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International studies have shown the reading competence of Indonesian students to be relatively low compared to other countries. Considering the importance of reading comprehension, the current research has two aims. The first is to provide some insight into the identification of students’ difficulty with reading. The second is to investigate the implementation of innovative methods for teaching reading comprehension in the Indonesian educational context. Both studies were conducted in state upper grade primary schools in Urban Sub District BCL in Bandung, Indonesia. Study One profiled students’ reading performance. Five measurement instruments were developed, written in Indonesian language. The construction of mental models was also introduced. Two hundred and sixty five Grade Five students from eight schools were measured for their competence in decoding, prior knowledge, comprehension monitoring, construction of mental models, reading comprehension specifically related to a particular topic, and their general reading comprehension. The students’ reading performance profiles were very varied. They showed the importance of each component for the achievement of reading comprehension. The profiles also showed the ability for each component of reading comprehension to compensate each other’s function to enable the students to perform well in reading comprehension. The best-fit model for the data accounted for 47% of students’ performance in reading comprehension. Study Two compared instructional interventions, and examined the way each method worked in the Indonesian educational context. The three instructional intervention methods were Reciprocal Teaching (RT), Instruction prompting students to develop Mental Models (IMM), and Instruction in Question Answering (IQA). Participants were one hundred and twelve students in the Sixth Grade from three primary schools. There were three groups in each school. Group 1 received RT followed by IMM (RT-IMM), Group 2 received IMM followed by RT (IMM-RT), and Group 3 received IQA. Group 3 was considered as the control group, since IQA is the traditional method widely adopted in Indonesia. Instruction was separated into 2 phases. Each phase consisted of four sessions of 30 minutes each over a two-week period. The implementation of IMM-RT tended to improve general reading comprehension more than other methods (RT-IMM and IQA). Interestingly, individuals who had a low performance in the pre-test for construction of mental models, improved their performance in the construction of mental models following implementation of RT at the first phase. The results support a conclusion that the IMM-RT combination is potentially effective for the enhancement of students’ reading comprehension. However, further results showed that, in order to implement RT and IMM in a common state school classroom in Indonesia, the teacher’s ability to manage and organise the group becomes crucial. Study Three was designed to validate the IMM-RT instructional intervention for improving performances of students with reading comprehension inadequacies, by addressing the limitations found in Study Two. Result showed that IMM-RT had potential for improving students’ performance in reading comprehension. The findings of the current study provide some understanding of reading comprehension in an Indonesian educational context. Moreover, the findings will add to the repertoire of educators about issues that need to be considered in implementing innovative methods for enhancing Indonesian students’ reading comprehension.

International studies have shown the reading competence of Indonesian students to be relatively low compared to other countries. Considering the importance of reading comprehension, the current research has two aims. The first is to provide some insight into the identification of students’ difficulty with reading. The second is to investigate the implementation of innovative methods for teaching reading comprehension in the Indonesian educational context. Both studies were conducted in state upper grade primary schools in Urban Sub District BCL in Bandung, Indonesia. Study One profiled students’ reading performance. Five measurement instruments were developed, written in Indonesian language. The construction of mental models was also introduced. Two hundred and sixty five Grade Five students from eight schools were measured for their competence in decoding, prior knowledge, comprehension monitoring, construction of mental models, reading comprehension specifically related to a particular topic, and their general reading comprehension. The students’ reading performance profiles were very varied. They showed the importance of each component for the achievement of reading comprehension. The profiles also showed the ability for each component of reading comprehension to compensate each other’s function to enable the students to perform well in reading comprehension. The best-fit model for the data accounted for 47% of students’ performance in reading comprehension. Study Two compared instructional interventions, and examined the way each method worked in the Indonesian educational context. The three instructional intervention methods were Reciprocal Teaching (RT), Instruction prompting students to develop Mental Models (IMM), and Instruction in Question Answering (IQA). Participants were one hundred and twelve students in the Sixth Grade from three primary schools. There were three groups in each school. Group 1 received RT followed by IMM (RT-IMM), Group 2 received IMM followed by RT (IMM-RT), and Group 3 received IQA. Group 3 was considered as the control group, since IQA is the traditional method widely adopted in Indonesia. Instruction was separated into 2 phases. Each phase consisted of four sessions of 30 minutes each over a two-week period. The implementation of IMM-RT tended to improve general reading comprehension more than other methods (RT-IMM and IQA). Interestingly, individuals who had a low performance in the pre-test for construction of mental models, improved their performance in the construction of mental models following implementation of RT at the first phase. The results support a conclusion that the IMM-RT combination is potentially effective for the enhancement of students’ reading comprehension. However, further results showed that, in order to implement RT and IMM in a common state school classroom in Indonesia, the teacher’s ability to manage and organise the group becomes crucial. Study Three was designed to validate the IMM-RT instructional intervention for improving performances of students with reading comprehension inadequacies, by addressing the limitations found in Study Two. Result showed that IMM-RT had potential for improving students’ performance in reading comprehension. The findings of the current study provide some understanding of reading comprehension in an Indonesian educational context. Moreover, the findings will add to the repertoire of educators about issues that need to be considered in implementing innovative methods for enhancing Indonesian students’ reading comprehension.

International studies have shown the reading competence of Indonesian students to be relatively low compared to other countries. Considering the importance of reading comprehension, the current research has two aims. The first is to provide some insight into the identification of students’ difficulty with reading. The second is to investigate the implementation of innovative methods for teaching reading comprehension in the Indonesian educational context. Both studies were conducted in state upper grade primary schools in Urban Sub District BCL in Bandung, Indonesia. Study One profiled students’ reading performance. Five measurement instruments were developed, written in Indonesian language. The construction of mental models was also introduced. Two hundred and sixty five Grade Five students from eight schools were measured for their competence in decoding, prior knowledge, comprehension monitoring, construction of mental models, reading comprehension specifically related to a particular topic, and their general reading comprehension. The students’ reading performance profiles were very varied. They showed the importance of each component for the achievement of reading comprehension. The profiles also showed the ability for each component of reading comprehension to compensate each other’s function to enable the students to perform well in reading comprehension. The best-fit model for the data accounted for 47% of students’ performance in reading comprehension. Study Two compared instructional interventions, and examined the way each method worked in the Indonesian educational context. The three instructional intervention methods were Reciprocal Teaching (RT), Instruction prompting students to develop Mental Models (IMM), and Instruction in Question Answering (IQA). Participants were one hundred and twelve students in the Sixth Grade from three primary schools. There were three groups in each school. Group 1 received RT followed by IMM (RT-IMM), Group 2 received IMM followed by RT (IMM-RT), and Group 3 received IQA. Group 3 was considered as the control group, since IQA is the traditional method widely adopted in Indonesia. Instruction was separated into 2 phases. Each phase consisted of four sessions of 30 minutes each over a two-week period. The implementation of IMM-RT tended to improve general reading comprehension more than other methods (RT-IMM and IQA). Interestingly, individuals who had a low performance in the pre-test for construction of mental models, improved their performance in the construction of mental models following implementation of RT at the first phase. The results support a conclusion that the IMM-RT combination is potentially effective for the enhancement of students’ reading comprehension. However, further results showed that, in order to implement RT and IMM in a common state school classroom in Indonesia, the teacher’s ability to manage and organise the group becomes crucial. Study Three was designed to validate the IMM-RT instructional intervention for improving performances of students with reading comprehension inadequacies, by addressing the limitations found in Study Two. Result showed that IMM-RT had potential for improving students’ performance in reading comprehension. The findings of the current study provide some understanding of reading comprehension in an Indonesian educational context. Moreover, the findings will add to the repertoire of educators about issues that need to be considered in implementing innovative methods for enhancing Indonesian students’ reading comprehension.

Thesis (MPhil (General Linguistics))--University of Stellenbosch, 2009.

Probabilistic data and knowledge bases are becoming increasingly important in academia and industry. They are continuously extended with new data, powered by modern information extraction tools that associate probabilities with knowledge base facts. The state of the art to store and process such data is founded on probabilistic database systems, which are widely and successfully employed. Beyond all the success stories, however, such systems still lack the fundamental machinery to convey some of the valuable knowledge hidden in them to the end user, which limits their potential applications in practice. In particular, in their classical form, such systems are typically based on strong, unrealistic limitations, such as the closed-world assumption, the closed-domain assumption, the tuple-independence assumption, and the lack of commonsense knowledge. These limitations do not only lead to unwanted consequences, but also put such systems on weak footing in important tasks, querying answering being a very central one. In this thesis, we enhance probabilistic data and knowledge bases with more realistic data models, thereby allowing for better means for querying them. Building on the long endeavor of unifying logic and probability, we develop different rigorous semantics for probabilistic data and knowledge bases, analyze their computational properties and identify sources of (in)tractability and design practical scalable query answering algorithms whenever possible. To achieve this, the current work brings together some recent paradigms from logics, probabilistic inference, and database theory.

The advent of Massive Open Online Courses (MOOCs) allows massive volume of registrants to enrol in these MOOCs. This research aims to offer MOOCs registrants with automatic content related feedback to fulfil their cognitive needs. A framework is proposed which consists of three modules which are the subject ontology learning module, the short text classification module, and the question answering module. Unlike previous research, to identify relevant concepts for ontology learning a regular expression parser approach is used. Also, the relevant concepts are extracted from unstructured documents. To build the concept hierarchy, a frequent pattern mining approach is used which is guided by a heuristic function to ensure that sibling concepts are at the same level in the hierarchy. As this process does not require specific lexical or syntactic information, it can be applied to any subject. To validate the approach, the resulting ontology is used in a question-answering system which analyses students' content-related questions and generates answers for them. Textbook end of chapter questions/answers are used to validate the question-answering system. The resulting ontology is compared vs. the use of Text2Onto for the question-answering system, and it achieved favourable results. Finally, different indexing approaches based on a subject's ontology are investigated when classifying short text in MOOCs forum discussion data; the investigated indexing approaches are: unigram-based, concept-based and hierarchical concept indexing. The experimental results show that the ontology-based feature indexing approaches outperform the unigram-based indexing approach. Experiments are done in binary classification and multiple labels classification settings . The results are consistent and show that hierarchical concept indexing outperforms both concept-based and unigram-based indexing. The BAGGING and random forests classifiers achieved the best result among the tested classifiers.

Computing with Words based Question Answering (CWQA) system provides a foundation to develop futuristic search engines where more of reasoning and less of pattern matching and statistical methods are used for information retrieval. In order to perform successful reasoning, these systems should analyze the semantic of the query and the related information in the Knowledge Base. The concept of Computing with Words (CW) which is a kind of perception based reasoning where manipulation of perceptions using fuzzy set theory and fuzzy logic play a key role in recognition, decision and execution processes can be utilized for this purpose. Two concepts that were introduced by Computing with Words are the Generalized Constraint Language (GCL) and the Generalized Theory of Uncertainty (GTU) . In GCL propositions, i.e. perceptions in natural language, are denoted using generalized constraints. The Generalized Theory of Uncertainty (GTU) uses GCL to express proposition drawn from natural language as a generalized constraint. The GCL plays a fundamental role in GTU by serving as a precisiation language for propositions, commands and questions in natural language. In GTU, deduction rules are used to propagate generalized constraints to accomplish reasoning under uncertainty. In the previous work a CW-based QA-system methodology was introduced which uses a knowledge tree data structure, called as a Constraint Propagation Tree (CPT) that utilizes the concepts briefed above. The realization of Constraint Propagation Tree, the first phase, and partial implementation of constraint propagation and node combination, the second phase, is the main goal of this work.

abstract: Question Answering has been under active research for decades, but it has recently taken the spotlight following IBM Watson's success in Jeopardy! and digital assistants such as Apple's Siri, Google Now, and Microsoft Cortana through every smart-phone and browser. However, most of the research in Question Answering aims at factual questions rather than deep ones such as ``How'' and ``Why'' questions. In this dissertation, I suggest a different approach in tackling this problem. We believe that the answers of deep questions need to be formally defined before found. Because these answers must be defined based on something, it is better to be more structural in natural language text; I define Knowledge Description Graphs (KDGs), a graphical structure containing information about events, entities, and classes. We then propose formulations and algorithms to construct KDGs from a frame-based knowledge base, define the answers of various ``How'' and ``Why'' questions with respect to KDGs, and suggest how to obtain the answers from KDGs using Answer Set Programming. Moreover, I discuss how to derive missing information in constructing KDGs when the knowledge base is under-specified and how to answer many factual question types with respect to the knowledge base. After having the answers of various questions with respect to a knowledge base, I extend our research to use natural language text in specifying deep questions and knowledge base, generate natural language text from those specification. Toward these goals, I developed NL2KR, a system which helps in translating natural language to formal language. I show NL2KR's use in translating ``How'' and ``Why'' questions, and generating simple natural language sentences from natural language KDG specification. Finally, I discuss applications of the components I developed in Natural Language Understanding. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2015

Made available in DSpace on 2016-12-23T14:33:48Z (GMT). No. of bitstreams: 1 Marta Talitha Carvalho Freire De Amorim.pdf: 1718108 bytes, checksum: 60eb34219545d0ffacecb5e5e80f2ea7 (MD5) Previous issue date: 2012-08-31 / When people want to learn a concept, the most common way is to use a search engine like: Google, Yahoo, Bing, among others. A natural language query is submitted to a search tool and which returns a lot of pages related to the concept studied. Usually the returned pages are listed and organized mainly based on the combination of keywords instead of using the interpretation and relevance of the terms found. The user must have read a lot of pages and selects the most appropriate to his needs. This kind of behavior takes time and focus on user-learner is dispersed to his goal. The use of intelligent systems that support the clarification of doubt has intent to solve this problem, presenting the most accurate answers to questions or sentences in natural language. Examples clarification of doubt systems are: question-answer system, help-desk intelligent among others. This work uses an architectural approach to a question answering system based on three steps: question analysis, selection and extraction of the answer and answer generation. One of the merits of this architecture is to use techniques that complement each other, such as ontologies, information retrieval techniques and a knowledge base written in AIML language to extract the answer quickly. The focus of this work is to answer questions WH-question (What, Who, When, Where, What, Who) of the English language / Quando as pessoas querem aprender algum conceito, a forma mais comum é usar uma ferramenta de pesquisa, como: Google, Yahoo, Bing, dentre outros. Uma consulta em linguagem natural é submetida para uma ferramenta e a pesquisa retorna uma grande quantidade de páginas relacionadas ao conceito pesquisado. Geralmente as páginas retornadas são listadas e organizadas principalmente baseando-se na combinação de palavras chaves ao invés de utilizar a interpretação e a relevância dos termos consultados. O usuário terá que ler uma grande quantidade de páginas e selecionar a mais apropriada a sua necessidade. Esse tipo de comportamento consome tempo e o foco do usuário-aprendiz é disperso do seu objetivo. A utilização de um sistema inteligente que apoie o esclarecimento de dúvidas pretende resolver esse problema, apresentando as respostas mais precisas ou frases para as perguntas em linguagem natural. Exemplos de sistemas de esclarecimento de dúvidas são: sistema de pergunta-resposta, help-desk inteligentes, entre outros. Este trabalho utiliza uma abordagem arquitetônica para um sistema de pergunta-resposta baseado em três passos: análise da pergunta, seleção e extração da resposta e geração da resposta. Um dos méritos dessa arquitetura é utilizar técnicas que se complementam, tais como: ontologias, técnicas de recuperação de informação e uma base de conhecimento escrita em linguagem AIML para extrair a resposta de forma rápida. O foco deste trabalho é responder perguntas WH-question (O que, Quem, Quando, Onde, Quais, Quem) da língua inglesa