A Comparative Study of the Application of Different Learning Techniques to Natural Language Interfaces

In this paper we present first results from a comparative study. Its aim is to test the feasibility of different inductive learning techniques to perform the automatic acquisition of linguistic knowledge within a natural language database interface. In our interface architecture the machine learning module replaces an elaborate semantic analysis component. The learning module learns the correct mapping of a user's input to the corresponding database command based on a collection of past input data. We use an existing interface to a production planning and control system as evaluation and compare the results achieved by different instance-based and model-based learning algorithms.Comment: 10 pages, to appear CoNLL9

EVALUATION OF THE 2006/7 AGRICULTURAL INPUT SUBSIDY PROGRAMME, MALAWI. FINAL REPORT

This report evaluates the 2006/7 Malawi Government Agricultural Input Subsidy Programme (AISP). The main objective of the evaluation is to assess the impact and implementation of the AISP in order to provide lessons for future interventions in growth and social protection. The evaluation combined qualitative and quantitative methods of data collection and analysis. Quantitative data were collected through a national survey in 2007 of 2,491 households who were previously interviewed in the 2004/05 Integrated Household Survey, a survey of retail shops selling inputs in six districts and data on stocks and sales from manufacturers, large-scale importers and dealers of fertilizers and seeds. The quantitative data was triangulated by qualitative data from focus group discussions with smallholder farmers in 12 districts, and key informant interviews with government staff, input distributors and beneficiary and non-beneficiary households. The analysis is based on descriptive statistics, econometric modelling and livelihood and rural economy modelling. An Interim Report in March 2007 provides fuller details of the implementation of the programme.Agribusiness, Agricultural and Food Policy, Community/Rural/Urban Development, Food Consumption/Nutrition/Food Safety, Food Security and Poverty, Productivity Analysis,

Determining the Success of NCAA Basketball Teams through Team Characteristics

Every year much of the nation becomes engulfed in the NCAA basketball postseason tournament more affectionately known as “March Madness.” The tournament has received the name because of the ability for any team to win a single game and advance to the next round. The purpose of this study is to determine whether concrete statistical measures can be used to predict the final outcome of the tournament. The data collected in the study include 13 independent variables ranging from the 2003-2004 season up until the current 2009-2010 season. Different tests were run in an attempt to achieve the most accurate predictive model. First, the data were input into Excel and ordinary least squares regressions were run for each year. Then the data were compiled into one file and an ordinary least squares regression was run on that collection of data in Excel. Next, the data were input into Minitab and a stepwise regression was run in order to keep only the significant independent variables. Following that, a regression analysis was run in Minitab. The coefficients from that regression analysis were input into a file with the 2009-2010 data in an attempt to test the model’s results against the actual results. All of the models developed, except one for the year 2005-2006, were determined to be significant. There were 6 significant independent variables determined. The final results showed that although the model developed through the study was significant, the ability to accurately predict the outcomes is very difficult

Local sensitivity analysis for compositional data with application to soil texture in hydrologic modelling

Compositional data, such as soil texture, are hard to deal with in the geosciences as standard statistical methods are often inappropriate to analyse this type of data. Especially in sensitivity analysis, the closed character of the data is often ignored. To that end, we developed a method to assess the local sensitivity of a model output with resect to a compositional model input. We adapted the finite difference technique such that the different parts of the input are perturbed simultaneously while the closed character of the data is preserved. This method was applied to a hydrologic model and the sensitivity of the simulated soil moisture content to local changes in soil texture was assessed. Based on a high number of model runs, in which the soil texture was varied across the entire texture triangle, we identified zones of high sensitivity in the texture triangle. In such zones, the model output uncertainty induced by the discrepancy between the scale of measurement and the scale of model application, is advised to be reduced through additional data collection. Furthermore, the sensitivity analysis provided more insight into the hydrologic model behaviour as it revealed how the model sensitivity is related to the shape of the soil moisture retention curve

Methodologies for data collection and model documentation in computer simulation

In recent years, computer simulation has become a mainstream decision support tool in an industry. In order to maximise the benefits of using simulation within businesses, simulation models should be designed, developed and deployed in a shorter time span. A number of factors, such as excessive model details, inefficient data collection, lengthy model documentation and poorly planned experiments, increase the overall lead-time of simulation projects. Among these factors, input data modeling and model documentation are seen as major obstacles. Input data identification, collection, validation and analysis typically take more than one-third of project time. This paper presents an IDEF (Integrated computer-aided manufacturing DEFinition) based approach to accelerate identification and collection of input data. A functional module library and a reference data model, both developed using the IDEF family of constructs, are the core elements of the methodology. In addition, this paper also intends to give a methodological approach that helps and motivates the project team to document simulation projects.N/

ACCESS 1: Approximation Concepts Code for Efficient Structural Synthesis program documentation and user's guide

The program documentation and user's guide for the ACCESS-1 computer program is presented. ACCESS-1 is a research oriented program which implements a collection of approximation concepts to achieve excellent efficiency in structural synthesis. The finite element method is used for structural analysis and general mathematical programming algorithms are applied in the design optimization procedure. Implementation of the computer program, preparation of input data and basic program structure are described, and three illustrative examples are given

A Tidy Data Model for Natural Language Processing Using CleanNLP

Recent advances in natural language processing have produced libraries that extract low level features from a collection of raw texts. These features, known as annotations, are usually stored internally in hierarchical, tree-based data structures. This paper proposes a data model to represent annotations as a collection of normalized relational data tables optimized for exploratory data analysis and predictive modeling. The R package cleanNLP, which calls one of two state of the art NLP libraries (CoreNLP or spaCy), is presented as an implementation of this data model. It takes raw text as an input and returns a list of normalized tables. Specific annotations provided include tokenization, part of speech tagging, named entity recognition, sentiment analysis, dependency parsing,coreference resolution, and word embeddings. The package currently supports input text in English, German, French, and Spanish