Informatics Publishing Limited

Adekunle Isiaka Obasa ¹, Naomie Salim ², Atif Khan ²

Affiliations
1 Department of Computer Science, College of Science and Technology, Kaduna Polytechnic, P.M.B 2021, Kaduna, NG
2 Faculty of Computing, Universiti Teknologi Malaysia, 81310, Skudai, Johor, MY

Abstract

Background/Objective:A web forum is a problem-solving online community.Web forum research activitieshave been focused on answer mining with the assumption that the starting post is a question post. This paper proposes methods for mining standard web forum questions. Methods/Statistical Analysis:Popular methods for web forum question post detection are question mark, question words, higher n-grams and sequential pattern mining. These methods have problem of low detection rate and implementation complexity. Implemented in this paper is hybridization of simple bag-of-words model with web forum metadata, simple rule of question mark and question words. Dimensional reduction was performed using chi-square and wrapper techniques. Findings:The quality of web forum question posts varies from excellent to mediocre or even spam. Detecting good question posts is non-trivial. It requires utilization of salient features. Combination of simple rule of question mark and question words with forum metadata performed better than each of the two.Integration of bag-of-words model with simple rule of question marks, question words and forum metadata enhances question post detection. Dimensionality reduction using chi-square were found to perform better than other popular filters like info gain, gain ratio and symmetric uncertain. Applications/Improvements: Three publicly available datasets of varying technical degrees were used for the experiments.The experimental results revealed that an enhanced bag-of-words model can perform better than complex techniques that implement higher N-gram with part-of-speech tagging.

Keywords

Bag-of-words, Forum Metadata, Web Forum,Question Detection, Dimensionality Reduction, Web Forum Question

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Atif Khan ¹, Naomie Salim ¹, Adekunle Isiaka obasa ¹

Affiliations
1 Faculty of Computing, Universiti Teknologi Malaysia, 81310, Skudai, Johor, MY

Abstract

Background/Objective: Multi-document summarization produces a concise summary from several online topically related documents. A major challenge in this domain is usually the information overlap in documents emanating from various sources. This paper introduces an optimized semantic technique for multi-document abstractive summarization. Methods/Statistical Analysis: Linguistic and semantic approaches are usually employed for abstractive summarization of multiple documents. Linguistic approaches lack semantic representation of source text while semantic approaches mostly rely on human experts to construct domain ontology and rules; which require immense time and effort. The technique in this paper utilizes the benefits of semantic role labeling, clustering and Particle Swarm Optimization (PSO) to rank predicate argument structures (semantic representation) in each cluster using optimized features. Findings: The summary quality is susceptible to the text features i.e., different features have varied importance towards summary generation. Therefore, optimal features weights obtained using PSO integrated in the semantic technique to rank semantic representation improved summarization results. The performance of the technique is evaluated against the benchmark summarization systems using pyramid evaluation measures (mean coverage score, precision and F-measure). A Paired- Samples T-test is carried out to validate the summarization results. Applications/Improvements: Experiment of this research is performed with DUC-2002, a benchmark data set for text summarization. Experimental results confirm that the proposed technique yields better results than other comparison summarization models in terms of mean coverage score and average F-measure.

Keywords

Language Generation, Multi-Document Abstractive Summarization, Particle Swarm Optimization, Semantic Similarity Measure, Semantic Role Labeling (SRL).

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Senosy Arrish ¹, Fadhil Noer Afif ¹, Ahmadu Maidorawa ¹, Naomie Salim ¹

Affiliations
1 Universiti Teknologi Malaysia, Skudai, MY

Abstract

Plagiarism detection is well known phenomenon in the academic arena. Copying other people is considered as serious offence that needs to be checked. There are many plagiarism detection systems such as turn-it-in that has been developed to provide this checks. Most, if not all, discard the figures and charts before checking for plagiarism. Discarding the figures and charts results in look holes that people can take advantage. That means people can plagiarized figures and charts easily without the current plagiarism systems detecting it. There are very few papers which talks about flowcharts plagiarism detection. Therefore, there is a need to develop a system that will detect plagiarism in figures and charts. This paper presents a method for detecting flow chart figure plagiarism based on shape-based image processing and multimedia retrieval. The method managed to retrieve flowcharts with ranked similarity according to different matching sets.

Keywords

Flowchart, Multimedia Retrieval, Figures Similarity, Image Comparison, Figure Retrieval.

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Yazan Alaya AL-Khassawneh ¹, Naomie Salim ¹, Mutasem Jarrah ¹

Affiliations
1 Faculty of Computing, Universiti Teknologi Malaysia, 81310, Skudai, Johor, MY

Abstract

Objective: Extractive Summarization, extracts the most applicable sentences from the main document, while keeping the most vital information in the document. The Graph-based techniques have become very popular for text summarisation. This paper introduces a hybrid graph based technique for single-document extractive summarization. Methods/Statistical Analysis: Prior research that utilised the graph-based approach for extractive summarisation deployed one function for computing the necessary summary. Nonetheless, in our work, we have recommended an innovative hybrid similarity function (H), for estimation purpose. This function hybridises four distinct similarity measures: cosine similarity (sim1), Jaccard similarity (sim2), word alignmentbased similarity (sim3) and the window-based similarity measure (sim4). The method uses a trainable summarizer, which takes into account several features. The effect of these features on the summarization task is investigated. Findings: By combining, the traditional similarity measures (Cosine and Jaccard) with dynamic programming approaches (word alignment-based and the window-based) for calculating the similarity between two sentences, more common information were extracted and helped to find the best sentences to be extracted in the final summary. The proposed method was evaluated using ROUGE measures on the dataset DUC2002. The experimental results showed that specific combinations of features could give higher efficiency. It also showed that some features have more effect than others on the summary creation. Applications/Improvements: The performance of this new method has been tested using the DUC 2002 data set. The effectiveness of this technique is measured using the ROUGE score, and the results are promising when compared with some existing techniques.

Keywords

Extractive Summarization, Feature Extraction, Graph-Based Summarization, Hybrid Similarity, Sentence Similarity, Triangle Counting

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