Abstract. Definition extraction can be useful for the creation of glossaries and in question answering systems. It is a tedious task to extract such sentences manually, and thus an automatic system is desirable. In this work we review various attempts at rule-based approaches reported in the literature and discuss their results. We also propose a novel experiment involving the use of genetic programming and genetic algorithms, aimed at assisting the discovery of grammar rules which can be used for the task of definition extraction. 1

This paper describes a character-based system called “Emotion Sensitive News Agent ” (ESNA). ESNA is been

In this paper, we use a Social Network Analysis method and decision tree analysis to study the distribution and relationship of Noun Phrases in documents and their corresponding abstracts. Initial results have shown significant improvement in extraction based text summarization by applying systematic predictions of the Noun Phrases that appear in both the documents and in their corresponding abstracts.

Organizing Web search results into clusters facilitates users' quick browsing through search results. Traditional clustering techniques are inadequate since they don't generate clusters with highly readable names. In this paper, we reformalize the clustering problem as a salient phrase ranking problem. Given a query and the ranked list of documents (typically a list of titles and snippets) returned by a certain Web search engine, our method first extracts and ranks salient phrases as candidate cluster names, based on a regression model learned from human labeled training data. The documents are assigned to relevant salient phrases to form candidate clusters, and the final clusters are generated by merging these candidate clusters. Experimental results verify our method's feasibility and effectiveness.

Definition extraction can be useful for the creation of glossaries and in question answering systems. It is a tedious task to extract such sentences manually, and thus an automatic system is desirable. In this work we review various attempts at rule-based approaches reported in the literature and discuss their results. We also propose a novel experiment involving the use of genetic programming and genetic algorithms, aimed at assisting the discovery of grammar rules which can be used for the task of definition extraction. 1

The identification of definitions from natural language texts is useful in learning environments, for glossary creation and question answering systems. It is a tedious task to extract such definitions manually, and several techniques have been proposed for automatic definition identification in these domains, including rule-based and statistical methods. These techniques usually rely on linguistic expertise to identify grammatical and word patterns which characterize definitions. In this paper, we look at the use of machine learning techniques, in particular genetic algorithms, to enable the automatic extraction of definitions. Genetic algorithms are used to determine the relative importance of a set of linguistic features which can be present or absent in definitional sentences as a set of numerical weights. These weights provide an importance measure to the set of features. In this work we report on the results of various experiments carried out and evaluate them on an eLearning corpus. We also propose a way forward for discovering such features automatically through genetic programming and suggest how these two techniques can be used together for definition extraction.

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Abstract — The emergence of mobile computing is inevitably followed by mobile advertising: advertising that target mobile device, such as feature phones, smartphones and tablets. However, the majority of mobile advertising is still relying on the traditional approach: to send an advertisement to as many people as possible, in hope that some of them will be interested in the advertisement and in turn buying the promoted product or service. The problem with this method is that each people have their own preferences, so that kind of strategy won’t get an optimum result. Furthermore, with traditional strategy there is very low chance of people getting the right advertisement in the right place. This research proposed a new advertisement system that combined a location based service with user profiling system. In this study, the advertisement system is integrated to an online traffic map mobile application, which also has an RSS feed reader feature. The system will learn user interest by using web mining to analyse browsing history that is taken from the RSS reader. When the user tries to generate a route in the map, the system will automatically fetch advertisements that are located along the route, which suited user interest. With this proposed system, we have successfully created a mobile advertisement that is highly efficient and effective. Moreover, it has gained positive feedback from user by being accurate, beneficial, and non-obtrusive. Better reception of advertisement from user will lead to the increasing rate of advertisement success.

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