Distributional similarity has been widely used to capture the semantic relatedness of words in many NLP tasks. However, various parameters such as similarity measures must be handtuned to make it work effectively. Instead, we propose a novel approach to synonym identiﬁcation based on supervised learning and distributional features, which correspond to the commonality of individual context types shared by word pairs. Considering the integration with pattern-based features, we have built and compared ﬁve synonym classiﬁers. ...
In this paper, we propose innovative representations for automatic classiﬁcation of verbs according to mainstream linguistic theories, namely VerbNet and FrameNet. First, syntactic and semantic structures capturing essential lexical and syntactic properties of verbs are deﬁned. Then, we design advanced similarity functions between such structures, i.e., semantic tree kernel functions, for exploiting distributional and grammatical information in Support Vector Machines.
Kersting, William H. “Distribution Systems” The Electric Power Engineering Handbook Ed. L.L. Grigsby Boca Raton: CRC Press LLC, 2001
William H. Kersting
New Mexico State University
6.1 Power System Loads Raymond R. Shoults and Larry D. Swift 6.2 Distribution System Modeling and Analysis William H. Kersting 6.3 Power System Operation and Control George L. Clark and Simon W. Bowe.6
We introduce cause identiﬁcation, a new problem involving classiﬁcation of incident reports in the aviation domain. Speciﬁcally, given a set of pre-deﬁned causes, a cause identiﬁcation system seeks to identify all and only those causes that can explain why the aviation incident described in a given report occurred. The difﬁculty of cause identiﬁcation stems in part from the fact that it is a multi-class, multilabel categorization task, and in part from the skewness of the class distributions and the scarcity of annotated reports. ...
This paper explores methods to alleviate the effect of lexical sparseness in the classiﬁcation of verbal arguments. We show how automatically generated selectional preferences are able to generalize and perform better than lexical features in a large dataset for semantic role classiﬁcation. The best results are obtained with a novel second-order distributional similarity measure, and the positive effect is specially relevant for out-of-domain data. Our ﬁndings suggest that selectional preferences have potential for improving a full system for Semantic Role Labeling. ...
We deﬁne a new feature selection score for text classiﬁcation based on the KL-divergence between the distribution of words in training documents and their classes. The score favors words that have a similar distribution in documents of the same class but different distributions in documents of different classes. Experiments on two standard data sets indicate that the new method outperforms mutual information, especially for smaller categories.
We present an automatic approach to determining whether a pronoun in text refers to a preceding noun phrase or is instead nonreferential. We extract the surrounding textual context of the pronoun and gather, from a large corpus, the distribution of words that occur within that context. We learn to reliably classify these distributions as representing either referential or non-referential pronoun instances. Despite its simplicity, experimental results on classifying the English pronoun it show the system achieves the highest performance yet attained on this important task. i...
In statistical language modeling, one technique to reduce the problematic eﬀects of data sparsity is to partition the vocabulary into equivalence classes. In this paper we investigate the eﬀects of applying such a technique to higherorder n-gram models trained on large corpora.
Stochastic uniﬁcation-based grammars (SUBGs) deﬁne exponential distributions over the parses generated by a uniﬁcationbased grammar (UBG). Existing algorithms for parsing and estimation require the enumeration of all of the parses of a string in order to determine the most likely one, or in order to calculate the statistics needed to estimate a grammar from a training corpus.
Following Link [14, 13] and Roberts , I present a semantic analysis of collective- distributivity comes from either an explicit quantifidistributive ambiguity, and resolution of such am- cational operator like each or an implicit distributive biguity by model-based reasoning. This approach operator called the D o p e r a t o r . The D operator goes beyond Scha and Stallard , whose reasoning was motivated by the equivalence in the semantics capability was limited to checking semantic types. of the following sentences.
This paper presents results from experiments in automatic classiﬁcation of animacy for Norwegian nouns using decision-tree classiﬁers. The method makes use of relative frequency measures for linguistically motivated morphosyntactic features extracted from an automatically annotated corpus of Norwegian. The classiﬁers are evaluated using leave-oneout training and testing and the initial results are promising (approaching 90% accuracy) for high frequency nouns, however deteriorate gradually as lower frequency nouns are classiﬁed.
Highway Bridge Loads and Load Distribution
6.1 6.2 6.3 Introduction Permanent Loads Vehicular Live Loads
Design Vehicular Live Load • Permit Vehicles • Fatigue Loads • Load Distribution for Superstructure Design • Load Distribution for Substructure Design • Multiple Presence of Live-Load Lanes • Dynamic Load Allowance • Horizontal Loads Due to Vehicular Trafﬁc
Susan E. Hida
California Department of Transportation
6.4 6.5 6.6 6.7
Pedestrian Loads Wind Loads Effects Due to Superimposed Deformations Exceptions to Code-Speciﬁed Design Loads
An Introduction to the Study of Mineralogy is a collection of papers that can be easily understood by a wide variety of readers, whether they wish to use it in their work, or simply to extend their knowledge. It is unique in that it presents a broad view of the mineralogy field. The book is intended for chemists, physicists, engineers, and the students of geology, geophysics, and soil science, but it will also be invaluable to the more advanced students of mineralogy who are looking for a concise revision guide....
3.1 Theory and Principles Harold Moore 3.2 Power Transformers H. Jin Sim and Scott H. Digby 3.3 Distribution Transformers Dudley L. Galloway 3.4 Underground Distribution Transformers Dan Mulkey 3.5 Dry Type Transformers Paulette A. Payne
3.6 Step-Voltage Regulators Craig A. Colopy 3.7 Reactors Richard Dudley, Antonio Castanheira, and Michael Sharp 3.8 Instrument Transformers Randy Mullikin and Anthony J. Jonnatti 3.9 Transformer Connections Dan D. Perco 3.10 LTC Control and Transformer Paralleling James H. Harlow
3.11 Loading Power Transformers Robert F. Tillman, Jr. 3.
4.1 Concept of Energy Transmission and Distribution 4.2 Transmission Line Structures Joe C. Pohlman George G. Karady 4.3 Insulators and Accessories George G. Karady and R.G. Farmer 4.4 Transmission Line Construction and Maintenance Wilford Caulkins and Kristine Buchholz 4.5 Insulated Power Cables for High-Voltage Applications Carlos V. Núñez-Noriega and Felimón Hernandez 4.6 Transmission Line Parameters Manuel Reta-Hernández 4.7 Sag and Tension of Conductor D.A. Douglass and Ridley Thrash
4.8 Corona and Noise Giao N. Trinh 4.
Toxic heavy metals, i.e. copper (II), lead (II) and cadmium (II), can be removed from water
by metallurgical solid wastes, i.e. bauxite waste red muds and coal fly ashes acting as sorbents. These
heavy-metal-loaded solid wastes may then be solidified by adding cement to a durable concrete mass
assuring their safe disposal. Thus, toxic metals in water have been removed by sorption on to inexpensive
solid waste materials as a preliminary operation of ultimate fixation. Metal uptake (sorption) and
release (desorption) have been investigated by thermostatic batch experiments.
2.1 Hydroelectric Power Generation Steven R. Brockschink, James H. Gurney, and Douglas B. Seely 2.2 Syncrhonous Machinery Paul I. Nippes 2.3 Thermal Generating Plants Kenneth H. Sebra 2.4 Distributed Utilities John R. Kennedy