This paper describes an experimental comparison of three unsupervised learning algorithms that distinguish the sense of an ambiguous word in untagged text. The methods described in this paper, McQuitty's similarity analysis, Ward's minimum-variance method, and the EM algorithm, assign each instance of an ambiguous word to a known sense definition based solely on the values of automatically identifiable features in text. These methods and feature sets are found to be more successful in disambiguating nouns rather than adjectives or verbs. Overall, the most accurate of these procedures is McQuitty's similarity analysis in combination with a high dimensional feature set.
A more recent bootstrapping approach is described in @cite_23 . This algorithm requires a small number of training examples to serve as a seed. There are a variety of options discussed for automatically selecting seeds; one is to identify collocations that uniquely distinguish between senses. For plant , the collocations manufacturing plant and living plant make such a distinction. Based on 106 examples of manufacturing plant and 82 examples of living plant this algorithm is able to distinguish between two senses of plant for 7,350 examples with 97 percent accuracy. Experiments with 11 other words using collocation seeds result in an average accuracy of 96 percent.
@cite_23
101201821 1514969207 2101664494 2765516633
We present a fully automatic approach to construct a large-scale, high-precision dataset from noisy web images. Within the entire pipeline, we focus on generating high quality seed images for subsequent dataset growing. High quality seeds are essential as we revealed, but they have received relatively less attention in previous works with respect to how to automatically generate them. In this work, we propose a density score based on rank-order distance to identify positive seed images. The basic idea is images relevant to a concept typically are tightly clustered, while the outliers are widely scattered. Through adaptive thresholding, we guarantee the selected seeds as numerous and accurate as possible. Starting with the high quality seeds, we grow a high quality dataset by dividing seeds and conducting iterative negative and positive mining. Our system can automatically collect thousands of images for one concept class, with a precision rate of 95 or more. Comparisons with recent state-of-the-arts also demonstrate our method’s superior performance. Open-class semantic lexicon induction is of great interest for current knowledge harvesting algorithms. We propose a general framework that uses patterns in bootstrapping fashion to learn open-class semantic lexicons for different kinds of relations. These patterns require seeds. To estimate the goodness (the potential yield) of new seeds, we introduce a regression model that considers the connectivity behavior of the seed during bootstrapping. The generalized regression model is evaluated on six different kinds of relations with over 10000 different seeds for English and Spanish patterns. Our approach reaches robust performance of 90 correlation coefficient with 15 error rate for any of the patterns when predicting the goodness of seeds. Large-scale comparison of genomic DNA is of fundamental importance in annotating functional elements of genomes. To perform large comparisons efficiently, BLAST (Methods: Companion Methods Enzymol 266 (1996) 460, J. Mol. Biol. 215 (1990) 403, Nucleic Acids Res. 25(17) (1997) 3389) and other widely used tools use seeded alignment, which compares only sequences that can be shown to share a common pattern or ''seed'' of matching bases. The literature suggests that the choice of seed substantially affects the sensitivity of seeded alignment, but designing and evaluating seeds is computationally challenging. This work addresses the problem of designing a seed to optimize performance of seeded alignment. We give a fast, simple algorithm based on finite automata for evaluating the sensitivity of a seed in a Markov model of ungapped alignments, along with extensions to mixtures and inhomogeneous Markov models. We give intuition and theoretical results on which seeds are good choices. Finally, we describe Mandala, a software tool for seed design, and show that it can be used to improve the sensitivity of alignment in practice. Recently, a body of computer vision research has studied the task of high-throughput plant phenotyping (measurement of plant attributes). The goal is to more rapidly and more accurately estimate plant properties as compared to conventional manual methods. In this work, we develop a method to measure two primary yield attributes of interest; stalk count and stalk width that are important for many broad-acre annual crops (sorghum, sugarcane, corn, maize for example). Prior work of using convolutional deep neural networks for plant analysis has either focused on object detection or dense image segmentation. In our work, we develop a novel pipeline that accurately extracts both detected object regions and dense semantic segmentation for extracting both stalk counts and stalk width. A ground-robot called the Robotanist is used to deploy a high-resolution stereo imager to capture dense image data of experimental plots of Sorghum plants. We ground-truth validate data extracted using two humans who assess the traits independently and we compare both accuracy and efficiency of human versus robotic measurements. Our method yields R-squared correlation of 0.88 for stalk count and a mean absolute error of 2.77 mm where average stalk width is 14.354 mm. Our approach is 30 times faster for stalk count and 270 times faster for stalk width measurement.