NAMED ENTITY RECOGNITION IN TURKISH USING ASSOCIATION MEASURESacijjournal
Named Entity Recognition which is an important subject of Natural Language Processing is a key technology of information extraction, information retrieval, question answering and other text processing applications. In this study, we evaluate previously well-established association measures as an initial
attempt to extract two-worded named entities in a Turkish corpus. Furthermore we propose a new association measure, and compare it with the other methods. The evaluation of these methods is performed by precision and recall measures.
This document describes a morphological tagger for Korean developed by Chung-Hye Han and Martha Palmer. The tagger takes raw text as input and outputs each word labeled with its lemma and part-of-speech tag, and inflections labeled with inflectional tags. Unlike prior approaches, this tagger performs statistical tagging before morphological analysis. It uses a trigram tagger followed by applying morphological rules to tag unknown words, achieving 95% accuracy on test data.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
The important problem of word segmentation in Thai language is sentential noun phrase. The existing
studies try to minimize the problem. But there is no research that solves this problem directly. This study
investigates the approach to resolve this problem using conditional random fields which is a probabilistic
model to segment and label sequence data. The results present that the corrected data of noun phrase was
detected more than 78.61 % based on our technique.
STATISTICAL FUNCTION TAGGING AND GRAMMATICAL RELATIONS OF MYANMAR SENTENCEScscpconf
This paper describes a context free grammar (CFG) based grammatical relations for Myanmar
sentences which combine corpus-based function tagging system. Part of the challenge of
statistical function tagging for Myanmar sentences comes from the fact that Myanmar has freephrase-order
and a complex morphological system. Function tagging is a pre-processing step to
show grammatical relations of Myanmar sentences. In the task of function tagging, which tags
the function of Myanmar sentences with correct segmentation, POS (part-of-speech) tagging
and chunking information, we use Naive Bayesian theory to disambiguate the possible function
tags of a word. We apply context free grammar (CFG) to find out the grammatical relations of
the function tags. We also create a functional annotated tagged corpus for Myanmar and propose the grammar rules for Myanmar sentences. Experiments show that our analysis achieves a good result with simple sentences and complex sentences.
PARSING OF MYANMAR SENTENCES WITH FUNCTION TAGGINGkevig
This paper describes the use of Naive Bayes to address the task of assigning function tags and context free
grammar (CFG) to parse Myanmar sentences. Part of the challenge of statistical function tagging for
Myanmar sentences comes from the fact that Myanmar has free-phrase-order and a complex
morphological system. Function tagging is a pre-processing step for parsing. In the task of function tagging, we use the functional annotated corpus and tag Myanmar sentences with correct segmentation, POS (part-of-speech) tagging and chunking information. We propose Myanmar grammar rules and apply context free grammar (CFG) to find out the parse tree of function tagged Myanmar sentences. Experiments
show that our analysis achieves a good result with parsing of simple sentences and three types of complex sentences.
The document summarizes LIMSI's participation in the QA4MRE 2013 question answering competition. It tested two methods: 1) exploiting discourse relations to answer complex questions like causal questions, and 2) using semantic variations from Wiktionary definitions to index documents and select passages for the entrance exams task. Relation recognition showed promise but needs improvement to impact answer selection. The two passage selection methods are described, as well as how answers are ranked based on question category and detected discourse relations. Results of the experiments are presented.
NAMED ENTITY RECOGNITION IN TURKISH USING ASSOCIATION MEASURESacijjournal
Named Entity Recognition which is an important subject of Natural Language Processing is a key technology of information extraction, information retrieval, question answering and other text processing applications. In this study, we evaluate previously well-established association measures as an initial
attempt to extract two-worded named entities in a Turkish corpus. Furthermore we propose a new association measure, and compare it with the other methods. The evaluation of these methods is performed by precision and recall measures.
This document describes a morphological tagger for Korean developed by Chung-Hye Han and Martha Palmer. The tagger takes raw text as input and outputs each word labeled with its lemma and part-of-speech tag, and inflections labeled with inflectional tags. Unlike prior approaches, this tagger performs statistical tagging before morphological analysis. It uses a trigram tagger followed by applying morphological rules to tag unknown words, achieving 95% accuracy on test data.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
The important problem of word segmentation in Thai language is sentential noun phrase. The existing
studies try to minimize the problem. But there is no research that solves this problem directly. This study
investigates the approach to resolve this problem using conditional random fields which is a probabilistic
model to segment and label sequence data. The results present that the corrected data of noun phrase was
detected more than 78.61 % based on our technique.
STATISTICAL FUNCTION TAGGING AND GRAMMATICAL RELATIONS OF MYANMAR SENTENCEScscpconf
This paper describes a context free grammar (CFG) based grammatical relations for Myanmar
sentences which combine corpus-based function tagging system. Part of the challenge of
statistical function tagging for Myanmar sentences comes from the fact that Myanmar has freephrase-order
and a complex morphological system. Function tagging is a pre-processing step to
show grammatical relations of Myanmar sentences. In the task of function tagging, which tags
the function of Myanmar sentences with correct segmentation, POS (part-of-speech) tagging
and chunking information, we use Naive Bayesian theory to disambiguate the possible function
tags of a word. We apply context free grammar (CFG) to find out the grammatical relations of
the function tags. We also create a functional annotated tagged corpus for Myanmar and propose the grammar rules for Myanmar sentences. Experiments show that our analysis achieves a good result with simple sentences and complex sentences.
PARSING OF MYANMAR SENTENCES WITH FUNCTION TAGGINGkevig
This paper describes the use of Naive Bayes to address the task of assigning function tags and context free
grammar (CFG) to parse Myanmar sentences. Part of the challenge of statistical function tagging for
Myanmar sentences comes from the fact that Myanmar has free-phrase-order and a complex
morphological system. Function tagging is a pre-processing step for parsing. In the task of function tagging, we use the functional annotated corpus and tag Myanmar sentences with correct segmentation, POS (part-of-speech) tagging and chunking information. We propose Myanmar grammar rules and apply context free grammar (CFG) to find out the parse tree of function tagged Myanmar sentences. Experiments
show that our analysis achieves a good result with parsing of simple sentences and three types of complex sentences.
The document summarizes LIMSI's participation in the QA4MRE 2013 question answering competition. It tested two methods: 1) exploiting discourse relations to answer complex questions like causal questions, and 2) using semantic variations from Wiktionary definitions to index documents and select passages for the entrance exams task. Relation recognition showed promise but needs improvement to impact answer selection. The two passage selection methods are described, as well as how answers are ranked based on question category and detected discourse relations. Results of the experiments are presented.
A survey on phrase structure learning methods for text classificationijnlc
Text classification is a task of automatic classification of text into one of the predefined categories. The
problem of text classification has been widely studied in different communities like natural language
processing, data mining and information retrieval. Text classification is an important constituent in many
information management tasks like topic identification, spam filtering, email routing, language
identification, genre classification, readability assessment etc. The performance of text classification
improves notably when phrase patterns are used. The use of phrase patterns helps in capturing non-local
behaviours and thus helps in the improvement of text classification task. Phrase structure extraction is the
first step to continue with the phrase pattern identification. In this survey, detailed study of phrase structure
learning methods have been carried out. This will enable future work in several NLP tasks, which uses
syntactic information from phrase structure like grammar checkers, question answering, information
extraction, machine translation, text classification. The paper also provides different levels of classification
and detailed comparison of the phrase structure learning methods.
THE ABILITY OF WORD EMBEDDINGS TO CAPTURE WORD SIMILARITIESkevig
Distributed language representation has become the most widely used technique for language representation in various natural language processing tasks. Most of the natural language processing models that are based on deep learning techniques use already pre-trained distributed word representations, commonly called word embeddings. Determining the most qualitative word embeddings is of crucial importance for such models. However, selecting the appropriate word embeddings is a perplexing task since the projected embedding space is not intuitive to humans.In this paper, we explore different approaches for creating distributed word representations. We perform an intrinsic evaluation of several state-of-the-art word embedding methods. Their performance on capturing word similarities is analysed with existing benchmark datasets for word pairs similarities. The research in this paper conducts a correlation analysis between ground truth word similarities and similarities obtained by different word embedding methods.
Novelty detection via topic modeling in research articlescsandit
In today’s world redundancy is the most vital problem faced in almost all domains. Novelty
detection is the identification of new or unknown data or signal that a machine learning system
is not aware of during training. The problem becomes more intense when it comes to “Research
Articles”. A method of identifying novelty at each sections of the article is highly required for
determining the novel idea proposed in the research paper. Since research articles are semistructured,
detecting novelty of information from them requires more accurate systems. Topic
model provides a useful means to process them and provides a simple way to analyze them. This
work compares the most predominantly used topic model- Latent Dirichlet Allocation with the
hierarchical Pachinko Allocation Model. The results obtained are promising towards
hierarchical Pachinko Allocation Model when used for document retrieval.
Statistical Named Entity Recognition for Hungarian – analysis ...butest
This document describes statistical named entity recognition for Hungarian texts. The authors created a corpus of Hungarian news articles annotated with named entity tags. They used a rich set of 225 linguistic features to train support vector machines, neural networks, and decision trees. Their best model achieved an F-measure of 93.59% for term-level named entity recognition and 90.57% for phrase-level, outperforming prior rule-based systems for Hungarian. Feature selection helped reduce the feature set to 135 while maintaining high performance.
Integrating Incoming Information into Discourse Model in Tunisian ArabicDr. Marwa Mekni-Toujani
There are two main lines in discourse processing research. The first one is interested in understanding the type of inferences that constitute discourse representations (logical inferences, bridging inferences, elaborative inferences, predictive inferences, etc.) (Stewart, Kidd, & Haigh, 2009). The second line is interested in the time course of integrating incoming information with the unfolding discourse model (ibid). This study addressed the second line of research. Incoming information can be integrated as soon as it is available (early integration model) or it is integrated later as a wrap-up operation (delayed integration model) (Guzman & Klin, 2000). That is, the endeavor of the present study was to gauge the time course of connecting incoming information to information mentioned earlier in the text that are no longer available in Working Memory (WM). Additionally, There are some factors that are believed to affect the time course of the generation of discourse-level representations. In fact, Hannon & Daneman (2001) argue that cognitive styles can influence the ability to detect anomalies. Concerning task demands, it is argued that some instructions require different strategies by the reader (Smith & O’Brien, 2012). Hence, this study explored the effects of both field-dependency and task demands. Ultimately, the present study aspired to answer the following research questions: (1) does readers’ sensitivity to spatial anomaly affect the time-course of integrating incoming information into the unfolding discourse model in Tunisian Arabic (TA)? (2) do field dependency and task demands affect the time-course of integration in TA?
A COMPARATIVE STUDY OF FEATURE SELECTION METHODSkevig
This article focuses on evaluating and comparing the available feature selection methods in general versatility regarding authorship attribution problems and tries to identify which method is the most effective. The discussions on general versatility of feature selection methods and its connection in selecting the appropriate features for varying data were done. In addition, different languages, different types of features, different systems for calculating the accuracy of SVM (support vector machine), and different criteria for determining the rank of feature selection methods were used to measure the general versatility of these methods together. The analysis results indicate the best feature selection method is different for each dataset; however, some methods can always extract useful information to discriminate the classes. The chi-square was proved to be a better method overall.
This document discusses applying theory revision techniques to automatically improve a heuristic-based algorithm for designing distributed databases. The algorithm decides which fragmentation technique to use for each database class. Theory revision is used to revise the algorithm's heuristics based on examples of previously tested fragmentation schemas and their performance. The revised algorithm is incorporated back into the design framework to produce improved fragmentation schemas with better performance.
Taxonomy extraction from automotive natural language requirements using unsup...ijnlc
In this paper we present a novel approach to semi-automatically learn concept hierarchies from natural
language requirements of the automotive industry. The approach is based on the distributional hypothesis
and the special characteristics of domain-specific German compounds. We extract taxonomies by using
clustering techniques in combination with general thesauri. Such a taxonomy can be used to support
requirements engineering in early stages by providing a common system understanding and an agreedupon
terminology. This work is part of an ontology-driven requirements engineering process, which builds
on top of the taxonomy. Evaluation shows that this taxonomy extraction approach outperforms common
hierarchical clustering techniques.
KANNADA NAMED ENTITY RECOGNITION AND CLASSIFICATIONijnlc
Named Entity Recognition and Classification (NERC) is a process of identification of proper nouns in the text and classification of those nouns into certain predefined categories like person name, location,organization, date, and time etc. NERC in Kannada is an essential and challenging task. The aim of this work is to develop a novel model for NERC, based on Multinomial Naïve Bayes (MNB) Classifier. The Methodology adopted in this paper is based on feature extraction of training corpus, by using term frequency, inverse document frequency and fitting them to a tf-idf-vectorizer. The paper discusses the
various issues in developing the proposed model. The details of implementation and performance evaluation are discussed. The experiments are conducted on a training corpus of size 95,170 tokens and test corpus of 5,000 tokens. It is observed that the model works with Precision, Recall and F1-measure of
83%, 79% and 81% respectively.
A comparative study on term weighting methods for automated telugu text categ...IJDKP
Automatic Text categorization refers to the process of assigning a category or some categories
automatically among predefined ones. Text categorization is challenging in Indian languages has rich in
morphology, a large number of word forms and large feature spaces. This paper investigates the
performance of different classification approaches using different term weighting approaches in order to
decide the most applicable one to Telugu text classification problem. We have investigated on different
term weighting methods for Telugu corpus in combination with Naive Bayes ( NB), Support Vector
Machine (SVM) and k Nearest Neighbor (kNN) classifiers.
Recognition of Words in Tamil Script Using Neural NetworkIJERA Editor
In this paper, word recognition using neural network is proposed. Recognition process is started with the partitioning of document image into lines, words, and characters and then capturing the local features of segmented characters. After classifying the characters, the word image is transferred into unique code based on character code. This code ideally describes any form of word including word with mixed styles and different sizes. Sequence of character codes of the word form input pattern and word code is a target value of the pattern. Neural network is used to train the patterns of the words. Trained network is tested with word patterns and is recognized or unrecognized based on the network error value. Experiments have been conducted with a local database to evaluate the performance of the word recognizing system and obtained good accuracy. This method can be applied for any language word recognition system as the training is based on only unique code of the characters and words belonging to the language.
This paper proposes Natural language based Discourse Analysis method used for extracting
information from the news article of different domain. The Discourse analysis used the Rhetorical Structure
theory which is used to find coherent group of text which are most prominent for extracting information
from text. RST theory used the Nucleus- Satellite concept for finding most prominent text from the text
document. After Discourse analysis the text analysis has been done for extracting domain related object
and relates this object. For extracting the information knowledge based system has been used which
consist of domain dictionary .The domain dictionary has a bag of words for domain. The system is
evaluated according gold-of-art analysis and human decision for extracted information.
The document provides a survey of word sense disambiguation (WSD) research. It discusses the history and applications of WSD, and categorizes the main WSD approaches as knowledge-based, supervised, and unsupervised. For each category, it outlines several common algorithms used, such as Lesk algorithm, decision trees, Naive Bayes, and support vector machines. The document surveys the state-of-the-art in WSD performance and compares different algorithm types. It also provides an overview of WSD research in Indian languages.
In this paper, we presented a method to retrieve documents with unstructured text data written in different
languages. Apart from the ordinary document retrieval systems, the proposed system can also process
queries with terms in more than one language. Unicode, the universally accepted encoding standard is used
to present the data in a common platform while converting the text data into Vector Space Model. We got
notable F measure values in the experiments irrespective of languages used in documents and queries.
Ontologisms have been applied to many applications in recent years, especially on Sematic Web, Information
Retrieval, Information Extraction, and Question and Answer. The purpose of domain-specific ontology
is to get rid of conceptual and terminological confusion. It accomplishes this by specifying a set of generic
concepts that characterizes the domain as well as their definitions and interrelationships. This paper will
describe some algorithms for identifying semantic relations and constructing an Information Technology
Ontology, while extracting the concepts and objects from different sources. The Ontology is constructed
based on three main resources: ACM, Wikipedia and unstructured files from ACM Digital Library. Our
algorithms are combined of Natural Language Processing and Machine Learning. We use Natural Language
Processing tools, such as OpenNLP, Stanford Lexical Dependency Parser in order to explore sentences.
We then extract these sentences based on English pattern in order to build training set. We use a
random sample among 245 categories of ACM to evaluate our results. Results generated show that our
system yields superior performance.
Semantic based automatic question generation using artificial immune systemAlexander Decker
The document describes a system that uses artificial immune systems and natural language processing techniques like semantic role labeling and named entity recognition to automatically generate questions from text. It introduces a model that applies these techniques to extract semantic patterns from sentences, trains a classifier using artificial immune systems to classify question types, and then generates questions by matching patterns. The system was tested on sentences from various sources and showed promising results, correctly determining question types 95% of the time and generating matching questions 87% of the time.
Este documento presenta información sobre tres idiomas: español, inglés y francés. Resume brevemente el origen y difusión del español como lengua romance originaria de Castilla, España. Explica que el inglés surgió en Inglaterra y se extendió a Escocia, convirtiéndose en un idioma global debido al imperio británico. Finalmente, describe al francés como lengua hablada originalmente en Francia y extendida a través del segundo imperio colonial francés.
Este documento presenta un resumen biográfico de Oscar Wilde. Nació en 1854 en Dublín en el seno de una familia intelectual. Mostró inteligencia desde temprana edad al aprender varios idiomas. Estudió en prestigiosas universidades donde recibió premios por su poesía. Se destacó como portavoz del esteticismo a través de conferencias y publicaciones. Fue conocido por su ingenio y personalidad extravagante.
Glenn M. Albers has over 20 years of experience leading operations and strategic planning in various industries. He has a proven track record of growing companies through acquisitions, improving processes, and reducing costs. Albers is skilled in strategic planning, budgeting, change management, and team leadership. He holds an MBA from Oklahoma State University and a BA from Texas A&M University.
The document provides guidance on developing an effective Individual Professional Growth Plan (IPGP). An IPGP is a living document that establishes goals to directly improve student achievement. It should be reviewed annually with input from supervisors and colleagues. Effective IPGPs balance the needs of students, staff, schools and districts while focusing on student learning. Goals should be specific, measurable, attainable, results-oriented and time-bound (SMART) to improve teaching practices and increase student achievement. The document outlines the key components and development process for an effective IPGP.
Packaging Logistics offers a line of unique premium shaped glass bottles, bar tops, and closures for spirits, wine, food and beverage products. Their bottles are made and stocked in the USA and offer beautiful aesthetics and value-added services like printing, engraving, and freight. The document provides details on bottle capacities, neck sizes, cases per pallet, and tops and closures options. It concludes with contact information for Packaging Logistics.
A survey on phrase structure learning methods for text classificationijnlc
Text classification is a task of automatic classification of text into one of the predefined categories. The
problem of text classification has been widely studied in different communities like natural language
processing, data mining and information retrieval. Text classification is an important constituent in many
information management tasks like topic identification, spam filtering, email routing, language
identification, genre classification, readability assessment etc. The performance of text classification
improves notably when phrase patterns are used. The use of phrase patterns helps in capturing non-local
behaviours and thus helps in the improvement of text classification task. Phrase structure extraction is the
first step to continue with the phrase pattern identification. In this survey, detailed study of phrase structure
learning methods have been carried out. This will enable future work in several NLP tasks, which uses
syntactic information from phrase structure like grammar checkers, question answering, information
extraction, machine translation, text classification. The paper also provides different levels of classification
and detailed comparison of the phrase structure learning methods.
THE ABILITY OF WORD EMBEDDINGS TO CAPTURE WORD SIMILARITIESkevig
Distributed language representation has become the most widely used technique for language representation in various natural language processing tasks. Most of the natural language processing models that are based on deep learning techniques use already pre-trained distributed word representations, commonly called word embeddings. Determining the most qualitative word embeddings is of crucial importance for such models. However, selecting the appropriate word embeddings is a perplexing task since the projected embedding space is not intuitive to humans.In this paper, we explore different approaches for creating distributed word representations. We perform an intrinsic evaluation of several state-of-the-art word embedding methods. Their performance on capturing word similarities is analysed with existing benchmark datasets for word pairs similarities. The research in this paper conducts a correlation analysis between ground truth word similarities and similarities obtained by different word embedding methods.
Novelty detection via topic modeling in research articlescsandit
In today’s world redundancy is the most vital problem faced in almost all domains. Novelty
detection is the identification of new or unknown data or signal that a machine learning system
is not aware of during training. The problem becomes more intense when it comes to “Research
Articles”. A method of identifying novelty at each sections of the article is highly required for
determining the novel idea proposed in the research paper. Since research articles are semistructured,
detecting novelty of information from them requires more accurate systems. Topic
model provides a useful means to process them and provides a simple way to analyze them. This
work compares the most predominantly used topic model- Latent Dirichlet Allocation with the
hierarchical Pachinko Allocation Model. The results obtained are promising towards
hierarchical Pachinko Allocation Model when used for document retrieval.
Statistical Named Entity Recognition for Hungarian – analysis ...butest
This document describes statistical named entity recognition for Hungarian texts. The authors created a corpus of Hungarian news articles annotated with named entity tags. They used a rich set of 225 linguistic features to train support vector machines, neural networks, and decision trees. Their best model achieved an F-measure of 93.59% for term-level named entity recognition and 90.57% for phrase-level, outperforming prior rule-based systems for Hungarian. Feature selection helped reduce the feature set to 135 while maintaining high performance.
Integrating Incoming Information into Discourse Model in Tunisian ArabicDr. Marwa Mekni-Toujani
There are two main lines in discourse processing research. The first one is interested in understanding the type of inferences that constitute discourse representations (logical inferences, bridging inferences, elaborative inferences, predictive inferences, etc.) (Stewart, Kidd, & Haigh, 2009). The second line is interested in the time course of integrating incoming information with the unfolding discourse model (ibid). This study addressed the second line of research. Incoming information can be integrated as soon as it is available (early integration model) or it is integrated later as a wrap-up operation (delayed integration model) (Guzman & Klin, 2000). That is, the endeavor of the present study was to gauge the time course of connecting incoming information to information mentioned earlier in the text that are no longer available in Working Memory (WM). Additionally, There are some factors that are believed to affect the time course of the generation of discourse-level representations. In fact, Hannon & Daneman (2001) argue that cognitive styles can influence the ability to detect anomalies. Concerning task demands, it is argued that some instructions require different strategies by the reader (Smith & O’Brien, 2012). Hence, this study explored the effects of both field-dependency and task demands. Ultimately, the present study aspired to answer the following research questions: (1) does readers’ sensitivity to spatial anomaly affect the time-course of integrating incoming information into the unfolding discourse model in Tunisian Arabic (TA)? (2) do field dependency and task demands affect the time-course of integration in TA?
A COMPARATIVE STUDY OF FEATURE SELECTION METHODSkevig
This article focuses on evaluating and comparing the available feature selection methods in general versatility regarding authorship attribution problems and tries to identify which method is the most effective. The discussions on general versatility of feature selection methods and its connection in selecting the appropriate features for varying data were done. In addition, different languages, different types of features, different systems for calculating the accuracy of SVM (support vector machine), and different criteria for determining the rank of feature selection methods were used to measure the general versatility of these methods together. The analysis results indicate the best feature selection method is different for each dataset; however, some methods can always extract useful information to discriminate the classes. The chi-square was proved to be a better method overall.
This document discusses applying theory revision techniques to automatically improve a heuristic-based algorithm for designing distributed databases. The algorithm decides which fragmentation technique to use for each database class. Theory revision is used to revise the algorithm's heuristics based on examples of previously tested fragmentation schemas and their performance. The revised algorithm is incorporated back into the design framework to produce improved fragmentation schemas with better performance.
Taxonomy extraction from automotive natural language requirements using unsup...ijnlc
In this paper we present a novel approach to semi-automatically learn concept hierarchies from natural
language requirements of the automotive industry. The approach is based on the distributional hypothesis
and the special characteristics of domain-specific German compounds. We extract taxonomies by using
clustering techniques in combination with general thesauri. Such a taxonomy can be used to support
requirements engineering in early stages by providing a common system understanding and an agreedupon
terminology. This work is part of an ontology-driven requirements engineering process, which builds
on top of the taxonomy. Evaluation shows that this taxonomy extraction approach outperforms common
hierarchical clustering techniques.
KANNADA NAMED ENTITY RECOGNITION AND CLASSIFICATIONijnlc
Named Entity Recognition and Classification (NERC) is a process of identification of proper nouns in the text and classification of those nouns into certain predefined categories like person name, location,organization, date, and time etc. NERC in Kannada is an essential and challenging task. The aim of this work is to develop a novel model for NERC, based on Multinomial Naïve Bayes (MNB) Classifier. The Methodology adopted in this paper is based on feature extraction of training corpus, by using term frequency, inverse document frequency and fitting them to a tf-idf-vectorizer. The paper discusses the
various issues in developing the proposed model. The details of implementation and performance evaluation are discussed. The experiments are conducted on a training corpus of size 95,170 tokens and test corpus of 5,000 tokens. It is observed that the model works with Precision, Recall and F1-measure of
83%, 79% and 81% respectively.
A comparative study on term weighting methods for automated telugu text categ...IJDKP
Automatic Text categorization refers to the process of assigning a category or some categories
automatically among predefined ones. Text categorization is challenging in Indian languages has rich in
morphology, a large number of word forms and large feature spaces. This paper investigates the
performance of different classification approaches using different term weighting approaches in order to
decide the most applicable one to Telugu text classification problem. We have investigated on different
term weighting methods for Telugu corpus in combination with Naive Bayes ( NB), Support Vector
Machine (SVM) and k Nearest Neighbor (kNN) classifiers.
Recognition of Words in Tamil Script Using Neural NetworkIJERA Editor
In this paper, word recognition using neural network is proposed. Recognition process is started with the partitioning of document image into lines, words, and characters and then capturing the local features of segmented characters. After classifying the characters, the word image is transferred into unique code based on character code. This code ideally describes any form of word including word with mixed styles and different sizes. Sequence of character codes of the word form input pattern and word code is a target value of the pattern. Neural network is used to train the patterns of the words. Trained network is tested with word patterns and is recognized or unrecognized based on the network error value. Experiments have been conducted with a local database to evaluate the performance of the word recognizing system and obtained good accuracy. This method can be applied for any language word recognition system as the training is based on only unique code of the characters and words belonging to the language.
This paper proposes Natural language based Discourse Analysis method used for extracting
information from the news article of different domain. The Discourse analysis used the Rhetorical Structure
theory which is used to find coherent group of text which are most prominent for extracting information
from text. RST theory used the Nucleus- Satellite concept for finding most prominent text from the text
document. After Discourse analysis the text analysis has been done for extracting domain related object
and relates this object. For extracting the information knowledge based system has been used which
consist of domain dictionary .The domain dictionary has a bag of words for domain. The system is
evaluated according gold-of-art analysis and human decision for extracted information.
The document provides a survey of word sense disambiguation (WSD) research. It discusses the history and applications of WSD, and categorizes the main WSD approaches as knowledge-based, supervised, and unsupervised. For each category, it outlines several common algorithms used, such as Lesk algorithm, decision trees, Naive Bayes, and support vector machines. The document surveys the state-of-the-art in WSD performance and compares different algorithm types. It also provides an overview of WSD research in Indian languages.
In this paper, we presented a method to retrieve documents with unstructured text data written in different
languages. Apart from the ordinary document retrieval systems, the proposed system can also process
queries with terms in more than one language. Unicode, the universally accepted encoding standard is used
to present the data in a common platform while converting the text data into Vector Space Model. We got
notable F measure values in the experiments irrespective of languages used in documents and queries.
Ontologisms have been applied to many applications in recent years, especially on Sematic Web, Information
Retrieval, Information Extraction, and Question and Answer. The purpose of domain-specific ontology
is to get rid of conceptual and terminological confusion. It accomplishes this by specifying a set of generic
concepts that characterizes the domain as well as their definitions and interrelationships. This paper will
describe some algorithms for identifying semantic relations and constructing an Information Technology
Ontology, while extracting the concepts and objects from different sources. The Ontology is constructed
based on three main resources: ACM, Wikipedia and unstructured files from ACM Digital Library. Our
algorithms are combined of Natural Language Processing and Machine Learning. We use Natural Language
Processing tools, such as OpenNLP, Stanford Lexical Dependency Parser in order to explore sentences.
We then extract these sentences based on English pattern in order to build training set. We use a
random sample among 245 categories of ACM to evaluate our results. Results generated show that our
system yields superior performance.
Semantic based automatic question generation using artificial immune systemAlexander Decker
The document describes a system that uses artificial immune systems and natural language processing techniques like semantic role labeling and named entity recognition to automatically generate questions from text. It introduces a model that applies these techniques to extract semantic patterns from sentences, trains a classifier using artificial immune systems to classify question types, and then generates questions by matching patterns. The system was tested on sentences from various sources and showed promising results, correctly determining question types 95% of the time and generating matching questions 87% of the time.
Este documento presenta información sobre tres idiomas: español, inglés y francés. Resume brevemente el origen y difusión del español como lengua romance originaria de Castilla, España. Explica que el inglés surgió en Inglaterra y se extendió a Escocia, convirtiéndose en un idioma global debido al imperio británico. Finalmente, describe al francés como lengua hablada originalmente en Francia y extendida a través del segundo imperio colonial francés.
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The Characteristics of DNA Splicing Languages via Yusof-Goode Approach
1. THE CHARACTERISTICS OF DNA SPLICING
LANGUAGES VIA YUSOF-GOODE APPROACH
MUHAMMAD AZRIN BIN AHMADMUHAMMAD AZRIN BIN AHMAD
FIRST ASSESSMENT
Doctor of Philosophy (Mathematics)- Fast Track
Supervisors
11
ASSOC PROF DR NOR HANIZA SARMIN (MAIN),ASSOC PROF DR NOR HANIZA SARMIN (MAIN), 22
DR FONG WAN HENG (CO)DR FONG WAN HENG (CO)
1
Department of Mathematical Sciences, Faculty of Science,
2
Ibnu Sina Institute for Fundamental Science Studies
Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor.
33
DR YUHANI YUSOF (CO)DR YUHANI YUSOF (CO)
3
Faculty of Industrial Science & Technology
Universiti Malaysia Pahang, 26300 UMP Gambang, Pahang.
2. PRESENTATION OUTLINEPRESENTATION OUTLINE
INTRODUCTION
Background
of the Research
Problem
Statement
Objectives of
the Research
Scope of the
Research
Significance
of the Research
LITERATURE
REVIEW
DNA and Its
Structure
Restriction
Enzyme
Mathematical
Model
The
Development of
Splicing System
and Languages
RESEARCH
METHODOLOGY
Research Design
and Procedure
Operational
Framework
Gantt Chart and
Schedule
STATUS OF
RESEARCH
What Had Been
Done?
What Need To
Be Done?
2
4. Deoxyribonucleic acid (DNA) has two important functions which
are protein synthesis and also self replication.
The splicing system which was introduced by Head [3] explained
about the recombinant behaviours
of DNA under the framework of Formal Language Theory.
Four nucleotides which are Adenine (A), Guanine (G), Cytosine (C)
and Thymine (T) can be paired as [AT], [GC], [CG], [TA] or
simply presented as a, g, c and t [5].
The splicing operation includes cutting by restriction enzyme and
also pasting by the existence of appropriate ligase.
Background of the Research
4
[3] Head, T. Formal Language Theory and DNA : An Analysis of the Generative Capacity of Specific Recombinant Behaviors. Bulletin of
Mathematical Biology. 1987. 49: 737 – 759.
[5] Gheorghe, P., Rozenberg, G., Salomaa, A. DNA Computing New Computing Paradigms. New York, London: Springer. 1998.
[3] Head, T. Formal Language Theory and DNA : An Analysis of the Generative Capacity of Specific Recombinant Behaviors. Bulletin of
Mathematical Biology. 1987. 49: 737 – 759.
[5] Gheorghe, P., Rozenberg, G., Salomaa, A. DNA Computing New Computing Paradigms. New York, London: Springer. 1998.
5. Background of the Research
5
Various studies in splicing system has led to the formulation of
Yusof-Goode (Y-G) Splicing System.
The resulting language from splicing system (called splicing
language) can be categorized into two types [6]: adult/inert and
limit language.
The extension of limit language which is n-th order limit language
has been defined in [6].
This research is narrowed to second order limit language where
its existence and characteristics will be further studied.
[6] Goode, E. and Pixton, D. Splicing to the Limit. Lecture Notes in Computer Science. 2004. 2950: 189-201.[6] Goode, E. and Pixton, D. Splicing to the Limit. Lecture Notes in Computer Science. 2004. 2950: 189-201.
6. Statement of Problem
1. How to determine the existence
of second order limit language in
Y-G splicing system? And what
characteristics does it posses?
3. What are the mechanisms that
relate second order limit language
with different classes and variants of
splicing system?
6
2. What are the sufficient conditions
for the second order limit language
to exist in a Y-G splicing system and
other variants of splicing system?
4. How to conduct a wet-lab experiment
and develop a mathematical model to
validate the existence of second order
limit language? Which method can be
used to compare those results in
mathematical and biological point of
view?
7. 7
Objectives of the Research
2. To provide the sufficient
conditions on the existence of
second order limit language in
splicing system.
1. To determine the
existence of second
order limit language
and study its
characteristics.
3. To relate the
existence of second
order limit languages
among variants of
splicing system.
4. To develop and verify a
mathematical model that can
validate the existence of second
order limit languages.
8. This research will only focus on the second order
limit language with at most two initial strings and
at most two rules. The splicing system used will
include Y-G splicing system which is restricted to Y-
G rule and also other classes of splicing systems.
Scope of the Research
8
11. DNA and Its Structure
11
[5] Gheorghe, P., Rozenberg, G., Salomaa, A. DNA Computing New Computing Paradigms. New York, London: Springer. 1998.[5] Gheorghe, P., Rozenberg, G., Salomaa, A. DNA Computing New Computing Paradigms. New York, London: Springer. 1998.
12. DNA and Its Structure (cont.)
12
[1] Tamarin, R. H. Principle of Genetics. 7th
. ed. USA: The MacGraw-Hill Companies. 2001.[1] Tamarin, R. H. Principle of Genetics. 7th
. ed. USA: The MacGraw-Hill Companies. 2001.
13. Restriction Enzyme
A restriction enzyme is found in bacteria. It plays
the role to cut the DNA molecules at their crossing
sites. The recognition process that determines the
cutting site is acted by restriction endonuclease [5].
After that, restriction enzyme will clamp at the
crossing site and the cutting process will take place.
13
[5] Gheorghe, P., Rozenberg, G., Salomaa, A. DNA Computing New Computing Paradigms. New York, London: Springer. 1998.[5] Gheorghe, P., Rozenberg, G., Salomaa, A. DNA Computing New Computing Paradigms. New York, London: Springer. 1998.
14. 14
The four bases of DNA molecules which are known as a, g, c and t
are presented by initial set of alphabet. Besides that, the initial
molecule is presented by the initial string and rules (which
represent restriction enzymes) in splicing. Mathematically, it can be
seen as follows:
S = (A, I, R) where A is an alphabet made up of four bases; a, c, g
and t. The symbol I represents initial string of dsDNA and R
represents rule of either left pattern (u; x, v : y; x, z), right pattern
(u, x; v : y, x; z), or both patterns (u, x, v : y, x, z)
Mathematical Model
15. The Development of Splicing System and
Languages
15
Y-G
Splicing System
2011
Pixton Splicing
System
1996
Paun Splicing
System
1996
Goode-Pixton
Splicing System
1999
Head Splicing
System
1987
16. 16
The Development of Splicing System and
Languages (cont.)
[3] Head, T. Formal Language Theory and DNA : An Analysis of the Generative Capacity of Specific Recombinant Behaviors. Bulletin of Mathematical Biology. 1987. 49: 737 – 759.
[9] Paun, Gh. On the Splicing Operation. Discrete Applied Mathematics. 1996. 70: 57-79.
[10] Pixton, D. Regularity of Splicing Languages. Discrete Applied Mathematics. 1996. 69: 101-124.
[12] Paun, G., Rozenberg, G., Salomaa, A. Computing by Splicing. Theoretical Computer Science. 2006. 168: 321-336.
[13] Bonizzoni, P., Ferretti, C., Mauri, G. and Zizza, R. Separating Some Splicing Models. Information Processing Letters. 2001. 79: 255-259.
[14] Yusof, Y., Sarmin, N. H., Goode, T. E., Mahmud, M. and Fong, W. H. An Extension of DNA Splicing System. Sixth International Conference on Bio-Inspired Computing: Theories and Application. September
27-29, 2011. Penang. 2011. 246-248.
[3] Head, T. Formal Language Theory and DNA : An Analysis of the Generative Capacity of Specific Recombinant Behaviors. Bulletin of Mathematical Biology. 1987. 49: 737 – 759.
[9] Paun, Gh. On the Splicing Operation. Discrete Applied Mathematics. 1996. 70: 57-79.
[10] Pixton, D. Regularity of Splicing Languages. Discrete Applied Mathematics. 1996. 69: 101-124.
[12] Paun, G., Rozenberg, G., Salomaa, A. Computing by Splicing. Theoretical Computer Science. 2006. 168: 321-336.
[13] Bonizzoni, P., Ferretti, C., Mauri, G. and Zizza, R. Separating Some Splicing Models. Information Processing Letters. 2001. 79: 255-259.
[14] Yusof, Y., Sarmin, N. H., Goode, T. E., Mahmud, M. and Fong, W. H. An Extension of DNA Splicing System. Sixth International Conference on Bio-Inspired Computing: Theories and Application. September
27-29, 2011. Penang. 2011. 246-248.
Variants
of Splicing
System
17. 17
The Development of Splicing System and
Languages (cont.)
[8] Yusof, Y. Bio Molecular Inspiration in DNA Splicing System. Ph.D. Thesis. Universiti Teknologi Malaysia (UTM); 2011.
[11] Laun, T. E. G. Constants and Splicing System. PhD. Thesis. State University of New York at Binghamton; 1999.
[15] Mateescu, A., Paun, Gh., Rozenberg, G. and Salomaa, A. Simple Splicing System. Discrete Applied Mathematics. 1998. 84: 145-163.
[16] Goode, E. and Pixton, D. Semi-simple Splicing Systems. In: Martin-Vide, C. and Mitrana, V. eds. Where Mathematics, Computer Science, Linguistics and Biology Meet.
Dordrecht: Kluwer Academic Publishers. 343-352; 2001.
[8] Yusof, Y. Bio Molecular Inspiration in DNA Splicing System. Ph.D. Thesis. Universiti Teknologi Malaysia (UTM); 2011.
[11] Laun, T. E. G. Constants and Splicing System. PhD. Thesis. State University of New York at Binghamton; 1999.
[15] Mateescu, A., Paun, Gh., Rozenberg, G. and Salomaa, A. Simple Splicing System. Discrete Applied Mathematics. 1998. 84: 145-163.
[16] Goode, E. and Pixton, D. Semi-simple Splicing Systems. In: Martin-Vide, C. and Mitrana, V. eds. Where Mathematics, Computer Science, Linguistics and Biology Meet.
Dordrecht: Kluwer Academic Publishers. 343-352; 2001.
Classes
of
Splicing
System
18. 18
The Development of Splicing System and
Languages (cont.)
[7] Sarmin, N. H. and Fong, W. H. Mathematical Modelling of Splicing System. First International Conference on natural Resources Engineering and Technology. July 24-25, 2006.
Putrajaya. 2006: 524-527.
[8] Yusof, Y. Bio Molecular Inspiration in DNA Splicing System. Ph.D. Thesis. Universiti Teknologi Malaysia (UTM); 2011.
[17] Laun, E. and Reddy, K. J. Wet Splicing Systems. DIMACS Series in Discrete Mathematics and Theoretical Computer Science. 1999. 48: 73-83.
[18] Kari, L. DNA Computing: The Arrival of Biological Mathematics. The Mathematical Intelligencer. 1997. 19(2): 9-22.
[7] Sarmin, N. H. and Fong, W. H. Mathematical Modelling of Splicing System. First International Conference on natural Resources Engineering and Technology. July 24-25, 2006.
Putrajaya. 2006: 524-527.
[8] Yusof, Y. Bio Molecular Inspiration in DNA Splicing System. Ph.D. Thesis. Universiti Teknologi Malaysia (UTM); 2011.
[17] Laun, E. and Reddy, K. J. Wet Splicing Systems. DIMACS Series in Discrete Mathematics and Theoretical Computer Science. 1999. 48: 73-83.
[18] Kari, L. DNA Computing: The Arrival of Biological Mathematics. The Mathematical Intelligencer. 1997. 19(2): 9-22.
Biological
Approach of
Splicing
System
19. 19
The Development of Splicing System and
Languages (cont.)
[6] Goode, E. and Pixton, D. Splicing to the Limit. Lecture Notes in Computer Science. 2004. 2950: 189-201.
[19] Lim, D. S. F. Splicing Systems and Languages. Master. Dissertation. Universiti Teknologi Malaysia (UTM); 2006.
[20] Goode, E. and DeLorbe, W. DNA Splicing System: An Ordinary Differential Equations Model and Simulation. Lecture Notes in Computer Science. 2008. 4848: 236-
245.
[6] Goode, E. and Pixton, D. Splicing to the Limit. Lecture Notes in Computer Science. 2004. 2950: 189-201.
[19] Lim, D. S. F. Splicing Systems and Languages. Master. Dissertation. Universiti Teknologi Malaysia (UTM); 2006.
[20] Goode, E. and DeLorbe, W. DNA Splicing System: An Ordinary Differential Equations Model and Simulation. Lecture Notes in Computer Science. 2008. 4848: 236-
245.
The
Splicing
Language
20. Basic Definitions
20
Definition 1 [3]: Head Splicing System
A splicing system S = (A, I, B, C) consists of a finite alphabet A, a finite set I of
initial strings in A*, and finite sets B and C of triples (c, x, d) with c, x and d in A*.
Each such triple in B or C is called a pattern. For each such triple the string cxd is
called a site and the string x is called a crossing. Patterns in B are called left
patterns and patterns in C are called right patterns. The language L = L(S)
generated by S consists of the strings in I and all strings that can be obtained by
adjoining the words ucxfq and pexdv to L whenever ucxdv and pexfq are in L and
(c, x, d) and (e, x, f) are patterns of the same hand. A language L is a splicing
language if there exists a splicing system S for which L = L(S).
[3] Head, T. Formal Language Theory and DNA : An Analysis of the Generative Capacity of Specific Recombinant Behaviors. Bulletin of
Mathematical Biology. 1987. 49: 737 – 759.
[3] Head, T. Formal Language Theory and DNA : An Analysis of the Generative Capacity of Specific Recombinant Behaviors. Bulletin of
Mathematical Biology. 1987. 49: 737 – 759.
21. Basic Definitions (cont.)
21
Definition 2 [8]: Y-G Splicing System
If , where and and are elements of I,
then splicing using r produces the initial string I together
with and , presented in either order where
are the free monoid generated by A with the concatenation operation and 1 as the
identity element.
.
[8] Yusof, Y. Bio Molecular Inspiration in DNA Splicing System. Ph.D. Thesis. Universiti Teknologi Malaysia (UTM); 2011.
.
[8] Yusof, Y. Bio Molecular Inspiration in DNA Splicing System. Ph.D. Thesis. Universiti Teknologi Malaysia (UTM); 2011.
22. 22
Definition 3 [6]: Transcient Language
A splicing language is called a transient splicing language if a set of
strings is eventually used up and disappear in a given system.
Definition 4 [6]: n-th Order Limit Language
Let Ln-1 be the set of second-order limit words of L, the set Ln of n-th
order limit words of L to be the set of first order limits of Ln-1. We
obtain Ln from Ln-1 by deleting the words that are transient in Ln-1.
Definition 5 [3]: Palindromic Rule
A string I of dsDNA is said to be palindromic if the sequence from
the left side of the upper single strand is equal with the sequence
from the right side of the lower single strand.
Basic Definitions (cont.)
[3] Head, T. Formal Language Theory and DNA : An Analysis of the Generative Capacity of Specific Recombinant Behaviors. Bulletin of
Mathematical Biology. 1987. 49: 737 – 759.
[6] Goode, E. and Pixton, D. Splicing to the Limit. Lecture Notes in Computer Science. 2004. 2950: 189-201.
[3] Head, T. Formal Language Theory and DNA : An Analysis of the Generative Capacity of Specific Recombinant Behaviors. Bulletin of
Mathematical Biology. 1987. 49: 737 – 759.
[6] Goode, E. and Pixton, D. Splicing to the Limit. Lecture Notes in Computer Science. 2004. 2950: 189-201.
24. Research Design and Procedure
1. Literature review on Formal Language Theory, DNA structures and its related
information, splicing system and splicing languages.
To examine the basic concepts of Formal Language Theory that will be used in
splicing system.
To explore the structure of DNA and the processes which will take place inside it that
boost the idea of splicing system.
To study the splicing system and the mechanism of it. In this research, the formation
of splicing language is studied carefully because most of the results come from it. In
addition, the types of splicing language will be explored too as different splicing
system will generate distinct types of splicing language.
2. Determine the existence of second order limit language and its characteristics
To find the existence of second order limit language with the presence of at most two
initial string and two rules.
To explore the characteristics of second order limit language and present those
characteristics by theorems.
24
25. 25
Research Design and Procedure
(cont.)3. Investigate sufficient conditions of second order limit language
To define the methods of recognizing second order splicing
language.
To search conditions based on the rules of splicing system
where the second order limit language exists.
To present all those conditions by theorems and provide the
proofs.
4. Find the relation of second order limit language among other types
of splicing language
To present the relation of second order limit language with
other types of splicing system like self-closed splicing system,
semi-null splicing language and others.
26. Research Design and Procedure
(cont.)5. Construct a mathematical model to validate the existence of second
order limit language
To develop a mathematical model of splicing system to validate
the existence of second order limit language using the
restriction enzyme from New England Biolabs Catalogue.
To provide a mathematical analysis from limit graph so that the
results later can be compared with the wet-lab experiment
results.
6. Conduct a wet-lab experiment
To study and construct the procedures of conducting a wet-lab
experiment.
To carry out the wet-lab experiment.
26
31. Second Order Limit Language
31
Let L1 be the set of second order limit words of L,
the set L2 of 2-nd order limit words of L to be the
set of first order limits of L1. We obtain L2 from L1
by deleting words that are transient in L1.
32. 32
Conjecture 1
If the combination of two splicing languages of first stage splicing
under the stated rule has different length from those two splicing
languages of first stage splicing, then second order limit language is
identified and existed.
Conjecture 2
If the resulted splicing language that is derived from first stage
splicing is different from the resulted splicing language, then it is
second order limit language.
Mechanism of Recognizing the Second Order Limit
Language
33. Biological Examples of Second Order and Non-Second
Order Limit Language
33
Example 1
Let be a Y-G splicing system consisting of two restriction enzymes
namely FauI and AciI: for with
where for this case we choose r = (r1 : r2) where r1 = (cccgcttaa;cg,1) and r2 =
(c;cg,c) respectively such that and initial strings I = {αcccgcttaacgβ}
where . When splicing occurs, the following splicing languages are
generated:
34. 34
Biological Examples of Second Order and Non-Second
Order Limit Language (cont.)
Based on the rules stated above, when the resulted splicing languages are being spliced,
new splicing languages are obtained. They are listed as below:
35. Biological Examples of Second Order and Non-Second
Order Limit Language (cont.)
35
Example 2
Suppose is a Y-G splicing system consisting only one restriction
enzyme namely AciI with then an element of R such that
and initial string The following is the resulted splicing
language:
After that, the splicing among the language of first stage splicing will result in the
same molecules as the previous hence creates no new molecules at all. Thus, no
second order limit language is produced.
36. 36
Conjecture 3
If the splicing system is null-context splicing system with the
presence of a rule and initial string which consists of two times
crossing site of the restriction enzyme in the initial string, then the
second order limit language exist.
Conjecture 4
If a splicing system is self-closed splicing system, then the second
order limit language does not exist.
Relation of Second Order Limit Language Among the
Variants of Splicing System
37. Characterization of the Second Order Limit
Language
37
Theorem 1
If the rule of a splicing system is itself palindromic, then
there will be no second order limit language.
38. 38
Characterization of the Second Order Limit
Language (cont.)
Proof
Suppose is a Y-G splicing system and the rule, of selected
restriction enzyme is palindromic. Let us consider a case where there is a given rule
for where a and b is complement to each other. So, the
splicing occurs as below,
where
The splicing process among the resulted splicing languages do not produce distinct
language as the splicing between those languages again produce the same language
as the previous one.
39. Characterization of the Second Order Limit
Language (cont.)
39
Now, let there be two rules. So, let for where a and b
is complement to each other. Therefore, the splicing occurs as below,
Again, the splicing among the resulted language do not produce the distinct splicing
language hence no second order limit language is detected. Assume for k-th number
of rules, no second order limit language exists. By the hypothesis, no second order
limit language exists in (k+1)-th iteration of splicing. □
40. 40
Theorem 2
An initial string that contains two recognition sites
of two rules with identical crossing sites produce
second order limit language.
Characterization of the Second Order Limit
Language (cont.)
41. Characterization of the Second Order Limit
Language (cont.)
41
Proof
We prove by contradiction. Suppose no second order limit language exist. Assume
the Y-G splicing system, and the rule, have two crossing sites of
two different rules in the form of where a is complement
to b, and k is complement to k’ and vice versa. The splicing occurs and produces one
of the following:
42. 42
Characterization of the Second Order Limit
Language (cont.)
By splicing those two resulted splicing languages using the rules presented above, a
new splicing language is produced as given below:
The new splicing language, is a distinct splicing language
(the resulted splicing language from the first splicing can be referred to Example
4.1). Thus contradict to the assumption above. Hence the original statement is true.
□
From the above theorem, we have the following immediate result.
43. 43
Characterization of the Second Order Limit
Language (cont.)
Corollary 1
If only an initial string and a rule is involved, then the second order limit language
does not exist.
45. 45
The characteristic of second order limit language will be further
studied from the properties of rule such as the effect on right and
left context and others.
The sufficient conditions for the existing of second order limit
language will be focused more upon the choice of initial string
from lambda phage and also on the properties of rules.
What Need To Be Done?
46. 46
More classes of splicing system will be implemented to obtain
more characterization of second order limit language and also its
properties.
The standard procedure of handling wet-lab experiment will be
revised based on the New England Biolabs manual and its
websites, and also from past researchers that also work on this
laboratory experiment.
The limit graph of second order limit language will be constructed
in order to make comparison and also to analyse the results
through the data obtained.
What Need To Be Done?
47. REFERENCES
47
1. Tamarin, R. H. Principle of Genetics. 7th
. ed. USA: The MacGraw-Hill Companies.
2001.
2. Linz, P. An Introduction to Formal Languages and Automata. Fourth Edition. USA:
Jones and Bartlett Publishers. 2006.
3. Head, T. Formal Language Theory and DNA : An Analysis of the Generative Capacity
of Specific Recombinant Behaviors. Bulletin of Mathematical Biology. 1987. 49: 737 –
759.
4. Dwyer, C. and Lebeck, A. Introducton to DNA Self Assembled Computer Design.
Boston, London: Artech House, Inc. 2008.
5. Gheorghe, P., Rozenberg, G., Salomaa, A. DNA Computing New Computing Paradigms.
New York, London: Springer. 1998.
6. Goode, E. and Pixton, D. Splicing to the Limit. Lecture Notes in Computer Science.
2004. 2950: 189-201.
48. REFERENCES (cont.)
48
7. Sarmin, N. H. and Fong, W. H. Mathematical Modelling of Splicing System. First
International Conference on natural Resources Engineering and Technology. July 24-25,
2006. Putrajaya. 2006: 524-527.
8. Yusof, Y. Bio Molecular Inspiration in DNA Splicing System. Ph.D. Thesis. Universiti
Teknologi Malaysia (UTM); 2011.
9. Paun, Gh. On the Splicing Operation. Discrete Applied Mathematics. 1996. 70: 57-79.
10. Pixton, D. Regularity of Splicing Languages. Discrete Applied Mathematics. 1996. 69:
101-124.
11. Laun, T. E. G. Constants and Splicing System. PhD. Thesis. State University of New
York at Binghamton; 1999.
12. Paun, G., Rozenberg, G., Salomaa, A. Computing by Splicing. Theoretical Computer
Science. 2006. 168: 321-336.
49. REFERENCES (cont.)
49
13. Bonizzoni, P., Ferretti, C., Mauri, G. and Zizza, R. Separating Some Splicing Models.
Information Processing Letters. 2001. 79: 255-259.
14. Yusof, Y., Sarmin, N. H., Goode, T. E., Mahmud, M. and Fong, W. H. An Extension of
DNA Splicing System. Sixth International Conference on Bio-Inspired Computing:
Theories and Application. September 27-29, 2011. Penang. 2011. 246-248.
15. Mateescu, A., Paun, Gh., Rozenberg, G. and Salomaa, A. Simple Splicing System.
Discrete Applied Mathematics. 1998. 84: 145-163.
16. Goode, E. and Pixton, D. Semi-simple Splicing Systems. In: Martin-Vide, C. and
Mitrana, V. eds. Where Mathematics, Computer Science, Linguistics and Biology Meet.
Dordrecht: Kluwer Academic Publishers. 343-352; 2001.
17. Laun, E. and Reddy, K. J. Wet Splicing Systems. DIMACS Series in Discrete
Mathematics and Theoretical Computer Science. 1999. 48: 73-83.
50. REFERENCES (cont.)
50
18. Kari, L. DNA Computing: The Arrival of Biological Mathematics. The Mathematical
Intelligencer. 1997. 19(2): 9-22.
19. Lim, D. S. F. Splicing Systems and Languages. Master. Dissertation. Universiti
Teknologi Malaysia (UTM); 2006.
20. Goode, E. and DeLorbe, W. DNA Splicing System: An Ordinary Differential Equations
Model and Simulation. Lecture Notes in Computer Science. 2008. 4848: 236-245.
51. 51
1. Yuhani Yusof, Nor Haniza Sarmin, Fong Wan Heng, T. Elizabeth Goode and
Muhammad Azrin Ahmad. “An Analysis of Four Variants of Splicing System”.
Proceedings of the 20th National Symposium on Mathematical Sciences
(SKSM20) AIP Conf. Proc. 1522, 888 – 895 (2013).
2. Muhammad Azrin Ahmad, Nor Haniza Sarmin, Fong Wan Heng, Yuhani
Yusof. “On the Characteristics of Second Order Limit Language”. The Asia
Mathematical Conference 2013 (AMC 2013). 1 – 4 July 2013. (Poster
Session).
3. Muhammad Azrin Ahmad, Nor Haniza Sarmin, Yuhani Yusof, Fong Wan
Heng. “Exploring the New Type of Splicing Language”. The 2013 International
Conference on Mathematics and Its Application (ICMA 2013). 18 – 21 August
2013. (Submitted).
Publications
52. ACKNOWLEDGEMENT
52
EXAMINERS
For their time and useful comments
SUPERVISORS
Assoc Prof Dr Nor Haniza Sarmin,
Dr Fong Wan Heng,
Dr Yuhani Yusof
SCHOLARSHIP
MyBrain15 MYPhD