Rabiah Abdul Kadir

Sentiment classification of customer reviews based on fuzzy logic

Nowadays, e-commerce is growing fast, so product reviews have grown rapidly on the web. The large number of reviews makes it difficult for manufacturers or businesses to automatically classify them into different semantic orientations (positive, negative, and neutral). Most existing method utilize a list of opinion words for sentiment classification. whereas, this paper propose a fuzzy logic model to perform semantic classifications of customers review into the following sub-classes: very weak, weak, moderate, very strong and strong by combinations adjective, adverb and verb to increase holistic the accuracy of lexicon approach. Fuzzy logic, unlike statistical data mining techniques, not only allows using non-numerical values also introduces the notion of linguistic variables. Using linguistic terms and variables will result in a more human oriented querying process.

Quranic-based concepts: Verse relations extraction using Manchester OWL syntax

In recent years, there is global demand for Islamic knowledge by both Muslims and non-Muslims. This has brought about number of automated applications that ease the retrieval of knowledge from the holy books. However current retrieval methods lack semantic information they are mostly base on keywords matching approach. In this paper we have proposed a Model that will make use of semantic Web technologies (ontology) to model Quran domain knowledge. The system will enhance Quran knowledge by enabling queries in natural language.

Query translation using concepts similarity based on Quran ontology for cross-language information retrieval.

In Cross-Language Information Retrieval (CLIR) process, the translation effects have a direct impact o n the accuracy of follow-up retrieval results. In diction ary-based approach, we are dealing with the words t hat have more than one meaning which can decrease the retrieval performance if the query translation retur n an incorrect translations. These issues need to be ove rcome using efficient technique. In this study we p roposed a Cross-Language Information Retrieval (CLIR) method based on domain ontology using Quran concepts for disambiguating translation of the query and to improve the dictionary-based query translation. For experimentation, we use Quran ontology written in E nglish and Malay languages as a bilingual parallelcorpora and Quran concepts as a resource for cross- language query translation along with dictionary-ba sed translation. For evaluation, we measure the perform ance of three IR systems. IR 1 is natural language query IR, IR 2 is natural language query CLIR based on dictionary (as a Baseline) and IR 3 is the retrieval of this research proposed method using Mean Average Precision (MAP) and average precision at 11 points of recall. The experimental result shows that our prop osed method brings significant improvement in retri eval accuracy for English document collections, but defi cient for Malay document collections. The proposed CLIR method can obtain query expansion effect and improve retrieval performance in certain language.

Ontology semantic approach to extraction of knowledge from Holy Quran

With the continued demand for Islamic knowledge, which is mainly based on the Quran as a source of knowledge and wisdom, systems that facilitate an easy search of the content of the Quran remain a considerable challenge. Although in recent years there have been tools for Quran search, most of these tools are based on keyword search, meaning that the user needs to know the correct keywords before being able to retrieve the content of Quran. In this paper, we propose a system that supports the end user in querying and exploring the Quran ontology. The system comprises user query reformulation against the Quran ontology stored and annotated in the knowledge base. The Quran ontology c ons i s t s of n oun concepts ident i f i ed in a l - Quran, and the relationship that exists between these concepts. The user writes a query in the natural language and the proposed system reformulates the query to match the content found in the knowledge base in order to retrieve the relevant answer. The answer is represented by the Quranic verse related to the user query.

Query translation architecture for Malay-English Cross-Language information retrieval system

This paper discusses research on query translation events in Malay-English Cross-Language Information Retrieval (CLIR) system. We assume that by improving query translation accuracy, we can improve the information retrieval performance. The dictionary-based CLIR system facing three main problems: translation ambiguity; compound and phrase handling and proper names translation. The use of natural language processing (NLP) techniques, such as stemming, Part-of-Speech (POS) tagging is useful in query translation process. Hence, n-gram matching technique has successfully applied to information retrieval (IR) system for phrases and proper names translation. The proposed query translation architecture consist of stemming, Part-of-Speech (POS) tagging and n-gram matching techniques is useful in CLIR system as well as search engine application.

Automatic Lexicon Generator for Logic Based Question Answering System

Computer science is in the more challenging era due to the digital growth and demand that we are facing today. A typical IR system will not go far enough as it uses keyword in order to retrieve the desired information. On the other hand, natural language question answering which is based on logic retrieval have proven to perform better than other methods. The most important component of question answering system is the lexicon. Researchers have identified this as the backbone of any NLP system. In this paper we present our approach on how an automatic lexicon generator has been integrated with question answering system which uses logical inference model. This paper also shows on how the automatic lexicon generator has helped in creating knowledge representation to be used by logic based question answering system. Besides that, the lexicon generated managed to reduce significant amount of time and manpower. It has also helped to make the question answering system to be more robust.

Automatic lexicon generator

Over the past decades, computer revolution has opened up many possibilities for new field of investigation. With greater accessibility to information and lowering cost of powerful computers, this has spawned new efforts towards understanding complex tasks. Lexicon in particular has long been recognized as interesting and challenging because of its complexness. It is the knowledge of individual words in the language that has been perceived as central component for all types of natural language processing system. In this paper we present an algorithm to create an automatic lexicon generator in order to generate lexicon from an input document by making use of Apple Pie Parser. The lexicon generated managed to reduce significant amount of time and manpower drastically. Psycholinguists as well as computational linguists can benefit from this automatic lexicon construction.

Building Knowledge Representation for Multiple Documents Using Semantic Skolem Indexing

The rapid growth of digital data and users’ information needs have made the demands for automatic indexing to become more important than before. Indexing based on keyword has proven to be unsuccessful to cater for the current needs. Thus, this paper presents a new approach in creating semantic skolem indexing for multiple documents that automatically index all the documents into single knowledge representation. The skolem indexing matrix will then be incorporated in question answering system to retrieve the answer for users query.

Extracting lexical and phrasal paraphrases: a review of the literature

Recent advances in natural language processing have increased the popularity of paraphrase extraction. Most of the attention, however, has been focused on the extraction methods only without taking the resource factor into the consideration. Unknowingly, there is a strong relationship between them and the resource factor also plays an equally important role in paraphrase extraction. In addition, almost all of the previous studies have been focused on corpus-based methods that extract paraphrases from corpora based solely on syntactic similarity. Despite the popularity of corpus-based methods, a considerable amount of research has consistently shown that these methods are vulnerable to several types of erroneous paraphrases. For these reasons, it is necessary to evaluate whether the trend is moving in a positive direction. This paper reviews the major research on paraphrase extraction methods in detail. It begins by exploring the definition of paraphrase from different perspectives to provide a better understanding of the concept of paraphrase extraction. It then studies the characteristics and potential uses of different types of paraphrase resources. After that, it divides paraphrase extraction methods into four main categories: heuristic-based, knowledge-based, corpus-based and hybrid-based and summarizes their strengths and weaknesses. This paper concludes with some potential open research issues for future directions.

A review on the cross-lingual information retrieval

Information retrieval involves finding some required information in a collection of information or in database. The collection not necessarily be in one language only as information does not limited to language. The simplest way to search for the information is to look at every item in the collection and when the need to translate the languages being used arises, this is where the techniques and methods that were being developed for the cross-lingual retrieval system will take place. This article reviews some recent researches focusing on topics in cross-lingual information retrieval and their role in current research directions in the wide area of information retrieval.