling training data or creating gazetteers. Second, the system can handle more than the three classical named-entity types (person, location, and organization). We describe the system’s architecture and compare its performance with a supervised system. We experimentally evaluate the system on a standard corpus, with the three classical named-entity types, and also on a new corpus, with a new named-entity type (car brands)

Automatic Dream Sentiment Analysis

In this position paper, we propose a first step toward automatic analysis of sentiments in dreams. 100 dreams were sampled from a dream bank created for a normative study of dreams. Two human judges assigned a score to describe dream sentiments. We ran four baseline algorithms in an attempt to automate the rating of sentiments in dreams. Particularly, we compared the General Inquirer (GI) tool, the Linguistic Inquiry and Word Count (LIWC), a weighted version of the GI lexicon and of the HM lexicon and a standard bag-of-words. We show that machine learning allows automating the human judgment with accuracy superior to majority class choice

A Review of Global Sensitivity Analysis Methods and a comparative case study on Digit Classification

Global sensitivity analysis (GSA) aims to detect influential input factors that lead a model to arrive at a certain decision and is a significant approach for mitigating the computational burden of processing high dimensional data. In this paper, we provide a comprehensive review and a comparison on global sensitivity analysis methods. Additionally, we propose a methodology for evaluating the efficacy of these methods by conducting a case study on MNIST digit dataset. Our study goes through the underlying mechanism of widely used GSA methods and highlights their efficacy through a comprehensive methodology