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![Non-Word Error Detection for Luganda
Robert Ssali Balagadde * and Parvataneni Premchand **
* ** Department of Computer Science & Engineering, University College of Engineering, Osmania University
Hyderabad, 500007, TS, India
baross.kla@gmail.com, Profpremchand.p@gmail.com
Abstract: Editing or word processing Luganda text has been an uphill task mainly because of lack of a system in this
environment which could detect spelling errors in Luganda. In this context, this research paper presents a model for non-word
error detection for Luganda (LugDetect) which comes in handy to address this gap and consequently provide a more user
friendly environment for editing Luganda text. To the best of our knowledge LugDetect is the first of this kind of system
developed for Luganda. Experimentation results show that LugDetect detects non-word errors with an accuracy (AP) of 100%
for all the five categories of Luganda words at an average speed of 1471 Hz (number of words per second) so long as the
erroneous word is not a real word. Experimentation on the Luganda corpus which was used in this research work shows that
19% of Luganda text is composed of Clitics Host Word Combination (CHWC), while the other part (81%)- Real Luganda
Words (RLW).
Keywords: non-word error detection, Luganda error detector, dictionary look-up technique, clitic-host word combination
(CHWC), spelling detector.
Introduction
Editing or word processing Luganda text has been an uphill task mainly because of lack of a environmental system which
could detect spelling errors in Luganda. In this context, this research work presents a model for Non-Word Error Detection
For Luganda (NWEDL- LugDetect) which comes in handy to address this gap, and consequently, provide a more user
friendly environment for editing Luganda text. To the best of our knowledge, LugDetect is the first of this kind of system
developed for Luganda.
One challenge encountered while developing a model for Luganda spell checking is dealing with the infinite number of
clitic-host word combinations (CHWCs) which makes Luganda distinct from other foreign languages especially non-Bantu
languages. In this research work, three types of CHWC are identified in respect to the use of inter-word apostrophe (IWA)
defined in subsection on ”The Error Detection Mechanism"
Type one - CHWC_1, bolded in Example 1 - are created as a result of compounding a modified monosyllabic word
(MMW) or modified disyllabic word with an initial vowel (MDWIV) with another succeeding word which begins with a
vowel. Compounding in Luganda may involve two to three words. MMW or MDWIV- referred to as clitic - is formed by
dropping the ending vowel of the mono-syllabic word (MW) or disyllabic word with an initial vowel (DWIV) and replacing
it with an apostrophe resulting into a long sound that is not represented by a double vowel.
Example 1
� omwenge n'ennyama (alcohol and meat) [conjunctive form]
� n'otema omuti (and you cut the tree) [narrative form]
� ew'omuyizzi (at the hunter's place) [locative form]
� Minisita omubeezi ow'ebyobusuubuzi ne tekinologiya (Minister of State for Trade and Technology)
� n'obw'embwa sibwagala (even that for the dog, I don't like)
� n'olw'ensonga eyo, sijja kujja (and for that reason, I will not come)
Example 2 shows some MWs and DWIVs (bolded) used in compounding to form CHWC_Is. Not all MWs and DWIVs are
used in compounding, and Example 3 shows some of these.
Example 2
� ne, na, nga [conjunction]
� ne, nga [narratives]
� be, ze, ge. gwe , bwe, bye, lwe, lye, kye, bye [object relatives]
� kya, ya, za. lya ba, bya, ga, gwa, ka, lwa, wa [possessives]
� ekya, eya, eza. erya, owa, aba [possessives with initial vowel]
� kye, ye, be, ze, twe [copulatives]
� e [locatives]](https://image.slidesharecdn.com/52770-250413060045-e7157975/75/Emerging-Technologies-in-Engineering-Mahesh-P-K-22-2048.jpg)
![2 Sixth International Conference on Advances in Information Technology and Mobile Communication – AIM 2016
Example 3
� era, ate, nti, so [conjunction]
� atya, oti [adverbs]
� ggwe, nze, ye, yo. bo, zo, bwo [emphatic pronouns]
� bba [nonn]
� si [negation]
Type II - CHWC_II, shown in Example 4 - are formed as a result of using clitic ng' and nng' to represent "�����������������
double "���������������������������������������������������������������������
Example 4
� ng'ang'ala (to whimper like a dog)
� bbiring'anya (egg plant)
� agakung'anyizzaamu (to collect in it something)
� enng'anda (relatives)
� nng'oma (dram)
Type III - CHWC_III, shown in Example 5 - are formed as a result of using clitic ng' and nng' to represent "�����������������
double "��������������������ctively in words which are initially CHWC_1.
Example 5
� n'agakung'anyizzaamu (and to collect in it something)
� ng'eng'anda (like relatives)
� ng'enng'oma (like a dram)
In view of this, the first task is disambiguating the three types of CHWCs. The approach adopted to disambiguate these words
in LugDetect is discussed in subsection on "Disambiguating the CHWCs"
The second task is how to detect errors in each type of CHWCs. The approach adopted to provide this functionality in
LugDetect is discussed in subsection on "Spell Checking Luganda Clitic Host Word Combination Type I (CHWC_I)".
CHWC_IIs, which can be easy converted into real Luganda words (RLW) by substitution, are dealt with in subsection on
"Spell Checking RLW".
It is worthwhile noting that the CHWC_1 akin to Luganda are also found in other Bantu languages like Runyankore-
Rukiga, Kinyarwanda, among others. Example 6 and Example 7 shows samples of CHWC_I extracted from Runyankore-
Rukiga and Kinyarwanda text respectively.
Example 6
� ky’okutunga
� nk’eihanga
� n’ekya
� g’ebyemikono
Example 7
� n’uw’Umukuru ( and His Excellence)
� Nk’uko (like that)
� y’Umukuru (His Excellence)
� w’Igihugu (of a country)
The third challenge is deciding when to invoke LugDetect or the Error Detection Mechanism (EDM) during interactive
processing and during batch processing. For interactive processing, should LugDetect be invoked when the space bar (SB) is
struck or at every key-stroke? The former approach has a shortfall in that a modified word can miss out on spell checking
through the use of insertion bar (IB) movement keys - a list of which is shown in Table 1 - which move the position of IB
from the modified word to some other word without striking the SB. The latter approach would eliminate this problem but its
main problem is that it is strenuous on the system due to the numerous EDM invocations. A solution to this is the
development of an Algorithm for Interactive Invocation of EDM (IIEDM) discussed in subsection on "Interactive invocation
of EDM"
For batch processing, how should EDM be invoked? A solution to this is discussed in form of an algorithm. A summary
of the modules making up LugDetect is presented in subsection on "The Error Detection Mechanism"
LugDetect can detect numerical errors and word level punctuation errors, however, in this paper, we only mention them.
Details of the modules dealing with these errors can be obtained from "unpublished" [2].](https://image.slidesharecdn.com/52770-250413060045-e7157975/75/Emerging-Technologies-in-Engineering-Mahesh-P-K-23-2048.jpg)
![Non-Word Error Detection for Luganda 3
The scope of LugDetect is limited to Luganda non-word detection. Named entities, abbreviations, e-mail addresses, uniform
resource locator (URL) strings are not handled in LugDetect, and neither are the real word errors.
Literature Survey
Errors
According to Peterson [15], spelling errors are basically of two types. Firstly, cognitive errors which are errors due to lack of
knowledge of the language and are errors often ignored on the assertion that they are infrequent. In this context, their
frequency is evaluated to be between 10% and 15% [17]. Secondly, typographical errors are 80% of "typos" (also called
single character errors) and are one of the following four types: one exceeding character, one missing character, a mistaken
character, or the transposition of two consecutive characters [4]. This means that 80% of errors are within edit distance of one
and almost all errors - within edit distance of two.
Error Detection
There are two techniques used in detecting non-word errors. First, the Dictionary Lookup Technique (DLT), in which each
word in the input text is lookup in the dictionary. In case a word is not found in the lexicon that word is a non-word or
incorrect, and therefore, flagged or entered into the list of erroneous words. The larger the lexicon the better is the results.
Shinghal and Toussaint [18] noted that DLT has low error rates, but is disadvantaged by large storage demands and high
computational complexity. Most current spelling correction techniques rely on DLT for non-word error detection.
Second, a technique that uses a non-positional bi-gram 26 by 26 matrix which captures information on the existence of
the bi-grams. A bi-gram is assigned a value of one if it exists in the corpus of the language in consideration, otherwise it is
assigned zero. All the bi-grams making up the input text are checked for their existence in the matrix. if any of the bi-grams
is non-existence than the word is flagged or a non-word. This technique is appropriate for Optical Character Recognition
(OCR) errors and it has proven to be less accurate for detecting human-generated errors.
Lexicon
User-lexicon can be interactively enriched with new entries enabling the checker to recognize all the possible inflexions
derived from them. A lexicon for a spelling correction or text recognition application must be carefully tuned to its intended
domain of discourse. Too small a lexicon can burden the user with too many false rejections of valid terms; too large a
lexicon can result in an unacceptably high number of false acceptances. The relationship between misspellings and word
frequencies is not straightforward.
Peterson [16]. recommend that lexicon for spelling correction be kept relatively small based on the fact that
approximately half a percent of all single error transformations of each of the words on a 350,000-item word list result in
other valid words on the list However, Damerau and Mays [5] challenge this recommendation by using a corpus of over 22
million words of text from various genres and they found that by increasing the size of their frequency rank-ordered word list
from 50,000 to 60,000 words; they were able to eliminate 1,348 false rejections while incurring only 23 additional false
acceptances. Since this 50-to-l differential error rate represents a significant improvement in correction accuracy; therefore,
they recommend the use of larger lexicons.
Dictionaries alone are often insufficient sources for lexicon construction. Walker and Amsle [19] observed that nearly
two-thirds (61%) of the words in the Merriam-Webster Seventh Collegiate Dictionary did not appear in an eight million word
corpus of New York Times news wire text, and conversely, almost two-thirds (64.9%) of the words in the text were not in the
dictionary.
On the topic of construction of the lexicon for the spell program, McIlroy [13] provides some helpful insights into
appropriate sources that may be drawn upon for general lexicon construction.
An article by Damerau [6] provides some insights into and guidelines for the automatic construction and customization of
domain oriented vocabularies for specialized NLP (Natural Language Processing) applications.
LugDetect
LugDetect, developed using Python programming language, identifies the type of Luganda word or token which need be
spell checked, and consequently, invokes the appropriate module for detecting the error. LugDetect works in tandem with
LugCorrect to provide detection and correction for Luganda words. In other words, LugDetect is required to detect the error
before LugCorrect can be invoked for correction purposes. The mechanism for LugCorrect, which provides correction
candidate list (CCL) or an explanation or hint on the type of error committed, is shown in Algorithm 1 and more details are
provided in [3].](https://image.slidesharecdn.com/52770-250413060045-e7157975/75/Emerging-Technologies-in-Engineering-Mahesh-P-K-24-2048.jpg)
Emerging Technologies in Engineering Mahesh P. K.
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- 7. McGraw Hill Education(India) Private Limited CHENNAI McGraw Hill Education Offices Chennai New York St Louis San Francisco Auckland Bogotá Caracas Kuala Lumpur Lisbon London Madrid Mexico City Milan Montreal San Juan Santiago Singapore Sydney Tokyo Toronto Editors-in-Chief Mahesh P K Don Bosco Institute of Technology, India Su-Qun Cao Huaiyin Institute of Technology, China Ghanshyam Singh Malaviya National Institute of Technology, India Emerging Technologies in Engineering
- 8. McGraw Hill Education(India) Private Limited Published by McGraw Hill Education (India) Private Limited, 444/1, Sri Ekambara Naicker Industrial Estate, Alapakkam, Porur, Chennai-600 116, Tamil Nadu, India Emerging Technologies in Engineering Copyright © 2016, by McGraw Hill Education (India) Private Limited No part of this publication may be reproduced or distributed in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise or stored in a database or retrieval system without the prior written permission of the publishers. The program listing (if any) may be entered, stored and executed in a computer system, but they may not be reproduced for publication. This edition can be exported from India only by the publishers, McGraw Hill Education (India) Private Limited. ISBN (13): 978-93-5260-317-6 ISBN (10): 93-5260-317-6 Managing Director: Kaushik Bellani Director: Products (Higher Education and Professional): Vibha Mahajan Manager—Product Development (B&E–BGR): Hemant K Jha Senior Specialist—Product Development (B&E–BGR): Laxmi Singh Senior Coordinator—Product Development: Kunal Adhikari Head—Production (Higher Education and Professional): Satinder S Baveja Assistant Manager—Production: Jagriti Kundu General Manager—Production: Rajender P Ghansela Manager—Production: Reji Kumar Information contained in this work has been obtained by McGraw Hill Education (India), from sources believed to be reliable. However, neither McGraw Hill Education (India) nor its authors guarantee the accuracy or completeness of any information published herein, and neither McGraw Hill Education (India) nor its authors shall be responsible for any errors, omissions, or damages arising out of use of this information. This work is published with the understanding that McGraw Hill Education (India) and its authors are supplying information but are not attempting to render engineering or other professional services. If such services are required, the assistance of an appropriate professional should be sought. Typeset at Text-o-Graphics, B-1/56, Aravali Apartment, Sector-34, Noida 201301, and printed at Cover Printer:
- 9. Committees Honorary Chair Dr. ShuvraDas (University of Detroit Mercy, USA) Dr. Jiguo Yu (Qufu Normal University, China) Dr. Pawel Hitczenko (Drexel University, USA) Dr. Lisa Osadciw (Syracuse University, USA) Dr. Jiguo Yu (Qufu Normal University, China) Prof. Harry E. Ruda (University of Toronto, Canada) Technical Chair Dr. Anooj P K (Al Musanna College of Technology, Sultanate of Oman) Dr. Urmila Shrawankar (G H Raisoni College of Engineering, India) Dr. Ching-Chih Tsai (IEEE SMCS TC on Intelligent Learning on Control Systems) Dr. A G Hessami (IEEE SMCS TC on Systems Safety & Security) Dr. Xuelong Li (Birkbeck College, University of London, U.K.) Dr. R Vijayakumar (MG University, India) Dr. N.Nagarajan (Anna University, Coimbatore) Chief Editors Dr. Mahesh P.K (Don Bosco Institute of Technology, India) Dr. Su-Qun Cao (Huaiyin Institute of Technology, China) Dr. Ghanshyam Singh (Malaviya National Institute of Technology, India) Technical Co-Chair Dr. Natarajan Meghanathan (Jackson State University, USA) Dr. Hicham Elzabadani (American University in Dubai) Dr. Pingkun Yan (Philip Research North America) General Chair Dr. Janahanlal Stephen (Matha College of Technology, India) Dr. Yogesh Chaba (Guru Jambeswara University, India) General Co-Chair Prof. K. U Abraham (Holykings College of Engineering, India) Finance Chair Dr. Gylson Thomas (Thejus Engineering College, India) Dr. Zhenyu Y Angz (Florida International University, USA) Publicity Chair Dr. Amit Manocha (Maharaja Agrasen Institute of Technology, India)
- 10. vi Committees Publicity Co-Chair Dr.Ford Lumban Gaol (University of Indonesia) Dr. Amlan Chakrabarti (University of Culcutta, India) Publication Chair Dr. Vijayakumar (NSS Engg. College, India) Dr. T.S.B.Sudarshan (BITS Pilani, India) Dr. KP Soman (Amritha University, India) Program Committee Chair Dr. Harry E. Ruda (University of Toronto, Canada) Dr Deepak Laxmi Narasimha (University of Malaya, Malaysia) Dr.N.Nagarajan (Anna University, Coimbatore, India) Prof. Akash Rajak (Krishna Institute of Engg. & Tech., UP, India) Dr. Javed Vassilis Khan (Academy for Digital Entertainment, The Netherlands) International Advisory Committee Dr. Pawel Hitczenko (Drexel University, USA) Dr. Kristian J. Hammond (Northwestern University, USA) Dr. Long Que (Louisiana Tech University, USA) Dr. Peter Vadasz (Northern Arizona University, USA) Dr. Hamid Bagheri (Sharif University of Technology, Iran) National Advisory Committee Dr. Saurabh Chaudhury (IIT Kharagpur, India) Dr. R.Kumar (SRM University, India) Dr. M Kumar (SIRT, Bhopal, India) Program Committee Members Dr. Shu-Ching Chen (Florida International University, USA) Dr. T.S.B.Sudarshan (BITS Pilani, India) Dr. Habibollah Haro (Universiti Teknologi Malaysia) Dr. V. K. Bhat (SMVD University, India) Dr. Keivan Navi (Shahid Beheshti University, Tehran) Prof. Anil K Jacob (MES College of Engineering, India) Dr. B. Kannan (CUSAT, India) Dr. Liviu Vladutu (Dublin City University, Ireland) Dr. Malabika Basu (Dublin Institute of Technology, Ireland) Dr. Pritimoy Sanyal (West Bengal University of Technology, India) Prof. J.Arunadevi (Thiagarajar School of Management, India) Dr. R.D. Sudhaker Samuel (S J College of Engineering, India) Dr. Amitabha Sinha (West Bengal University of Technology, India) Prof. Ananta Ojha (ICFAI University, India) Dr. A K Sharma (YMCA Institute of Engineering, India) Dr. Debasis Giri (IIT Kharagpur, India) Prof. Suparna Biswas (WBUT, India)
- 11. Foreword It’s a greatprivilege and pleasure for me to serve as the Editor-in-chief for the IDES Joint International Conferences. Innovative ideas and research in two streams are extremely important for the current Electronics industry to be in support to “Digital India” and “Make in India” an initiative by Government of India. This Joint International Conference provides a rostrum to the researchers from the academia and industries all around the world to share their research results, novel ideas as well as the improvements over the existing methodology. This conference covers a wide variety of topics in Control Systems and Power Electronics, to name a few—Mobile Communication Technology, Natural Language Processing, Algorithm/Protocol Design and Analysis, VLSI Systems, Intelligent Systems and Approach, Data Communication, Embedded System, Digital Security, Data Compression, Data Mining, Databases, Digital Signal Processing, Telecommunication Technologies, Control Theory and Application, Computational Intelligence, Robotics, HVDC, MEMS-Related Technology. The response from the researchers for the research papers is staggering. I would like to convey my heartfelt gratitude and appreciation to the members of the following committees— Honorary Chair, Technical Chair, Technical Co-Chair, General Chair, General Co-Chair, Publicity Chair, Publicity Co-Chair, Publication Chair, Finance Chair, NationalAdvisory Committees, Program Committee Chair, International Advisory Committee, Review Committee, Program Committee Members for contributing either their precious time in reviewing the papers or their effort in monitoring and making the conference a grand success. I would also like to acknowledge the support of IDES, Matha College of Technology, Association of Computer Electrical Electronics and Communication Engineers (ACEECom), ACEE and AMAE for organising such a platform to welcome the future technology. I also wish to convey my gratitude to McGraw-Hill Education for publishing the registered papers. Mahesh P K Don Bosco Institute of Technology, India
- 13. Preface The IDES JointInternational Conference “Emerging Technologies in Engineering” attracted several hundreds of submissions. After rigorous reviews, 63 high-quality papers are included in the proceedings. The goal of the joint conference is to promote research, developmental activities and scientific information interchange between researchers, developers, engineers, students, and practitioners working in India and around the world in the fields of Computer Science, Information Technology, Computational Engineering, Communication, Electrical Measurements, Instrumentation Engineering, Electronic Devices, Digital Electronics, Circuits, Control and Instrumentation, Communication system, Robotics, Power Electronics, Civil Engineering and Power Engineering. The conference is jointly organised by the ECE Department of Matha College of Technology, the IDES, ACEECom,ACEE andAMAE. I thank the members of the Organizing Committee and the Programming Committee for their hard working in the past several months. I wish to express my heartfelt appreciation to the keynote speakers, session chairs, reviewers and student helpers. Finally, I thank all the authors and participants for their great contributions and exchanging the experiences. Su-Qun Cao Huaiyin Institute of Technology, China
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- 16. Contents Committees v Foreword vii Prefaceix 1. Non-Word Error Detection for Luganda 1-17 Robert Ssali Balagadde and Parvataneni Premchand 2. Modern Approaches Towards Superior Dynamic Allocation 18-27 Snehal C Ratnaparkhi, Mithra Venkatesan and A V Kulkarni 3. Performance Analysis of AODV+ Routing Protocol for Wireless Ad-hoc Networks 28-35 Sanjeev Kumar Srivastava, Ranjana D Raut and P T Karule 4. Comparative Study of Various File Systems for Data Storage in Virtualized 36-44 Cloud Environment Rashmi Jogdand, Mohan A Gholap and D R Gangodkar 5. Study and Analysis of Modified Aggressive Packet Combining with 45-48 Consideration of the Physical Signal Achyuth Sarkar, Swarnendu K Chakraborty and C T Bhunia 6. Thyroid Malignancy Detection Using K-Nearest Neighbor (KNN) and 49-56 Probabilistic Neural Network (PNN) Wrushali Mendre, Ranjana Raut and Pradeep T Karule 7. Signature Imprintment on VLSI Core Designs 57-61 Saurabh Radheshyam Chiraniya, Pradeep Patil and A B Patki 8. Comparison of Network Codes in Distributed Storage Area Network based 62-68 on Performance using NS3 Simulator Priyanka Ramesh Nanaware and S K Srivastava 9. Optimal PI Tuning with GA and PSO for Shunt Active Power Filter 69-75 N Gowtham and Shobha Shankar 10. Optimal Control of DC Motor using Linear Quadratic Regulator 76-83 Meetty Tomy and A G Thosar 11. A Review on Implementation of UART with Optimum Noise Immunity 84-93 Pallavi R Upase and P S Choudhari
- 17. xii Contents 12. Introductionto Energy Efficient Motors 94-98 Himanshu Shekhar and C R Bharath 13. Computation of Steady State Temperature Distribution in the Stator of a 99-108 75 kW Slip-Ring Induction Motor by Finite Element Method Pradyumna Ranjan Ghosh, Nirmal Kr Bhattacharya and Debasis Sarkar 14. Parametric Study of Drag on Orientation of High Rise Building 109-114 Amol S Pote and Smrutirekha Mishra 15. An Overview of Developments in Adsorption Refrigeration Systems: 115-121 Principle and Optimization Techniques Jaimin K Patel, N S Mehta and J B Dabhi 16. Pedestrian and Vehicle Detection for Advanced Driver Assistance Systems 122-126 P Lavanya, G Harshith, Chiraag and S S Shylaja 17. Artificial Neural Network for Detection and Location of Faults in Mixed 127-136 Underground Cable and Overhead Transmission Line Ankita Nag and Anamika Yadav 18. Delineating Data Security and Privacy Boundary in Bioinformatics 137-141 Mukti Routray 19. Sentiment Analysis of Transliterated Hindi and Marathi Script 142-149 Mohammed Arshad Ansari and Sharvari Govilkar 20. An EOQ Model Dealing with Weibull Deterioration with Shortages, 150-163 Ramp Type Demand Rate and Unit Production Cost Incorporating the Effect of Inflation Jayshree and Shalini Jain 21. Parameter Centric XML Parsing Techniques for Embedded Systems 164-171 Rashmi Sonar, Sadique Ali and Amol Bhagat 22. Emotion Extraction using Rule-based and SVM-KNN Algorithm 172-181 Mohini Chaudhari and Sharvari Govilkar 23. Sentiment Classification in Hindi by using a HindiSentiWordNet (HSWN) 182-189 Pooja Pandey, Sharvari Govilkar and Sagar Kulkarni 24. Regression Analysis for Stock Market Prediction using Weka Tool Without 190-199 Sentiment Analysis Sudip Padhye and Karuna Gull 25. Component based Face Recognition using Hybrid Approach 200-208 R Srinivasa Perumal and Chandra Mouli P V S S R 26. Framework for Surplus Food Management using Data Analytics 209-214 M Sridevi and B R Arunkumar
- 18. Contents xiii 27. GroupKey Management Protocol: Secured Transmission in Compliant Groups 215-222 Amol Bhagat and Lovely Mutneja 28. A Scheduling Approach in Cloud Environment using Deadline and 223-228 VMs MIPS and Bandwidth Anamika and Pawan Luthra 29. Prioritized Schedule Algorithm : Time Table Generation 229-235 Rutuja Popale, Veeresh Hiremath, Nityanand B Nishanimath, Anil Rathod, Vandana Kale, G N Jayalaxmi and C Sujata 30. Handling Sink and Object Mobility in Wireless Sensor Networks 236-242 Kulwardhan Singh and T P Sharma 31. Instance based Multi Criteria Decision Model for Cloud Service Selection 243-251 using TOPSIS and VIKOR Deepti Rai and V Pavan Kumar 32. Part of Speech for TAGGER MARATHI Language 252-259 Shubhangi Rathod, Sharvari Govilkar and Sagar Kulkarni 33. Aadhaar based Secure E-Voting System using Cortex-A15 Processor 260-263 Prathiba Jonnala, Joshua Reginald Pullagura and Ashok Kumar Reddy K 34. An Secured and Energy Conserved Utilization Path Algorithm using Secret 264-275 Key and Adaptive Partition Controller in WSN K Ramanan and E Baburaj 35. Smart Watchmen with Home Automation System based on Raspberry Pi 276-281 Vanita Jain, Ashwani Sinhal and Saksham Jain 36. Big Data Analytics using Hadoop Collaborative Approach on Android 282-288 Altaf Shah, Amol Bhagat and Sadique Ali 37. Adaptive Dynamic Surface Control of Chaotic Micro-Electro-Mechanical 289-299 System with Unknown System Parameters and Dead-Zone Input Shaohua Luo, Suqun Cao, Zhong Chen and K Sujatha 38. Five Level Inverter Fed Squirrel Cage Induction Motor Drive with Reduced 300-306 Number of Power Elements R Harikrishnan, C N Ravi and S Vimala 39. Intelligent Web based Home Automation and Security System using 307-312 Raspberry PI R Harikrishnan, C N Ravi, Liji S Job and Rudra J Kumar 40. Enhancement of Smart Grid Performance through Logic based Fault Tolerant MPSoC 313-322 D Vijayakumar and V Malathi
- 19. xiv Contents 41. TheASVI - Assistance System for Visually Impaired 323-327 Arvind Prasad, H Govindsingh, R Hari Krishnan and C N Ravi 42. Biometric Recognition System (Algorithm) 328-335 Rahul Kumar Jaiswal and Gaurav Saxena 43. A Comprehensive Measurement Placement Method for Power System State Estimation 336-342 Rakesh J Motiyani and Ajitsinh R Chudasama 44. Voltage Control of DC to DC Converter in Integrated Renewable Energy 343-348 System through Fuzzy based GA K Harinadha Reddy, T Raghu and K P Prasad Rao 45. Placement of Synchronized Measurements in Power Networks for Redundant Observability 349-356 Satyendra Pratap Singh and S P Singh 46. Cationic Catalysis of Lime Treated Soft Soils 357-364 Greeshma Nizy Eujine, S Chandrakaran and N Sankar 47. Behaviour of Square Model Footing on Sand Reinforced with Woven Coir Geotextiles 365-371 Dharmesh Lal, N Sankar and S Chandrakaran 48. Upgradation of a Building to Higher Certification Levels as per LEEDv4 – Case Study 372-380 N Amrutha Sudhakar and S Shrihari 49. Advanced Oxidation Process (AOP) for Removing High Concentration of 381-386 Iron in Drinking Water Sources Lakshmy A Kumar and V Meera 50. Waste Footprint of Kochi City, Kerala – An Analysis 387-400 Athira Ravi and Subha Vishnudas 51. Effect of Infill on Performance of Steel Frame Buildings 401-407 Ashitha K Ikbal and Satyabrata Choudhury 52. A Study on Dynamic Shear Amplification in RC Frame Structures 408-417 Uday Sinha and Satyabrata Choudhury 53. Prediction of Compressive Strength for Different Curing Stages using Steepest Descent ANNs 418-421 K Rajasekhar and Gottapu Santosh Kumar 54. Study of Factors Affecting Labour Efficiency at Construction Sites 422-427 Purnima Bajpai, Megha Kalra and Garima Srivastava 55. Comparative Analysis of Motif Discovery Methods in Time Series 428-433 Sukriti Parnami and Veenu Mangat 56. Biometric Identification using Lip Imprint with Hybrid Feature Extraction Techniques 434-444 Semonti Chowdhury, Joydev Hazra and Satarupa Bagchi Biswas
- 20. Contents xv 57. RoutingProtocols in Wireless Local Area Networks (WLANs) 445-455 Sachin D Latkar and S A Patil 58. True Time based Simulation for AODV in MANET 456-462 L Usha and D K Kumuda 59. A Model to Enhance the Performance of Distributed File System for Cloud Computing 463-468 Pradheep Manisekaran and M R Ashwin Dhivakar 60. Fuzzy Logic Classification based Approach for Linear Time Series Analysis 469-476 in Medical Data Set Manish Pandey, Meenu Talwar, Sachin Chauhan and Gurinderjit Kaur 61. Design and Implementation of Cost Effective Controller for Solar PV Application 477-493 Pulkit Singh and D K Palwalia 62. A New Approach of Offline Parameters Estimation for Vector Controlled 494-504 Induction Motor Drive Krishna R More, P N Kapil and Hormaz Amrolia 63. A Comparative Study of Switching Strategies for Single Phase Matrix Converter 505-514 Mohammadamin Yusufji Khatri and Hormaz Amrolia
- 22. Non-Word Error Detectionfor Luganda Robert Ssali Balagadde * and Parvataneni Premchand ** * ** Department of Computer Science & Engineering, University College of Engineering, Osmania University Hyderabad, 500007, TS, India baross.kla@gmail.com, Profpremchand.p@gmail.com Abstract: Editing or word processing Luganda text has been an uphill task mainly because of lack of a system in this environment which could detect spelling errors in Luganda. In this context, this research paper presents a model for non-word error detection for Luganda (LugDetect) which comes in handy to address this gap and consequently provide a more user friendly environment for editing Luganda text. To the best of our knowledge LugDetect is the first of this kind of system developed for Luganda. Experimentation results show that LugDetect detects non-word errors with an accuracy (AP) of 100% for all the five categories of Luganda words at an average speed of 1471 Hz (number of words per second) so long as the erroneous word is not a real word. Experimentation on the Luganda corpus which was used in this research work shows that 19% of Luganda text is composed of Clitics Host Word Combination (CHWC), while the other part (81%)- Real Luganda Words (RLW). Keywords: non-word error detection, Luganda error detector, dictionary look-up technique, clitic-host word combination (CHWC), spelling detector. Introduction Editing or word processing Luganda text has been an uphill task mainly because of lack of a environmental system which could detect spelling errors in Luganda. In this context, this research work presents a model for Non-Word Error Detection For Luganda (NWEDL- LugDetect) which comes in handy to address this gap, and consequently, provide a more user friendly environment for editing Luganda text. To the best of our knowledge, LugDetect is the first of this kind of system developed for Luganda. One challenge encountered while developing a model for Luganda spell checking is dealing with the infinite number of clitic-host word combinations (CHWCs) which makes Luganda distinct from other foreign languages especially non-Bantu languages. In this research work, three types of CHWC are identified in respect to the use of inter-word apostrophe (IWA) defined in subsection on ”The Error Detection Mechanism" Type one - CHWC_1, bolded in Example 1 - are created as a result of compounding a modified monosyllabic word (MMW) or modified disyllabic word with an initial vowel (MDWIV) with another succeeding word which begins with a vowel. Compounding in Luganda may involve two to three words. MMW or MDWIV- referred to as clitic - is formed by dropping the ending vowel of the mono-syllabic word (MW) or disyllabic word with an initial vowel (DWIV) and replacing it with an apostrophe resulting into a long sound that is not represented by a double vowel. Example 1 � omwenge n'ennyama (alcohol and meat) [conjunctive form] � n'otema omuti (and you cut the tree) [narrative form] � ew'omuyizzi (at the hunter's place) [locative form] � Minisita omubeezi ow'ebyobusuubuzi ne tekinologiya (Minister of State for Trade and Technology) � n'obw'embwa sibwagala (even that for the dog, I don't like) � n'olw'ensonga eyo, sijja kujja (and for that reason, I will not come) Example 2 shows some MWs and DWIVs (bolded) used in compounding to form CHWC_Is. Not all MWs and DWIVs are used in compounding, and Example 3 shows some of these. Example 2 � ne, na, nga [conjunction] � ne, nga [narratives] � be, ze, ge. gwe , bwe, bye, lwe, lye, kye, bye [object relatives] � kya, ya, za. lya ba, bya, ga, gwa, ka, lwa, wa [possessives] � ekya, eya, eza. erya, owa, aba [possessives with initial vowel] � kye, ye, be, ze, twe [copulatives] � e [locatives]
- 23. 2 Sixth InternationalConference on Advances in Information Technology and Mobile Communication – AIM 2016 Example 3 � era, ate, nti, so [conjunction] � atya, oti [adverbs] � ggwe, nze, ye, yo. bo, zo, bwo [emphatic pronouns] � bba [nonn] � si [negation] Type II - CHWC_II, shown in Example 4 - are formed as a result of using clitic ng' and nng' to represent "����������������� double "��������������������������������������������������������������������� Example 4 � ng'ang'ala (to whimper like a dog) � bbiring'anya (egg plant) � agakung'anyizzaamu (to collect in it something) � enng'anda (relatives) � nng'oma (dram) Type III - CHWC_III, shown in Example 5 - are formed as a result of using clitic ng' and nng' to represent "����������������� double "��������������������ctively in words which are initially CHWC_1. Example 5 � n'agakung'anyizzaamu (and to collect in it something) � ng'eng'anda (like relatives) � ng'enng'oma (like a dram) In view of this, the first task is disambiguating the three types of CHWCs. The approach adopted to disambiguate these words in LugDetect is discussed in subsection on "Disambiguating the CHWCs" The second task is how to detect errors in each type of CHWCs. The approach adopted to provide this functionality in LugDetect is discussed in subsection on "Spell Checking Luganda Clitic Host Word Combination Type I (CHWC_I)". CHWC_IIs, which can be easy converted into real Luganda words (RLW) by substitution, are dealt with in subsection on "Spell Checking RLW". It is worthwhile noting that the CHWC_1 akin to Luganda are also found in other Bantu languages like Runyankore- Rukiga, Kinyarwanda, among others. Example 6 and Example 7 shows samples of CHWC_I extracted from Runyankore- Rukiga and Kinyarwanda text respectively. Example 6 � ky’okutunga � nk’eihanga � n’ekya � g’ebyemikono Example 7 � n’uw’Umukuru ( and His Excellence) � Nk’uko (like that) � y’Umukuru (His Excellence) � w’Igihugu (of a country) The third challenge is deciding when to invoke LugDetect or the Error Detection Mechanism (EDM) during interactive processing and during batch processing. For interactive processing, should LugDetect be invoked when the space bar (SB) is struck or at every key-stroke? The former approach has a shortfall in that a modified word can miss out on spell checking through the use of insertion bar (IB) movement keys - a list of which is shown in Table 1 - which move the position of IB from the modified word to some other word without striking the SB. The latter approach would eliminate this problem but its main problem is that it is strenuous on the system due to the numerous EDM invocations. A solution to this is the development of an Algorithm for Interactive Invocation of EDM (IIEDM) discussed in subsection on "Interactive invocation of EDM" For batch processing, how should EDM be invoked? A solution to this is discussed in form of an algorithm. A summary of the modules making up LugDetect is presented in subsection on "The Error Detection Mechanism" LugDetect can detect numerical errors and word level punctuation errors, however, in this paper, we only mention them. Details of the modules dealing with these errors can be obtained from "unpublished" [2].
- 24. Non-Word Error Detectionfor Luganda 3 The scope of LugDetect is limited to Luganda non-word detection. Named entities, abbreviations, e-mail addresses, uniform resource locator (URL) strings are not handled in LugDetect, and neither are the real word errors. Literature Survey Errors According to Peterson [15], spelling errors are basically of two types. Firstly, cognitive errors which are errors due to lack of knowledge of the language and are errors often ignored on the assertion that they are infrequent. In this context, their frequency is evaluated to be between 10% and 15% [17]. Secondly, typographical errors are 80% of "typos" (also called single character errors) and are one of the following four types: one exceeding character, one missing character, a mistaken character, or the transposition of two consecutive characters [4]. This means that 80% of errors are within edit distance of one and almost all errors - within edit distance of two. Error Detection There are two techniques used in detecting non-word errors. First, the Dictionary Lookup Technique (DLT), in which each word in the input text is lookup in the dictionary. In case a word is not found in the lexicon that word is a non-word or incorrect, and therefore, flagged or entered into the list of erroneous words. The larger the lexicon the better is the results. Shinghal and Toussaint [18] noted that DLT has low error rates, but is disadvantaged by large storage demands and high computational complexity. Most current spelling correction techniques rely on DLT for non-word error detection. Second, a technique that uses a non-positional bi-gram 26 by 26 matrix which captures information on the existence of the bi-grams. A bi-gram is assigned a value of one if it exists in the corpus of the language in consideration, otherwise it is assigned zero. All the bi-grams making up the input text are checked for their existence in the matrix. if any of the bi-grams is non-existence than the word is flagged or a non-word. This technique is appropriate for Optical Character Recognition (OCR) errors and it has proven to be less accurate for detecting human-generated errors. Lexicon User-lexicon can be interactively enriched with new entries enabling the checker to recognize all the possible inflexions derived from them. A lexicon for a spelling correction or text recognition application must be carefully tuned to its intended domain of discourse. Too small a lexicon can burden the user with too many false rejections of valid terms; too large a lexicon can result in an unacceptably high number of false acceptances. The relationship between misspellings and word frequencies is not straightforward. Peterson [16]. recommend that lexicon for spelling correction be kept relatively small based on the fact that approximately half a percent of all single error transformations of each of the words on a 350,000-item word list result in other valid words on the list However, Damerau and Mays [5] challenge this recommendation by using a corpus of over 22 million words of text from various genres and they found that by increasing the size of their frequency rank-ordered word list from 50,000 to 60,000 words; they were able to eliminate 1,348 false rejections while incurring only 23 additional false acceptances. Since this 50-to-l differential error rate represents a significant improvement in correction accuracy; therefore, they recommend the use of larger lexicons. Dictionaries alone are often insufficient sources for lexicon construction. Walker and Amsle [19] observed that nearly two-thirds (61%) of the words in the Merriam-Webster Seventh Collegiate Dictionary did not appear in an eight million word corpus of New York Times news wire text, and conversely, almost two-thirds (64.9%) of the words in the text were not in the dictionary. On the topic of construction of the lexicon for the spell program, McIlroy [13] provides some helpful insights into appropriate sources that may be drawn upon for general lexicon construction. An article by Damerau [6] provides some insights into and guidelines for the automatic construction and customization of domain oriented vocabularies for specialized NLP (Natural Language Processing) applications. LugDetect LugDetect, developed using Python programming language, identifies the type of Luganda word or token which need be spell checked, and consequently, invokes the appropriate module for detecting the error. LugDetect works in tandem with LugCorrect to provide detection and correction for Luganda words. In other words, LugDetect is required to detect the error before LugCorrect can be invoked for correction purposes. The mechanism for LugCorrect, which provides correction candidate list (CCL) or an explanation or hint on the type of error committed, is shown in Algorithm 1 and more details are provided in [3].
- 25. 4 Sixth InternationalConference on Advances in Information Technology and Mobile Communication – AIM 2016 The two models form LugSpell, a model for an interactive spell checker providing spelling feedback while the end user is word processing their document in the editor. However, in this article, more emphasis is directed towards LugDetect. The Luganda corpus which was experimented upon in the development of LugDetect was developed from Luganda text obtained from an online local news paper, Bukedde. Disambiguating the CHWCs Analysis of the task of disambiguating the three types of CHWC zeros down on disambiguating between the use of inter- word apostrophe (IWA) - which is defined in subsection on "The Error Detection Mechanism" - in Luganda compounding resulting into creation of CHWC_1 and the use of IWA in representing the character "��� �������� ��� ����le or double character which results into formation of CHWC_II and CHWC_III. At the bottom line of the analysis, we finally arrive at disambiguating between clitic ng' used in the representation of character '��������������- shown in Example 4 - and in Get Erroneous Word IF{punctuation Error} Give feedback on punctuation error ELSE IF{Numeric Error } Give feedback on numeric error ELSE IF (Word Error) Generate correction candidate list (GCCL) ENDIF GCCL SubDic=Get all words beginning with 1st character of erroneous word from lexicon L=Number of words in SubDic IF {L>10} Get 1st 10 words from SubDic load in Dictionary with their corresponding Jaccard coefficient(JC) ELSE Get all words in SubDic load in Dictionary with their corresponding JC ENDIF REPEAT swap if JC of word in SubDic is greater than minimum JC of word in dictionary UNTILL all words in SubDic have been checked Sort words in Dictionary in descending order of JC Present the word to the user for selection IF{user selects Dictionary word and gives consent} Change word in text ELSE Ignore ENDIF Algorithm 1 : Error Correction Module
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- 27. Exploring the Varietyof Random Documents with Different Content
- 28. Pag. 5 9 29 55 73 97 INDICE-SOMMARIO Prefazione Capitolo I. IlProvicario Apostolico dell'Africa Centrale Ignazio Knoblecher — Ultime sue parole in Koròsko ai Missionari veronesi — Le rive del fiume Bianco da Chartùm ai Scìluk — Le meraviglie di una foresta — Gli Arabi d'Abù-Zèt — I Baggàra-Selèm — Linguaggio mimico degli Arabi. Capitolo II. Vendette — Guerre — Armi — Coraggio passivo e fierezza — Ostinazione degli Arabi — Il suicidio — Le montagne dei Dénka — Il Tarciàm. Capitolo III. Caratteristiche della razza negra — Il paese dei Scìluk — I cani — Odio contro i Turchi — Raffronti della lingua dei Dénka con quella dei Scìluk. Capitolo IV. Il regno dei Scìluk e il loro Governo — Mezzi d'incivilimento — Punizioni — Diritto di elezione al trono — Residenza reale — Quanto si possa fare assegnamento della parola di un re Negro — Il latrocinio — Divisione, carattere e costumi vari dei Scìluk — La schiavitù presso i Scìluk e gli Arabi in Hèllat-Kàka — I mercanti d'avorio divenuti rapitori e mercanti di schiavi. Capitolo V.
- 29. 147 179 195 227 249 271 Una zerìbah dischiavi — L'asta, la vendita, la separazione — Le dieci schiave Abialàñġ rapite a tradimento — Il loro quartiere in Hèllat-Kàka — Scena commoventissima — Brutto rischio — Audaces fortuna juvat — Uno de' più bei giorni della mia vita — Diffidenza punita — Il tradimento. Capitolo VI. Il fiume Jâl — Il Sóbat e i suoi abitanti — Affluenti del Sóbat e i Negri scìluk — Dall'imboccatura del Sóbat al lago No — Il Bàhr-el-G¨azàl e i suoi affluenti — I Gnam-Gnàm; etimologia del nome e cannibalismo di questi popoli. Capitolo VII. I Nuèr del fiume Bianco — Fuochi notturni — Formiche — Zanzare — Cinocèfali, mostri, cannibali — Credenze religiose; indovini (Kogiùr); superstizioni, strani abbigliamenti e caccia dei Nuèr. Capitolo VIII. Bachìta la schiava. Capitolo IX. Le tribù Dénka della vallata superiore del fiume Bianco e la loro lingua — Stagioni e loro nomi — Il charìf — Una bufera — La stagion delle piogge, ed accrescimento e decrescimento del fiume sotto latitudini diverse — Morte di Francesco Oliboni — Un sogno. Capitolo X. La stazione di Santa-Croce — La caccia — Il barone De- Harnier vittima di un bufalo selvatico — Le tribù Dénka della vallata superiore del Nilo, e i loro costumi. Capitolo XI.
- 30. 297 Forme di salutopresso i Dénka — Matrimoni — Religione — Buffoni — Dialogo sulla schiavitù e sul diritto di punizione — Partenza da Santa-Croce verso Chartùm; stato della Missione; morte del missionario Angelo Melotto; ritorno a Santa-Croce. Capitolo XII. Da Santa-Croce a Kondókoro — Costumi dei Bàri — Tremuoti, venti, temperatura — I Bàri e la Missione — L'islamismo e la schiavitù — Conclusione.
- 31. ALTRE PUBBLICAZIONI DELLASTESSA DITTA EDITRICE ALEARDI A. — Epistolario con un'introduzione del prof. G. Trezza. 1 vol. in-12, 1879 L. 5 — BELTRAME prof. G. — Il Sènnaar e lo Sciangàllah; 2 vol. con ritratto dell'Autore ed una carta geog. 8 — BERTINI P. — Tutto pel meglio. Racc. 1 vol. in-12 3 — CALIARI prof. P. — La donna cristiana. Conferenze. 1 vol. in-12, 1879 1 50 CHAVASSE P. H. — Sull'educazione fisica dei bambini. Consigli di un medico alle madri. Trad. dalla XII ediz. inglese di C. Ruata-Pronati. 1 vol. 4 — FRIZZO prof. G. — La Geometria per le Scuole Tecniche secondo i nuovi programmi 2 60 GIULIARI M.r G. C. — Monumenti grafici affidati al marmo, al bronzo, al papiro, alla pergamena e alla carta, relativi alla storia di Verona, 1 op. 2 — GUERZONI prof. G. — Il terzo rinascimento. Corso di letteratura italiana dato nella Regia Università di Palermo, II ediz. 1 vol. in-12 di pag. 560 4 50 — Il primo rinascimento, 1 vol. in-12 di pag. 220 3 — LEBRECHT dott. G. — Il Risparmio e la educazione del popolo. Studio sulle Casse di Risparmio italiane ed estere. 1 vol. in-12 di pag. 455 5 — MANGANOTTI prof. A. — Manuale di storia naturale applicata al commercio, 1 vol. in-8 4 — MATTEAZZI E. — Doveri morali della giovinetta italiana. 1 vol. in-12 1 — PATUZZI G. L. — A proposito dei Pensieri sull'Arte e Ricordi Autobiografici di Giov. Duprè. 1 op. 1 —
- 32. PEREZ. — Isette cerchi del Purgatorio di Dante. 1 vol. in-12 3 — SALA A. — I Musicisti veronesi. Saggio storico critico. 1 opusc. in-12 1 — SCOPOLI-BIASI I. — La tavola rotonda. Racconti educativi, 1 vol. in-12, 1877 di pag. 182 2 — DI PROSSIMA PUBBLICAZIONE: BELVIGLIERI C. — Scritti storici. — 1 volume in-12. VERONA 1881 — STAB. G. CIVELLI.
- 33. NOTE: 1. Vedi miememorie: Il Sènnaar e lo Sciangàllah, vol. II, pag. 51. 2. Vedi mie memorie: Il Sènnaar e lo Sciangàllah, vol. I, pag. 87, e vol. II, pag. 251. 3. Vedi mie memorie: Il Sènnaar e lo Sciangàllah, vol. I, pag. 201, 212, 244. 4. Questo illustre naturalista e ardito viaggiatore italiano fu spedito nel 1825 nel Sènnaar dal vicerè dell'Egitto Mahàmmed-Aly per istudiarvi il terreno; ma troppo presto e con grave danno della scienza cadde vittima dell'insalubrità del clima a Chartùm nel settembre 1826. Venne seppellito nel deserto a mezzodì delle poche capanne, di cui allora si componeva Chartùm; ed ora quella parte di deserto è occupata dagli abitati della città ingrandita. Io feci di tutto per rinvenirne le ossa, assistito dal dottore Peney ispettore sanitario del Sudàn, e da un vecchio greco, per nome Dimitri, che aveva conosciuto il Brocchi ed era stato presente alla sepoltura; ma indarno. Io dovetti contentarmi di far porre nel cimitero cristiano una pietra con iscrizione, la quale almeno indicasse il nome dell'illustre bassanese. 5. Vedi mie memorie: Il Sènnaar e lo Sciangàllah, vol. II, pag. 102. 6. Pianta, del cui fusto che è leggerissimo si servono le donne per passare dall'una all'altra riva del fiume. 7. Vedi mie memorie: Il Sènnaar e lo Sciangàllah, vol. I, pag. 19. 8. Viaggio sul fiume Bianco, Verona, Tipografia Vicentini e Franchini, 1861. 9. Expedition zur Entdeckung der Quellen des weissen Nils. Werne, Berlin, 1848. 10. Vedi mie memorie: Il Sènnaar e lo Sciangàllah, vol. I, pag. 266-270.
- 34. 11. Il paesedei Scìr è posto sulle rive del fiume Bianco tra il 5º e il 6º gr. Lat. N. a settentrione dei Bàri, coi quali confina. 12. Vedi mie memorie: Il Sènnaar e lo Sciangàllah, vol. I, pag. 215. 13. Vedi mie memorie «Il Sènnaar e lo Sciangallàh.» Vol. I, pag. 217. 14. Pochi giorni dopo la stampa di questa pagina, il vescovo Daniele Comboni mi scriveva da Chartùm in data 12 marzo 1881. «.... Cher-Allàh, il vostro turcimanno Cher-Allàh, che tanto vi ha assistito a trar fuori dalle ombre la lingua dei Dénka, è morto poco tempo fa. Voi, don Giovanni, che avete conosciuta la vita di questo incomparabile cristiano, sappiate che com'egli visse da santo, da santo morì.» 15. Vedi mie memorie: Il Sènnaar e lo Sciangàllah, vol. I, pag. 213, e vol. II, pag. 183. 16. Vedi mie memorie: Il Sènnaar e lo Sciangàllah, vol. I, pag. 211, e vol. II, pag. 182. 17. Vedi mia relazione, 13 dicembre 1859, Viaggio sul fiume Bianco. — Verona, Tipografia Vicentini e Franchini, 1861. 18. Vedi «Le fleuve Blanc, notes géographiques et ethnologiques» — Paris. Arthus Bertrand, editeur, libraire de la société de géographie, 21, rue Hautefeuille. 19. Vedi mia relazione, 13 dicembre 1859, Viaggio sul fiume Bianco. — Verona, tipografia Vicentini e Franchini, 1861, con carta lit. Penuti. 20. Vedi mia relazione, 13 dicembre 1859, Viaggio sul fiume Bianco. — Verona, tipografia Vicentini e Franchini, 1861, con carta lit. Penuti. 21. Il nome di detta tribù tanto dai fratelli Poncet che dal Lejean venne scritto sempre Nìam-Niàm. 22. E qui si noti che sono i soli Dénka e i Bàri che chiamano con questo nome quella tribù, e che sì gli uni che gli altri per esprimere gran quantità di una cosa, ripetono il nome della cosa stessa due e anche tre volte. Di fatto i Gnam-Gnàm dei Dénka e dei Bàri dànno a sè stessi il nome di Zandèh; «e i Bòngo del nord li chiamano Mùndo e talvolta Maniània (Magniàgnia?); e dai Mittù dell'est sono detti Makarakkà; dai Gòlo, Kùnda; e i Mumbuttù li denominano Babùnghera.» (Vedi Giorgio Schweinfurth, nel cuore dell'Africa, vol. II, p. 3, ove dice pure; «il nome
- 35. sotto il qualeli conosciamo, e che vuol essere pronunziato Gnam-Gnàm (così), è preso dalla lingua dénka (così), e significa mangiatore, o meglio gran mangiatore, e allude evidentemente (egli dice) al cannibalismo della gente cui è imposto;» — significato ed allusione, a cui io non posso sottoscrivere.) 23. Vedi mie memorie: Il Sènnaar e lo Sciangàllah, vol. II, pag. 164. 24. Vedi mie memorie: Il Sènnaar e lo Sciangàllah, vol. II, pag. 193. 25. Vedi mie memorie: Il Sènnaar e lo Sciangàllah, vol. I, pag. 266, 268. 26. Isaia, cap. XLII, salmo LXXII. 27. Il nome di questa tribù, ch'io trovo scritto quasi sempre sulle carte geografiche a destra del fiume, deve essere posto a sinistra. Le stabili abitazioni dei Kìc, ch'io visitai nel 1858 col missionario Comboni, ora Vescovo di Claudiopoli e Vicario Apostolico dell'Africa centrale, sono tutte a sinistra del fiume ad una giornata circa di cammino, e non è che durante la stagione secca, che per la comodità dei pascoli i Kìc s'appressano al fiume provvisoriamente lungo la riva sinistra, passando alcuni anche sulla riva destra. 28. Vedi mie memorie: Il Sènnaar e lo Sciangàllah, vol. I, p. 243. 29. Vedi mie memorie: Il Sènnaar e lo Sciangàllah, vol. I, pag. 35. 30. Vedi mie memorie: Il Sènnaar e lo Sciangàllah, vol. I, pag. 120. 31. Le fleuve Blanc. — Notes géographiques et ethnologiques et les chasses à l'élephant dans le pays des Dénka et des Djour. — Paris, Arthus Bertrand éditeur. 32. Vedi mia Gram. dénka, cap. IV, § 37. 33. In tutto il tempo che la Missione si sostenne con enormi spese e con grandissime difficoltà, cioè dall'anno 1852 al 1860, i battezzati furono 47; n. 34 dal Provicario Apostolico Ignazio Knoblecher: n. 12 dal Presidente Antonio Überbacher, dieci dei quali per ordine del medesimo Provicario; e n. 1 dal missionario Antonio Kaufman. 34. Quest'ultimo giovinetto dopo due anni mi comparve, quasi per incanto, nel Cairo mentre tornavo in Europa, essendo stata la Missione Cattolica dell'Africa Centrale affidata ai Padri Francescani. Egli fece meco il viaggio a Terra Santa, a Costantinopoli, a Vienna; e giunti a Verona, egli venne
- 36. battezzato col nomedi Michele nella chiesa di S. Paolo di Campo Marzo, e gli fu padrino l'illustre comm. conte Antonio Pompei. Dopo cinque anni, affetto d'infiammazione polmonare, ritornò in Cairo, a spese dell'amato suo padrino, nella speranza che si potesse rimettere in salute; qui fu accolto amorosamente nel convento dei Padri Francescani, ove dopo un anno morì. Un'indole così dolce, così facile, così semplice, così buona come nel mio caro Michele, non la riscontrai che nel mio turcimanno Cher-Allàh, tutti e due veri tipi di bontà, profondamente cristiani. — E quali vissero, così morirono nella pace, tranquilli e contenti. La morte non è nulla, o cari, per coloro che v'assomigliano! per essi non vi sono nè tenebre, nè ombre, che si dileguano come la stella del mattino sotto i raggi dorati dell'aurora.... Voi otteneste la vittoria senza combattimento, e la corona senza lotta!... Or pregate in cielo per chi ha fatto qualche cosa per voi!... 35. Giorgio Schweinfurth. Nel cuore dell'Africa, vol. II, cap. XIV, pag. 179.
- 37. Nota del Trascrittore Ortografiae punteggiatura originali sono state mantenute, correggendo senza annotazione minimi errori tipografici. Nel testo originale sono presenti segni diacritici (accenti, legature, punti sovrascritti) per rappresentare foneticamente le parole della lingua dénka. In particolare, la legatura è usata per segnalare il suono della lettera n unita ad altre lettere: è stata qui resa tramite la tilde (ñ).
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