# Simple maths for keywords

Save this PDF as:

Size: px
Start display at page:

## Transcription

1 Simple maths for keywords Adam Kilgarriff Lexical Computing Ltd Abstract We present a simple method for identifying keywords of one corpus vs. another. There is no one-sizefits-all list, but different lists according to the frequency range the user is interested in. The method includes a variable which allows the user to focus on higher or lower frequency words. This word is twice as common here as there. Such observations are entirely central to corpus linguistics. We very often want to know which words are distinctive of one corpus, or text type, versus another. The simplest way to make the comparison is expressed in my opening sentence. Twice as common means the word s frequency (per thousand words, or million words) in the one corpus is twice its frequency in the other. We count occurrences in each corpus, divide each number by the number of words in that corpus, optionally multiply by 1,000 or 1,000,000 to give frequencies per thousand or million, and divide the one number by the other to give a ratio. (Since the thousands or millions cancel out when we do the division, it makes no difference whether we use thousands or millions. In the below I will assume millions and will use wpm for words per million, as in other sciences which often use parts per million. ) It is often instructive to find the ratio for all words, and to sort words by the ratio to find the words that are most associated with each corpus as against the other. This will give a first pass at two keywords lists, one (taken from the top of the sorted list) of corpus1 vs corpus2, and the other, taken from the bottom of the list (with scores below 1 and getting close to 0), for corpus2 vs corpus1. (In the below I will refer to the two corpora as the focus corpus fc, for which we want to find keywords, and the reference corpus rc: we divide relative frequency in the focus corpus by relative frequency in the reference corpus and are interested in the high-scoring words.) There are four problems with keyword lists prepared in this way. 1. All corpora are different, usually in a multitude of ways. We probably want to examine a keyword list because of one particular dimension of difference between fc and rc perhaps a difference of genre, or of region, or of domain. The list may well be dominated by other differences, which we are not at all interested in. Keyword lists tend to work best where the corpora are very well matched in all regards except the one in question. This is a question of how fc and rc have been prepared. It is often the greatest source of bewilderment when users see keyword lists (and makes keyword lists good tools for identifying the characteristics of corpora). However it is an issue of corpus construction, which is not the topic of this paper, so it is not discussed further. 2. Burstiness. If a word is the topic for one text in the corpus, it may well be used many times in that text with its frequency in that corpus mainly coming

2 from just one text. Such bursty words do a poor job of representing the overall contrast between fc and rc. This, again, is not the topic of this paper. A range of solutions have been proposed, as reviewed by Gries (2007). The method we use in our experiments is average reduced frequency (ARF, Savický and Hlaváčová 2002) which discounts frequency for words with bursty distributions: for a word with an even distribution across a corpus, ARF will be equal to raw frequency, but for a word with a very bursty distribution, only occurring in a single short text, ARF will be a little over You can t divide by zero. It is not clear what to do about words which are present in fc but absent in rc. 4. Even setting aside the zero cases, the list will be dominated by words with very few words in the reference corpus: there is nothing very surprising about a contrast between 10 in fc and 1 in rc, giving a ratio of 10, and we expect to find many such cases, but we would be very surprised to find words with frequency per million of 10,000 in fc and only 1,000 in rc, even though that also gives a ratio of 10. Simple ratios will give a list of rarer words. The last problem has been the launching point for an extensive literature. The literature is shared with the literature on collocation statistics, since formally, the problems are similar: in both cases we compare the frequency of the keyword in condition 1 (which is either in fc or with collocate x ) with frequency in condition 2 ( in rc or not with collocate x ). The literature starts with Church and Hanks (1989) and other much-cited references include Dunning (1993), Pederson. Proposed statistics include Mutual Information (MI), Log Likelihood and Fisher s Exact Test, see Chapter X of Manning and Schütze (1999). I have argued elsewhere (Kilgarriff 2005) that the mathematical sophistication of MI, Log Likelihood and Fisher s Exact Test is of no value to us, since all it serves to do is to disprove a null hypothesis - that language is random - which is patently untrue. Sophisticated maths needs a null hypothesis to build on and we have no null hypothesis: perhaps we can meet our needs with simple maths. A common solution to the zeros problem is add one. If we add one to all the frequencies, including those for words which were present in fc but absent in rc, then we have no zeros and can compute a ratio for all words. A word with 10 wpm in fc and none in rc gets a ratio of 11:1 (as we add 1 to 10 and 1 to 0) or 11. Add one is widely used as a solution to a range of problems associated with low and zero frequency counts, in language technology and elsewhere (Manning and Schütze 1999). Add one (to all counts) is the simplest variant: there are sometimes reasons for adding some other constant, or variable amount, to all frequencies. This suggests a solution to problem 4. Consider what happens when we add 1, 100, or 1000 to all counts-per-million from both corpora. The results, for the three

4 ( After some experimentation, we have set a default value of N=100, but it is a parameter that the user can change: for rarer phenomena like collocations, a lower setting seems appropriate. i To demonstrate the method we use BAWE, the British Academic Written English corpus (Nesi 2008). It is a carefully structured corpus so, for the most part, two different subcorpora will vary only on the dimension of interest and not on other dimensions, cf problem 1 above. The corpus comprises student essays, of which a quarter are Arts and Humanities (ArtsHum) and a quarter are Social Sciences (SocSci). Fig 1 shows keywords of ArtsHum vs. SocSci with parameter N=10. Fig 1: Keywords for ArtsHum compared with SocSci essays in BAWE, with parameter N=10, using the Sketch Engine.

5 We see here a range of nouns and adjectives relating to the subject matter of the arts and humanities, and which are, plausibly, less often discussed in the social sciences. Fig 2: Keywords for ArtsHum compared with SocSci essays in BAWE, with parameter N=1000, using the Sketch Engine. Fig. 2 shows the same comparison but with N=1000. Just one word, god (lowercased in the lemmatisation process) is in the top-15 samples of both lists, and Fig 1 has historian against Fig 2 s history. Many of the words in both lists are from the linguistics domain, with them providing corroborating evidence for this being distinctive of ArtsHum in BAWE, but none are shared. Fig 1 has the less-frequent verb, noun, adjective, pronoun, tense, native and speaker (the last two possibly in the compound native speaker) whereas Fig 2 has the closely-allied but more-frequent language, word, write, english and object. (Object may be in this list by virtue of its linguistic meaning, the object of the verb, or its general or verbal meanings, the object of the exercise, he objected ; this would require further examination to resolve.) The most striking feature of the Fig 2 is the four third-person and one first-person pronouns. Whereas the first list told us about recurring topics in ArtsHum vs. SocSci, the second also tells us about a grammatical contrast between the two varieties. In summary I have summarised the challenges in finding keywords of one corpus vs. another. I have proposed a method which is both simpler than other proposals and which has the advantage of a parameter which allows the user to specify whether they want to focus on higher-frequency or lower-frequency keywords. I have pointed out that there are

6 no theoretical reasons for using more sophisticated maths and I have demonstrated the proposed method with corpus data. The new method is implemented in the Sketch Engine corpus query tool and I hope it will be implemented in other tools shortly. References Church, K. and P. Hanks Word association norms, mutual information and lexicography. Computational Linguistics 16(1), 22_29. Dunning, T Accurate methods for the statistics of surprise and coincidence. Computational Linguistics 19(1), Gries, S Dispersion and Adjusted Frequencies in Corpora. Corpus Linguistics 13 (4), pp Kilgarriff, A Language is never ever ever random. Corpus Linguistics and Linguistic Theory 1 (2): Manning, C. and H. Schütze Foundations of Statistical Natural Language Processing, MIT Press. Cambridge, MA. Nesi, H. (2008) BAWE: An introduction to a new resource. In Proc. 8th Teaching and Language Corpora Conference. Lisbon, Portugal: Pedersen, T Fishing for exactness. Proceedings of the Conference of the South-Central SAS Users Group, P. Savický and J. Hlaváčová Measures of word commonness. Journal of Quantitative Linguistics, 9: i Note that the value of the parameter is related to the value of one million that we use for normalising frequencies. If we used words per thousand rather than words per million, the parameter would need scaling accordingly.

### What Makes a Good Online Dictionary? Empirical Insights from an Interdisciplinary Research Project

Proceedings of elex 2011, pp. 203-208 What Makes a Good Online Dictionary? Empirical Insights from an Interdisciplinary Research Project Carolin Müller-Spitzer, Alexander Koplenig, Antje Töpel Institute

### Differences in linguistic and discourse features of narrative writing performance. Dr. Bilal Genç 1 Dr. Kağan Büyükkarcı 2 Ali Göksu 3

Yıl/Year: 2012 Cilt/Volume: 1 Sayı/Issue:2 Sayfalar/Pages: 40-47 Differences in linguistic and discourse features of narrative writing performance Abstract Dr. Bilal Genç 1 Dr. Kağan Büyükkarcı 2 Ali Göksu

### Accurately and Efficiently Measuring Individual Account Credit Risk On Existing Portfolios

Accurately and Efficiently Measuring Individual Account Credit Risk On Existing Portfolios By: Michael Banasiak & By: Daniel Tantum, Ph.D. What Are Statistical Based Behavior Scoring Models And How Are

### EFL Learners Synonymous Errors: A Case Study of Glad and Happy

ISSN 1798-4769 Journal of Language Teaching and Research, Vol. 1, No. 1, pp. 1-7, January 2010 Manufactured in Finland. doi:10.4304/jltr.1.1.1-7 EFL Learners Synonymous Errors: A Case Study of Glad and

### Basic Concepts in Research and Data Analysis

Basic Concepts in Research and Data Analysis Introduction: A Common Language for Researchers...2 Steps to Follow When Conducting Research...3 The Research Question... 3 The Hypothesis... 4 Defining the

### COMPUTATIONAL DATA ANALYSIS FOR SYNTAX

COLING 82, J. Horeck~ (ed.j North-Holland Publishing Compa~y Academia, 1982 COMPUTATIONAL DATA ANALYSIS FOR SYNTAX Ludmila UhliFova - Zva Nebeska - Jan Kralik Czech Language Institute Czechoslovak Academy

There are three kinds of people in the world those who are good at math and those who are not. PSY 511: Advanced Statistics for Psychological and Behavioral Research 1 Positive Views The record of a month

### Testing an electronic collocation dictionary interface: Diccionario de Colocaciones del Español

Testing an electronic collocation dictionary interface: Diccionario de Colocaciones del Español Orsolya Vincze, Margarita Alonso Ramos Universidade da Coruña, Campus da Zapateira s/n, A Coruña 15071, Spain

Frequently Asked Questions About Using The GRE Search Service General Information Who can use the GRE Search Service? Institutions eligible to participate in the GRE Search Service include (1) institutions

### ANNLOR: A Naïve Notation-system for Lexical Outputs Ranking

ANNLOR: A Naïve Notation-system for Lexical Outputs Ranking Anne-Laure Ligozat LIMSI-CNRS/ENSIIE rue John von Neumann 91400 Orsay, France annlor@limsi.fr Cyril Grouin LIMSI-CNRS rue John von Neumann 91400

### An activity-based analysis of hands-on practice methods

Journal of Computer Assisted Learning (2000) 16, 358-365 An activity-based analysis of hands-on practice methods S. Wiedenbeck, J.A. Zavala & J. Nawyn University of Nebraska Abstract The success of exploration-based

### 11. Analysis of Case-control Studies Logistic Regression

Research methods II 113 11. Analysis of Case-control Studies Logistic Regression This chapter builds upon and further develops the concepts and strategies described in Ch.6 of Mother and Child Health:

### Wizard. Succession. making succession planning simple. Succession planning software to identify management succession issues within organizations

Succession Wizard making succession planning simple. Succession planning software to identify management succession issues within organizations Succession planning is increasingly being seen as a growing

### Fixed-Effect Versus Random-Effects Models

CHAPTER 13 Fixed-Effect Versus Random-Effects Models Introduction Definition of a summary effect Estimating the summary effect Extreme effect size in a large study or a small study Confidence interval

### Predicting the Performance of a First Year Graduate Student

Predicting the Performance of a First Year Graduate Student Luís Francisco Aguiar Universidade do Minho - NIPE Abstract In this paper, I analyse, statistically, if GRE scores are a good predictor of the

### Customizing an English-Korean Machine Translation System for Patent Translation *

Customizing an English-Korean Machine Translation System for Patent Translation * Sung-Kwon Choi, Young-Gil Kim Natural Language Processing Team, Electronics and Telecommunications Research Institute,

### CHAPTER 7 EFFECTIVE DECISION SUPPORT FOR NURSE SCHEDULING

CHAPTER EFFECTIVE DECISION SUPPORT FOR NURSE SCHEDULING The third chapter described three hypotheses. Two of these hypotheses the hypothesis of formalization and the hypothesis of robustness have been

### Generalized Linear Models

Generalized Linear Models We have previously worked with regression models where the response variable is quantitative and normally distributed. Now we turn our attention to two types of models where the

### ICAME Journal No. 24. Reviews

ICAME Journal No. 24 Reviews Collins COBUILD Grammar Patterns 2: Nouns and Adjectives, edited by Gill Francis, Susan Hunston, andelizabeth Manning, withjohn Sinclair as the founding editor-in-chief of

### Comparing Tag Clouds, Term Histograms, and Term Lists for Enhancing Personalized Web Search

Comparing Tag Clouds, Term Histograms, and Term Lists for Enhancing Personalized Web Search Orland Hoeber and Hanze Liu Department of Computer Science, Memorial University St. John s, NL, Canada A1B 3X5

### Achievement of Children Identified with Special Needs in Two-way Spanish/English Immersion Programs

The Bridge: From Research to Practice Achievement of Children Identified with Special Needs in Two-way Spanish/English Immersion Programs Dr. Marjorie L. Myers, Principal, Key School - Escuela Key, Arlington,

### DEFINING EFFECTIVENESS FOR BUSINESS AND COMPUTER ENGLISH ELECTRONIC RESOURCES

Teaching English with Technology, vol. 3, no. 1, pp. 3-12, http://www.iatefl.org.pl/call/callnl.htm 3 DEFINING EFFECTIVENESS FOR BUSINESS AND COMPUTER ENGLISH ELECTRONIC RESOURCES by Alejandro Curado University

### TF-IDF. David Kauchak cs160 Fall 2009 adapted from: http://www.stanford.edu/class/cs276/handouts/lecture6-tfidf.ppt

TF-IDF David Kauchak cs160 Fall 2009 adapted from: http://www.stanford.edu/class/cs276/handouts/lecture6-tfidf.ppt Administrative Homework 3 available soon Assignment 2 available soon Popular media article

### So You Want To Go To Econ Grad School...

So You Want To Go To Econ Grad School... Tim Salmon Department of Economics Florida State University October 2006 I get asked a number of questions each year by students who are considering economics graduate

### On Log-Likelihood-Ratios and the Significance of Rare Events

On Log-Likelihood-Ratios and the Significance of Rare Events Robert C. MOORE Microsoft Research One Microsoft Way Redmond, WA 90052 USA bobmoore@microsoft.com Abstract We address the issue of judging the

### How the Computer Translates. Svetlana Sokolova President and CEO of PROMT, PhD.

Svetlana Sokolova President and CEO of PROMT, PhD. How the Computer Translates Machine translation is a special field of computer application where almost everyone believes that he/she is a specialist.

### Texas Success Initiative (TSI) Assessment. Interpreting Your Score

Texas Success Initiative (TSI) Assessment Interpreting Your Score 1 Congratulations on taking the TSI Assessment! The TSI Assessment measures your strengths and weaknesses in mathematics and statistics,

### Quality Assurance at NEMT, Inc.

Quality Assurance at NEMT, Inc. Quality Assurance Policy NEMT prides itself on the excellence of quality within every level of the company. We strongly believe in the benefits of continued education and

### Predicting Medication Compliance and Persistency

Predicting Medication Compliance and Persistency By: Jay Bigelow, President Amanda Rhodes, M.P.H., C.H.E.S., Vice President Behavioral Solutions MicroMass Communications, Inc. Introduction A widely recognized

### CHARTES D'ANGLAIS SOMMAIRE. CHARTE NIVEAU A1 Pages 2-4. CHARTE NIVEAU A2 Pages 5-7. CHARTE NIVEAU B1 Pages 8-10. CHARTE NIVEAU B2 Pages 11-14

CHARTES D'ANGLAIS SOMMAIRE CHARTE NIVEAU A1 Pages 2-4 CHARTE NIVEAU A2 Pages 5-7 CHARTE NIVEAU B1 Pages 8-10 CHARTE NIVEAU B2 Pages 11-14 CHARTE NIVEAU C1 Pages 15-17 MAJ, le 11 juin 2014 A1 Skills-based

### Recurring Billing Guide

Recurring Billing Guide Overview Recurring billing is implemented in Yogareg as auto-renew class bundles (sometimes called class cards or class passes) and subscriptions. Auto-renew class bundles are class

### Electronic offprint from. baltic linguistics. Vol. 3, 2012

Electronic offprint from baltic linguistics Vol. 3, 2012 ISSN 2081-7533 Nɪᴄᴏʟᴇ Nᴀᴜ, A Short Grammar of Latgalian. (Languages of the World/Materials, 482.) München: ʟɪɴᴄᴏᴍ Europa, 2011, 119 pp. ɪѕʙɴ 978-3-86288-055-3.

### ln(p/(1-p)) = α +β*age35plus, where p is the probability or odds of drinking

Dummy Coding for Dummies Kathryn Martin, Maternal, Child and Adolescent Health Program, California Department of Public Health ABSTRACT There are a number of ways to incorporate categorical variables into

### ".,!520%'\$!#)!"#\$%&!>#(\$!#)! <*+,-(./0!/+!567!5+:,(\$2,+\$! @,'/(/#+(!

".,!520%'\$!#)!"#\$%&!>#(\$!#)!

### The Financial Crisis and the Bankruptcy of Small and Medium Sized-Firms in the Emerging Market

The Financial Crisis and the Bankruptcy of Small and Medium Sized-Firms in the Emerging Market Sung-Chang Jung, Chonnam National University, South Korea Timothy H. Lee, Equifax Decision Solutions, Georgia,

### Introduction to. Hypothesis Testing CHAPTER LEARNING OBJECTIVES. 1 Identify the four steps of hypothesis testing.

Introduction to Hypothesis Testing CHAPTER 8 LEARNING OBJECTIVES After reading this chapter, you should be able to: 1 Identify the four steps of hypothesis testing. 2 Define null hypothesis, alternative

### A Primer on Forecasting Business Performance

A Primer on Forecasting Business Performance There are two common approaches to forecasting: qualitative and quantitative. Qualitative forecasting methods are important when historical data is not available.

### Natural Language Database Interface for the Community Based Monitoring System *

Natural Language Database Interface for the Community Based Monitoring System * Krissanne Kaye Garcia, Ma. Angelica Lumain, Jose Antonio Wong, Jhovee Gerard Yap, Charibeth Cheng De La Salle University

### Summary Ph.D. thesis Fredo Schotanus Horizontal cooperative purchasing

Summary Ph.D. thesis Fredo Schotanus Horizontal cooperative purchasing Purchasing in groups is a concept that is becoming increasingly popular in both the private and public sector. Often, the advantages

### Chapter 5. Phrase-based models. Statistical Machine Translation

Chapter 5 Phrase-based models Statistical Machine Translation Motivation Word-Based Models translate words as atomic units Phrase-Based Models translate phrases as atomic units Advantages: many-to-many

### Facilitating Business Process Discovery using Email Analysis

Facilitating Business Process Discovery using Email Analysis Matin Mavaddat Matin.Mavaddat@live.uwe.ac.uk Stewart Green Stewart.Green Ian Beeson Ian.Beeson Jin Sa Jin.Sa Abstract Extracting business process

### Data Deduplication in Slovak Corpora

Ľ. Štúr Institute of Linguistics, Slovak Academy of Sciences, Bratislava, Slovakia Abstract. Our paper describes our experience in deduplication of a Slovak corpus. Two methods of deduplication a plain

### Social Media Analytics Summit April 17-18, 2012 Hotel Kabuki, San Francisco WELCOME TO THE SOCIAL MEDIA ANALYTICS SUMMIT #SMAS12

Social Media Analytics Summit April 17-18, 2012 Hotel Kabuki, San Francisco WELCOME TO THE SOCIAL MEDIA ANALYTICS SUMMIT #SMAS12 www.textanalyticsnews.com www.usefulsocialmedia.com New Directions in Social

### Examples of Tasks from CCSS Edition Course 3, Unit 5

Examples of Tasks from CCSS Edition Course 3, Unit 5 Getting Started The tasks below are selected with the intent of presenting key ideas and skills. Not every answer is complete, so that teachers can

### Assignment 2: Option Pricing and the Black-Scholes formula The University of British Columbia Science One CS 2015-2016 Instructor: Michael Gelbart

Assignment 2: Option Pricing and the Black-Scholes formula The University of British Columbia Science One CS 2015-2016 Instructor: Michael Gelbart Overview Due Thursday, November 12th at 11:59pm Last updated

### Logs Transformation in a Regression Equation

Fall, 2001 1 Logs as the Predictor Logs Transformation in a Regression Equation The interpretation of the slope and intercept in a regression change when the predictor (X) is put on a log scale. In this

### WRITING SUCCESSFUL MANUSCRIPTS FOR PHYSICAL REVIEW LETTERS. Saad E. Hebboul * The American Physical Society, Ridge, NY.

WRITING SUCCESSFUL MANUSCRIPTS FOR PHYSICAL REVIEW LETTERS Saad E. Hebboul * The American Physical Society, Ridge, NY PART 2 of 2 * Email: hebboul@aps.org OUTLINE : PART 2 of 2 INACCESSIBLE TEST MANUSCRIPT

### Automatic Mining of Internet Translation Reference Knowledge Based on Multiple Search Engines

, 22-24 October, 2014, San Francisco, USA Automatic Mining of Internet Translation Reference Knowledge Based on Multiple Search Engines Baosheng Yin, Wei Wang, Ruixue Lu, Yang Yang Abstract With the increasing

### Pronunciation in English

The Electronic Journal for English as a Second Language Pronunciation in English March 2013 Volume 16, Number 4 Title Level Publisher Type of product Minimum Hardware Requirements Software Requirements

### You Can t Beat Frequency (Unless You Use Linguistic Knowledge) A Qualitative Evaluation of Association Measures for Collocation and Term Extraction

You Can t Beat Frequency (Unless You Use Linguistic Knowledge) A Qualitative Evaluation of Association Measures for Collocation and Term Extraction Joachim Wermter Udo Hahn Jena University Language & Information

### Terminology Extraction from Log Files

Terminology Extraction from Log Files Hassan Saneifar 1,2, Stéphane Bonniol 2, Anne Laurent 1, Pascal Poncelet 1, and Mathieu Roche 1 1 LIRMM - Université Montpellier 2 - CNRS 161 rue Ada, 34392 Montpellier

### Using Wikipedia to Translate OOV Terms on MLIR

Using to Translate OOV Terms on MLIR Chen-Yu Su, Tien-Chien Lin and Shih-Hung Wu* Department of Computer Science and Information Engineering Chaoyang University of Technology Taichung County 41349, TAIWAN

### Using Web Search for Machine Translation Nicolas Wehmeier BSc Computing and German 2003/2004

Using Web Search for Machine Translation Nicolas Wehmeier BSc Computing and German 2003/2004 The candidate confirms that the work submitted is their own and the appropriate credit has been given where

### Intelligence. User Manual. Ad\$pender TM

Intelligence User Manual Ad\$pender TM January 2011 INTRODUCTION Introduction About Ad\$pender TM Ad\$pender is a tool that allows you to view a top-level summary of the multi-media advertising marketplace.

### PoS-tagging Italian texts with CORISTagger

PoS-tagging Italian texts with CORISTagger Fabio Tamburini DSLO, University of Bologna, Italy fabio.tamburini@unibo.it Abstract. This paper presents an evolution of CORISTagger [1], an high-performance

### dm106 TEXT MINING FOR CUSTOMER RELATIONSHIP MANAGEMENT: AN APPROACH BASED ON LATENT SEMANTIC ANALYSIS AND FUZZY CLUSTERING

dm106 TEXT MINING FOR CUSTOMER RELATIONSHIP MANAGEMENT: AN APPROACH BASED ON LATENT SEMANTIC ANALYSIS AND FUZZY CLUSTERING ABSTRACT In most CRM (Customer Relationship Management) systems, information on

### PELLISSIPPI STATE COMMUNITY COLLEGE MASTER SYLLABUS BEGINNING SPANISH I SPAN 1010. Laboratory Hours: 0.0 Date Revised: Summer 10

PELLISSIPPI STATE COMMUNITY COLLEGE MASTER SYLLABUS BEGINNING SPANISH I SPAN 1010 Class Hours: 3.0 Credit Hours: 3.0 Laboratory Hours: 0.0 Date Revised: Summer 10 Catalog Course Description: Introduction

### Texas Success Initiative (TSI) Assessment

Texas Success Initiative (TSI) Assessment Interpreting Your Score 1 Congratulations on taking the TSI Assessment! The TSI Assessment measures your strengths and weaknesses in mathematics and statistics,

### THE PREDICTIVE MODELLING PROCESS

THE PREDICTIVE MODELLING PROCESS Models are used extensively in business and have an important role to play in sound decision making. This paper is intended for people who need to understand the process

### A FUZZY BASED APPROACH TO TEXT MINING AND DOCUMENT CLUSTERING

A FUZZY BASED APPROACH TO TEXT MINING AND DOCUMENT CLUSTERING Sumit Goswami 1 and Mayank Singh Shishodia 2 1 Indian Institute of Technology-Kharagpur, Kharagpur, India sumit_13@yahoo.com 2 School of Computer

### Belief Formation in the Returns to Schooling

Working paper Belief Formation in the Returns to ing Jim Berry Lucas Coffman March 212 Belief Formation in the Returns to ing March 31, 212 Jim Berry Cornell University Lucas Coffman Ohio State U. & Yale

### COMPARATIVES WITHOUT DEGREES: A NEW APPROACH. FRIEDERIKE MOLTMANN IHPST, Paris fmoltmann@univ-paris1.fr

COMPARATIVES WITHOUT DEGREES: A NEW APPROACH FRIEDERIKE MOLTMANN IHPST, Paris fmoltmann@univ-paris1.fr It has become common to analyse comparatives by using degrees, so that John is happier than Mary would

### If A is divided by B the result is 2/3. If B is divided by C the result is 4/7. What is the result if A is divided by C?

Problem 3 If A is divided by B the result is 2/3. If B is divided by C the result is 4/7. What is the result if A is divided by C? Suggested Questions to ask students about Problem 3 The key to this question

### Probability and statistical hypothesis testing. Holger Diessel holger.diessel@uni-jena.de

Probability and statistical hypothesis testing Holger Diessel holger.diessel@uni-jena.de Probability Two reasons why probability is important for the analysis of linguistic data: Joint and conditional

### Glossary of Terms Ability Accommodation Adjusted validity/reliability coefficient Alternate forms Analysis of work Assessment Battery Bias

Glossary of Terms Ability A defined domain of cognitive, perceptual, psychomotor, or physical functioning. Accommodation A change in the content, format, and/or administration of a selection procedure

### HUM 295 American Sign Language Ohio Valley University (1 semester hr. credit) Joe Spivy, Instructor

HUM 295 American Sign Language Ohio Valley University (1 semester hr. credit) Spring 209 F 11:45 12:35 Room 64 Joe Spivy, Instructor 304-295-5116 (Ofc) 304-210-8950 (Cell) joespivy@hotmail.com Feel free

### Milk, bread and toothpaste : Adapting Data Mining techniques for the analysis of collocation at varying levels of discourse

Milk, bread and toothpaste : Adapting Data Mining techniques for the analysis of collocation at varying levels of discourse Rob Sanderson, Matthew Brook O Donnell and Clare Llewellyn What happens with

### Structural and Semantic Indexing for Supporting Creation of Multilingual Web Pages

Structural and Semantic Indexing for Supporting Creation of Multilingual Web Pages Hiroshi URAE, Taro TEZUKA, Fuminori KIMURA, and Akira MAEDA Abstract Translating webpages by machine translation is the

### Left-Brain/Right-Brain Multi-Document Summarization

Left-Brain/Right-Brain Multi-Document Summarization John M. Conroy IDA/Center for Computing Sciences conroy@super.org Jade Goldstein Department of Defense jgstewa@afterlife.ncsc.mil Judith D. Schlesinger

### CINTIL-PropBank. CINTIL-PropBank Sub-corpus id Sentences Tokens Domain Sentences for regression atsts 779 5,654 Test

CINTIL-PropBank I. Basic Information 1.1. Corpus information The CINTIL-PropBank (Branco et al., 2012) is a set of sentences annotated with their constituency structure and semantic role tags, composed

### Simple Language Models for Spam Detection

Simple Language Models for Spam Detection Egidio Terra Faculty of Informatics PUC/RS - Brazil Abstract For this year s Spam track we used classifiers based on language models. These models are used to

### 9. Sampling Distributions

9. Sampling Distributions Prerequisites none A. Introduction B. Sampling Distribution of the Mean C. Sampling Distribution of Difference Between Means D. Sampling Distribution of Pearson's r E. Sampling

### Algebra 1 Course Information

Course Information Course Description: Students will study patterns, relations, and functions, and focus on the use of mathematical models to understand and analyze quantitative relationships. Through

### Non-nominal Which-Relatives

Non-nominal Which-Relatives Doug Arnold, Robert D. Borsley University of Essex The properties of non-restrictive relatives All non-restrictive relative clauses include a wh-word. There are no that or zero

### {the guide} The Small and Midsize Business Email Marketing Survey 2013

{the guide} The Small and Midsize Business Email Marketing Survey 2013 Introduction Email remains a staple component in the marketing strategies of businesses and organizations of all shapes and sizes.

### Enhancing the relativity between Content, Title and Meta Tags Based on Term Frequency in Lexical and Semantic Aspects

Enhancing the relativity between Content, Title and Meta Tags Based on Term Frequency in Lexical and Semantic Aspects Mohammad Farahmand, Abu Bakar MD Sultan, Masrah Azrifah Azmi Murad, Fatimah Sidi me@shahroozfarahmand.com

### UCINET Quick Start Guide

UCINET Quick Start Guide This guide provides a quick introduction to UCINET. It assumes that the software has been installed with the data in the folder C:\Program Files\Analytic Technologies\Ucinet 6\DataFiles

### Probability, statistics and football Franka Miriam Bru ckler Paris, 2015.

Probability, statistics and football Franka Miriam Bru ckler Paris, 2015 Please read this before starting! Although each activity can be performed by one person only, it is suggested that you work in groups

### Symbiosis of Evolutionary Techniques and Statistical Natural Language Processing

1 Symbiosis of Evolutionary Techniques and Statistical Natural Language Processing Lourdes Araujo Dpto. Sistemas Informáticos y Programación, Univ. Complutense, Madrid 28040, SPAIN (email: lurdes@sip.ucm.es)

Advanced Load Balancing Mechanism on Mixed Batch and Transactional Workloads G. Suganthi (Member, IEEE), K. N. Vimal Shankar, Department of Computer Science and Engineering, V.S.B. Engineering College,

### Hypothesis Test for Mean Using Given Data (Standard Deviation Known-z-test)

Hypothesis Test for Mean Using Given Data (Standard Deviation Known-z-test) A hypothesis test is conducted when trying to find out if a claim is true or not. And if the claim is true, is it significant.

### Teaching Vocabulary. Lessons from the Corpus Lessons for the Classroom. Jeanne McCarten

Teaching Vocabulary Lessons from the Corpus Lessons for the Classroom Jeanne McCarten cambridge university press Cambridge, New York, Melbourne, Madrid, Cape Town, Singapore, São Paulo Cambridge University

### An Investigation through Different Types of Bilinguals and Bilingualism Hamzeh Moradi Abstract Keywords:

International Journal of Humanities & Social Science Studies (IJHSSS) A Peer-Reviewed Bi-monthly Bi-lingual Research Journal ISSN: 2349-6959 (Online), ISSN: 2349-6711 (Print) Volume-I, Issue-II, September

### PTE Academic Preparation Course Outline

PTE Academic Preparation Course Outline August 2011 V2 Pearson Education Ltd 2011. No part of this publication may be reproduced without the prior permission of Pearson Education Ltd. Introduction The

### MATH 90 CHAPTER 6 Name:.

MATH 90 CHAPTER 6 Name:. 6.1 GCF and Factoring by Groups Need To Know Definitions How to factor by GCF How to factor by groups The Greatest Common Factor Factoring means to write a number as product. a

### Report to the 79 th Legislature. Use of Credit Information by Insurers in Texas

Report to the 79 th Legislature Use of Credit Information by Insurers in Texas Texas Department of Insurance December 30, 2004 TABLE OF CONTENTS Executive Summary Page 3 Discussion Introduction Page 6

### A Hands-On Exercise Improves Understanding of the Standard Error. of the Mean. Robert S. Ryan. Kutztown University

A Hands-On Exercise 1 Running head: UNDERSTANDING THE STANDARD ERROR A Hands-On Exercise Improves Understanding of the Standard Error of the Mean Robert S. Ryan Kutztown University A Hands-On Exercise

### Most EFL teachers in Japan find that there are groups of verbs which consistently

Results of a Corpus Study of LOOK, SEE, and WATCH Gregory C. Anthony Hachinohe University Reference Data: Anthony, G. C. (2012). Results of a Corpus Study of LOOK, SEE, and WATCH. In A. Stewart & N. Sonda

Grameen Foundation s Savings Seminar Data Analytics: Answering business questions with data Oct 22 nd, 2013 Washington DC Speakers Tanaya Kilara, Financial Sector Analyst at CGAP Jacobo Menajovsky, Senior

### Interpreting Kullback-Leibler Divergence with the Neyman-Pearson Lemma

Interpreting Kullback-Leibler Divergence with the Neyman-Pearson Lemma Shinto Eguchi a, and John Copas b a Institute of Statistical Mathematics and Graduate University of Advanced Studies, Minami-azabu

### Assessing the Impact of a Tablet-PC-based Classroom Interaction System

STo appear in Proceedings of Workshop on the Impact of Pen-Based Technology on Education (WIPTE) 2008. Assessing the Impact of a Tablet-PC-based Classroom Interaction System Kimberle Koile David Singer

### Name: Date: Use the following to answer questions 3-4:

Name: Date: 1. Determine whether each of the following statements is true or false. A) The margin of error for a 95% confidence interval for the mean increases as the sample size increases. B) The margin

### Sample Questions for Series 8100 PROFESSIONAL LEVEL EXAM (PLE)

Louisiana Department of State Civil Service Sample Questions for Series 8100 PROFESSIONAL LEVEL EXAM (PLE) This booklet contains SAMPLE QUESTIONS ONLY. None of the questions in this booklet are actual

### Examining the Savings Habits of Individuals with Present-Fatalistic Time Perspectives using the Theory of Planned Behavior

Examining the Savings Habits of Individuals with Present-Fatalistic Time Perspectives using the Theory of Planned Behavior Robert H. Rodermund April 3, 2012 Lindenwood University 209 S. Kingshighway, Harmon

### SAS Software to Fit the Generalized Linear Model

SAS Software to Fit the Generalized Linear Model Gordon Johnston, SAS Institute Inc., Cary, NC Abstract In recent years, the class of generalized linear models has gained popularity as a statistical modeling

### HOW TO WRITE A LABORATORY REPORT

HOW TO WRITE A LABORATORY REPORT Pete Bibby Dept of Psychology 1 About Laboratory Reports The writing of laboratory reports is an essential part of the practical course One function of this course is to

### Bond valuation and bond yields

RELEVANT TO ACCA QUALIFICATION PAPER P4 AND PERFORMANCE OBJECTIVES 15 AND 16 Bond valuation and bond yields Bonds and their variants such as loan notes, debentures and loan stock, are IOUs issued by governments

### Robustness of a Spoken Dialogue Interface for a Personal Assistant

Robustness of a Spoken Dialogue Interface for a Personal Assistant Anna Wong, Anh Nguyen and Wayne Wobcke School of Computer Science and Engineering University of New South Wales Sydney NSW 22, Australia